Engineering Applications of Artificial Intelligence 9783031502996, 9783031503009

Table of contents :
Preface
Introduction
Contents
Applications of Artificial Intelligence in Research
Artificial Intelligence: An Overview
1 Introduction
2 Historical Background of Artificial Intelligence
3 Literature Review
3.1 A.I. Structure
3.2 A.I. Environment
3.3 A.I. System
3.4 Natural Language Processing
3.5 Artificial Subfields
3.6 A.I. and Human
3.7 A.I. and Society
3.8 A.I. and Firms
3.9 A.I. and Management Process
4 Conclusion
5 The Implication of AI in Future Management Process
References
Application of Artificial Intelligence to Control a Nonlinear SIR Model
1 Introduction
2 A Nonlinear Control System for an SIR Epidemic Model Incorporating Vaccination
3 Optimal Control Using PMP
3.1 Optimality System
3.2 Numerical Simulation and Discussions
4 Optimal Control by a Trained ANN
4.1 Building and Training the ANN
4.2 Numerical Results and Discussions
5 Conclusion
References
Computer Vision with Deep Learning for Human Activity Recognition: Features Representation
1 Introduction
2 Review of the Literature
2.1 Convolutional Neural Network (CNN)
3 Results from Various Studied Approaches for Modeling Human Data
4 Conclusion and Outlook
References
Applications of Artificial Intelligence in Education
Streamlining Student Support: Enhancing Administrative Assistance and Interaction Through a Chatbot Solution
1 Introduction
2 Related Works
3 Limitation
4 Proposed Approach
5 Conclusions and Future Work
References
Towards a System that Predicts the Category of Educational and Vocational Guidance Questions, Utilizing Bidirectional Encoder Representations of Transformers (BERT)
1 Introduction
2 Review of Corresponding Research
2.1 The RIASEC Assessment
2.2 Deep Artificial Neural Networks
2.3 Pre-training Model
3 The Proposed Model
4 Exploring Experimental Findings and Results
4.1 The Dataset and Its Characteristics
4.2 The Steps of the Experiment
4.3 The Results
5 Conclusion
References
Artificial Intelligence in the Context of Digital Learning Environments (DLEs): Towards Adaptive Learning
1 Introduction
2 Review of Literature
2.1 Adaptive Learning: Personalized Education Through AI
2.2 Ethical Considerations in AI-Enhanced Learning
2.3 Student Perspectives on AI-Powered Adaptive Learning
3 Methodology
3.1 Data Collection
3.2 Questionnaire Design
3.3 Data Collection Procedure
3.4 Data Analysis
4 Discussion
5 Conclusion
References
A Methodology for Evaluating and Reporting the Integration of Artificial Intelligence for Sustainability in Higher Education: New Insights and Opportunities
1 Introduction
2 Assessment Tools for Sustainability in Higher Education
3 Methodology
3.1 Identify STARS Indicators
3.2 AI for ESD Areas
3.3 Mapping Process
3.4 Matrix Presentation
4 Result
5 Discussion
6 Conclusion
References
Blockchain Technology and Artificial Intelligence for Smart Education: State of Art, Challenges and Solutions
1 Introduction
2 Blockchain in Education
3 Literature Review
3.1 Methodology
3.2 Data Analysis
3.3 Discussion
4 The Proposed Solution
5 Conclusion
References
Artifical Intelligence in Nurse Education
1 Introduction
2 AI & Nurse Education
3 Roles of AI in High School and University
4 Examples of AI Tools Using in High School and University
5 AI & Curriculum of Nursing Education
5.1 Situation of Using AI in Curriculum
6 General Framework for Incorporating AI into a Nursing Curriculum
6.1 An Example of Curriculum
7 Skills for Nurses in the Education Process Related to AI
8 Preconditions for Efficient Using of AI in Nurse Education
9 Ensuring the Preconditions
10 Advantages and Disadvantages of Using AI in Nursing Education
11 Risks of Teaching About AI in Nursing Education
12 Overcoming the Risks
13 The Main Topics of Literature About AI in Nursing Education
13.1 Review of Literature
14 Sources of Information About AI in Nurse Education
15 Conclusion
References
Applications of Artificial Intelligence in Health
Artificial Intelligence Applications in Healthcare
1 Introduction
2 A Review of the Literature on AL in Healthcare
3 AI Applications in Healthcare
4 AL Challenges in Healthcare
5 Conclusions and Future Trends
6 Recommendations
References
The Use of Feature Engineering and Hyperparameter Tuning for Machine Learning Accuracy Optimization: A Case Study on Heart Disease Prediction
1 Introduction
2 Methodology
3 Process Detailing
4 Results, Discussion, and Evaluation
5 Conclusion and Recommendation
References
Plant Health—Detecting Leaf Diseases: A Systematic Review of the Literature
1 Introduction
2 Ease of Use
2.1 Search Strategy
2.2 Study Choice
3 Results and Analysis
4 Comparative Study
5 Critical Study
6 Proposed Approach
7 Conclusion and Future Work
References
Exploring the Intersection of Machine Learning and Causality in Advanced Diabetes Management: New Insight and Opportunities
1 Introduction
2 Association and Prediction
3 Causal Inference
3.1 Prediction Versus Causality
3.2 Association Versus Causality
3.3 Validity and Reliability of Real-World Evidence, and Causal Inference
4 Advancement of Machine Learning Methods in Diabetology
4.1 Biomarker Identification and Prediction of DM
4.2 Diabetic Complications
4.3 Drugs and Therapies
5 Conclusion
References
For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used
1 Introduction
2 Related Work
3 Deep Learning Techniques for Classification and Segmentation of Histopathological Images
4 Description of the Dataset
4.1 Pre-Processing
4.2 Segmentation
4.3 Classification
5 Proposed Approach
6 Results
7 Conclusion
References
Applications of Artificial Intelligence in Recruitment and in Marketing
Metaverse for Job Search: Towards an AI-Based Virtual Recruiter in the Metaverse Era: A Systematic Literature Review
1 Introduction
2 Related Work
3 Critical Study
4 Proposed Approach
5 Limitation
6 Conclusion and Future Work
References
Metaverse for the Recruitment Process: Towards an Intelligent Virtual Recruiter
1 Introduction
2 Review of Literature
2.1 Meta-Recruitment Process
3 Methodology
3.1 Result
4 Discussion
5 Conclusion
References
Enhancing Immersive Virtual Shopping Experiences in the Retail Metaverse Through Visual Analytics, Cognitive Artificial Intelligence Techniques, Blockchain-Based Digital Assets, and Immersive Simulations: A Systematic Literature Review
1 Introduction
2 Methodology
3 The Metaverse: A New Frontier for Retails
4 Role of Visual Analytics in Comprehension of Customer Preferences
5 How AI Cognitively Enhances Retail Business in the Immersive Virtual Shopping
6 Virtual Reality and Augmented Reality Technologies Effects on the Retails Sector’s Transformation
7 Blochchain Necessary Tool for Secure Metaverse Transactions
8 Challenges of Use Blockchain
9 Challenges
10 Synthesis of the Major Results of Research
11 Limitation
12 Conclusion
References
Enhancing Customer Engagement in Loyalty Programs Through AI-Powered Market Basket Prediction Using Machine Learning Algorithms
1 Introduction
2 Related Work
3 Proposed Approach
4 Results and Discussion
4.1 Dataset
4.2 Length Feature Engineering: Identifying Key Factors for Enhancing Market Basket Prediction Accuracy
4.3 Feature Selection
5 Feature Scaling
6 Models Explored
6.1 Logistic Regression
6.2 Support Vector Machines (SVMs)
6.3 Decision Tree
6.4 Random Forest
6.5 K-nearest Neighbors Algorithm (k-NN)
7 Creating Predictive Models
7.1 Evaluating Model Performance
7.2 Evaluation of the Predictive Model
8 Discussion
9 Conclusion
References
Applications of Artificial Intelligence in Industry and in Agriculture
Application of Artificial Intelligence in the Oil and Gas Industry
1 Introduction
2 Background
2.1 Overview of the Oil and Gas Sector
2.2 Oil and Gas Sector and Its Complexity
2.3 Key Challenges
2.4 Addressing the Challenges of the Industry with AI Solutions
2.5 Overview of Artificial Intelligence
2.6 Artificial Intelligence Techniques
3 AI Applications in Upstream Operations
4 AI Applications in Midstream Operations
4.1 Pipeline Integrity Management Using AI Algorithms
5 AI Applications in Downstream Operations
5.1 Predictive Maintenance for Refining Processes
6 Safety and Environmental Applications of AI
6.1 AI-Powered Risk Assessment and Safety Monitoring
6.2 Environmental Impact Analysis and Mitigation Using AI
7 Challenges and Limitations of AI in the Oil and Gas Industry
7.1 Data Quality and Availability Challenges
7.2 Regulatory and Security Concerns
8 Future Trends and Outlook
8.1 Potential Impact of AI on the Oil and Gas Future
8.2 Opportunities for Further Research and Development
References
Duplicated Tasks Elimination for Cloud Data Center Using Modified Grey Wolf Optimization Algorithm for Energy Minimization
1 Introduction
2 Related Work
3 Grey Wolf Optimization Algorithm
3.1 Duplicated Tasks Assignment Elimination
3.2 Proposed Modified Grey Wolf Optimizer Algorithm
4 Evaluation Parameter
4.1 Experimental Setup
5 Result and Discussion
6 Conclusion
References
Enhancing Deep Learning-Based Semantic Segmentation Approaches for Smart Agriculture
1 Introduction
2 Problematic
3 Objectives
4 Technologies Used
5 Related Work
6 Results
7 Conclusion
References
Applications of Artificial Intelligence in Management, in Supply Chain, and in Finance
Role of Artificial Intelligence in Sustainable Finance
1 Introduction
2 Literature Review
2.1 Sustainable Finance
2.2 Artificial Intelligence
2.3 Sustainable Finance and Financial Performance
3 Research Method and Objectives
4 Result and Discussion
5 Findings and Suggestions
6 Implication of the Study
7 Conclusion
References
Optimizing Processes in Digital Supply Chain Management Through Artificial Intelligence: A Systematic Literature Review
1 Introduction
1.1 Rationale
2 Objectives
3 Method
3.1 Search Strategy
3.2 Study Selection
4 Results and Discussion
4.1 What’s the Role of Digitalization in Supply Chain Management?
4.2 What Are the Dimensions of Digital Technologies?
4.3 What Kinds of Limitations Exist in Research on Digital Technologies in Supply Chain Management?
5 Conclusion
References
Enhancing Hotel Services Through Sentiment Analysis
1 Introduction
2 Related Works
3 Research Methodology
3.1 Proposed Workflow
3.2 BERT
3.3 Comparison Between Traditional Machine Learning Techniques and BERT
4 Experimentation
4.1 Datasets Used
4.2 Performance Measure
4.3 Results
5 Conclusion
References

Citation preview

Synthesis Lectures on Engineering, Science, and Technology

Aziza Chakir · Johanes Fernandes Andry · Arif Ullah · Rohit Bansal · Mohamed Ghazouani   Editors

Engineering Applications of Artificial Intelligence

Synthesis Lectures on Engineering, Science, and Technology

The focus of this series is general topics, and applications about, and for, engineers and scientists on a wide array of applications, methods and advances. Most titles cover subjects such as professional development, education, and study skills, as well as basic introductory undergraduate material and other topics appropriate for a broader and less technical audience.

Aziza Chakir · Johanes Fernandes Andry · Arif Ullah · Rohit Bansal · Mohamed Ghazouani Editors

Engineering Applications of Artificial Intelligence

Editors Aziza Chakir Faculty of Law, Economic and Social Sciences (Ain Chock) Hassan II University Casablanca, Morocco Arif Ullah Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad, Pakistan

Johanes Fernandes Andry Information Systems Universitas Bunda Mulia Jakarta, Indonesia Rohit Bansal Department of Management Studies Vaish College of Engineering Rohtak, India

Mohamed Ghazouani Department of Mathematics and Computer Science University of Hassan II Casablanca, Morocco

ISSN 2690-0300 ISSN 2690-0327 (electronic) Synthesis Lectures on Engineering, Science, and Technology ISBN 978-3-031-50299-6 ISBN 978-3-031-50300-9 (eBook) https://doi.org/10.1007/978-3-031-50300-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

In today’s fast-paced world, the proliferation of technology has led to an exponential growth in the number of applications across diverse domains. This surge has brought with it the need for innovative and intelligent solutions to cater to the ever-evolving demands of these fields. At the forefront of this technological revolution is the field of Computer Science, where Artificial Intelligence (AI) has emerged as a game-changer. This book emphasizes the paramount importance of AI in Computer Science, shedding light on how it has redefined the way we approach and solve complex problems. From machine learning algorithms to natural language processing, AI has become the cornerstone of modern computer science, making tasks more efficient, decisions more informed, and solutions more ingenious. What sets this work apart is its unparalleled collection of original theories and groundbreaking research findings. The book features a series of chapters dedicated to exploring the multifaceted use of AI in various domains, including health care, industry, finance, agriculture, management, and more. Each chapter provides a deep dive into the specific applications of AI, highlighting the transformative impact it has had on each sector. In the realm of health care, AI has revolutionized diagnostics, treatment planning, and patient care. In industry, it has optimized manufacturing processes, predictive maintenance, and supply chain management. The financial sector has witnessed AI-driven algorithms for risk assessment and investment strategies. In agriculture, AI-driven precision farming has enhanced crop yields and resource utilization. Management practices have evolved with AI-powered decision support systems, making organizations more agile and competitive. This work stands as a testament to the limitless possibilities of AI in our modern world. It is not merely a book; it is a compendium of insights, knowledge, and inspiration for those seeking to harness the power of AI in their respective domains. Join us on this

v

vi

Preface

enlightening journey into the realm of artificial intelligence and its transformative impact on the way we live, work, and innovate . Casablanca, Morocco Jakarta, Indonesia Islamabad, Pakistan Rohtak, India Casablanca, Morocco

Aziza Chakir Johanes Fernandes Andry Arif Ullah Rohit Bansal Mohamed Ghazouani

Introduction

In the ever-evolving landscape of technology, the fusion of engineering applications and artificial intelligence has ushered in a new era of innovation and problem-solving. The marriage of engineering prowess and intelligent systems has catalyzed a revolution, promising to tackle some of the most profound challenges in computer science. As we embark on this enlightening journey through the pages of this book, we delve into the profound impact of artificial intelligence in engineering, dissecting its role as a transformative force in overcoming complex computational problems. Artificial intelligence, often regarded as the pinnacle of human ingenuity, stands as a testament to our relentless pursuit of innovation. This multidisciplinary field harnesses the power of intelligent systems, machine learning, and data analytics to enable machines to emulate human cognition. Within the vast expanse of engineering, AI has emerged as an indispensable tool, enabling engineers to transcend the boundaries of traditional problem-solving and achieve feats that were once thought impossible. The synergy between engineering and AI is a dynamic one, manifesting in a multitude of applications that have altered the course of technological progress. From autonomous vehicles that navigate crowded streets with remarkable precision to medical imaging systems capable of early disease detection, the scope of AI’s influence is boundless. This book is an exploration of the symbiotic relationship between engineering and artificial intelligence, a testament to the extraordinary strides taken in developing intelligent systems to enhance the capabilities of engineering. In the pages that follow, we will traverse through the corridors of this transformative partnership, unveiling how AI-driven engineering solutions have impacted a spectrum of industries, from aerospace to health care, from finance to energy. We will uncover the methods and technologies that enable machines to learn, adapt, and excel at tasks that were once solely the purview of human intelligence. As we journey through the chapters ahead, we will gain a deeper understanding of the cutting-edge technologies, applications, and methodologies that underpin this remarkable merger. This book serves as a testament to the extraordinary achievements born from the

vii

viii

Introduction

union of engineering applications and artificial intelligence, offering readers an opportunity to grasp the intricacies of this exciting field and its boundless potential to reshape our world. Join us on this expedition into the heart of engineering and artificial intelligence, where the frontiers of technology are redrawn, and the possibilities are limited only by the depths of human imagination.

Contents

Applications of Artificial Intelligence in Research Artificial Intelligence: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Jaboob, Omar Durrah, and Aziza Chakir

3

Application of Artificial Intelligence to Control a Nonlinear SIR Model . . . . . . . Oussama Chayoukh and Omar Zakary

23

Computer Vision with Deep Learning for Human Activity Recognition: Features Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laila El Haddad, Mostafa Hanoune, and Abdelaziz Ettaoufik

41

Applications of Artificial Intelligence in Education Streamlining Student Support: Enhancing Administrative Assistance and Interaction Through a Chatbot Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ghazouani Mohamed, Fandi Fatima Zahra, Chafiq Nadia, Daif Abderrahmane, Ettarbaoui Badr, Aziza Chakir, and Azzouazi Mohamed Towards a System that Predicts the Category of Educational and Vocational Guidance Questions, Utilizing Bidirectional Encoder Representations of Transformers (BERT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Omar Zahour, El Habib Benlahmar, Ahmed Eddaoui, and Oumaima Hourane Artificial Intelligence in the Context of Digital Learning Environments (DLEs): Towards Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imane Elimadi, Nadia Chafiq, and Mohamed Ghazouani A Methodology for Evaluating and Reporting the Integration of Artificial Intelligence for Sustainability in Higher Education: New Insights and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yman Chemlal and Mohamed Azzouazi

69

81

95

113

ix

x

Contents

Blockchain Technology and Artificial Intelligence for Smart Education: State of Art, Challenges and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelaziz Ettaoufik, Amine Gharbaoui, and Abderrahim Tragha Artifical Intelligence in Nurse Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Velibor Boži´c

131 143

Applications of Artificial Intelligence in Health Artificial Intelligence Applications in Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . Omar Durrah, Fairouz M. Aldhmour, Lujain El-Maghraby, and Aziza Chakir

175

The Use of Feature Engineering and Hyperparameter Tuning for Machine Learning Accuracy Optimization: A Case Study on Heart Disease Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cevi Herdian, Sunu Widianto, Jusia Amanda Ginting, Yemima Monica Geasela, and Julius Sutrisno

193

Plant Health—Detecting Leaf Diseases: A Systematic Review of the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fandi Fatima Zahra, Ghazouani Mohamed, and Azouazi Mohamed

219

Exploring the Intersection of Machine Learning and Causality in Advanced Diabetes Management: New Insight and Opportunities . . . . . . . . . Sahar Echajei, Yman Chemlal, Hanane Ferjouchia, Mostafa Rachik, Nassim Essabah Haraj, and Asma Chadli For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arifullah, Aziza Chakir, Dorsaf Sebai, and Abdu Salam

237

263

Applications of Artificial Intelligence in Recruitment and in Marketing Metaverse for Job Search: Towards an AI-Based Virtual Recruiter in the Metaverse Era: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . Ghazouani Mohamed, Fandi Fatima Zahra, Chafiq Nadia, Elimadi Imane, Lakrad Hamza, Aziza Chakir, and Azzouazi Mohamed Metaverse for the Recruitment Process: Towards an Intelligent Virtual Recruiter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nadia Chafiq, Imane Elimadi, and Mohamed Ghazouani

277

287

Contents

Enhancing Immersive Virtual Shopping Experiences in the Retail Metaverse Through Visual Analytics, Cognitive Artificial Intelligence Techniques, Blockchain-Based Digital Assets, and Immersive Simulations: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ghazouani Mohamed, Fandi Fatima Zahra, Zaher Najwa, Ounacer Soumaya, Karim Yassine, Aziza Chakir, and Azzouazi Mohamed Enhancing Customer Engagement in Loyalty Programs Through AI-Powered Market Basket Prediction Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Meftah, Soumaya Ounacer, and Mohamed Azzouazi

xi

305

319

Applications of Artificial Intelligence in Industry and in Agriculture Application of Artificial Intelligence in the Oil and Gas Industry . . . . . . . . . . . . . Muhammad Hussain, Aeshah Alamri, Tieling Zhang, and Ishrat Jamil Duplicated Tasks Elimination for Cloud Data Center Using Modified Grey Wolf Optimization Algorithm for Energy Minimization . . . . . . . . . . . . . . . . . . . . . Arif Ullah, Aziza Chakir, Irshad Ahmed Abbasi, Muhammad Zubair Rehman, and Tanweer Alam Enhancing Deep Learning-Based Semantic Segmentation Approaches for Smart Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imade Abourabia, Soumaya Ounacer, Mohamed Yassine Ellghomari, and Mohamed Azzouazi

341

375

395

Applications of Artificial Intelligence in Management, in Supply Chain, and in Finance Role of Artificial Intelligence in Sustainable Finance . . . . . . . . . . . . . . . . . . . . . . . . Monika Rani and Ram Singh Optimizing Processes in Digital Supply Chain Management Through Artificial Intelligence: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . Zaher Najwa, Ghazouani Mohamed, Aziza Chakir, and Chafiq Nadia Enhancing Hotel Services Through Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . Soumaya Ounacer, Abderrahmane Daif, Mohamed El Ghazouani, and Mohamed Azzouazi

409

421 429

Applications of Artificial Intelligence in Research

Artificial Intelligence: An Overview Ali Jaboob, Omar Durrah, and Aziza Chakir

Abstract

Over the preceding decades, the gradual and incessant advancement and dissemination of artificial intelligence (A.I.) and automation have occasioned a noteworthy degree of motivation and profound alteration across various industries. Artificial Intelligence (A.I.), an interdisciplinary field combining computer science, mathematics, and cognitive psychology, has been rapidly burgeoning with many applications across various industries. The current chapter aims to provide an extensive overview of the theoretical foundations of artificial intelligence, "encompassing its definition, characteristics, and subfields, including machine learning, natural language processing, computer vision, and robotics". In addition, this chapter delves into diverse intelligence theories, examining how they inform A.I. research and development. Despite the promising potential, A.I. faces significant challenges and limitations, such as biases and ethical concerns, that necessitate prompt addressing. Thus, the chapter will cover the managerial challenges in organizations that may adopt A.I. in the future. This chapter, therefore, A. Jaboob GSB-University Kebangsaan Malaysia, Bangi, Malaysia e-mail: [email protected] O. Durrah (B) Management Department, College of Commerce and Business Administration, Dhofar University, Salalah, Oman e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_1

3

4

A. Jaboob et al.

underscores the paramount importance of artificial intelligence and its potential ramifications for society and organizations, underscoring the need for continuous research in the field of artificial intelligence. This chapter aims to provide a comprehensive understanding of the theoretical foundations of artificial intelligence and its potential implications for the future. Keywords

Artificial intelligence . A.I. theoretical foundations . A.I. subfields . Managerial challenges

1

Introduction

By 2030, it is expected that the artificial intelligence field will have added up to $15.7 trillion to the world economy, greatly altering it [60]. This interdisciplinary study topic focuses on the creation of intelligent systems capable of doing tasks that have traditionally required intelligence from humans [21, 42]. Machine learning, which involves inferring patterns from data, natural language processing, which includes speech and text recognition, expert systems, which involves inferring designs from pre-existing rules or knowledge; and computer vision, among other applications, are all used for this purpose, although studying what drives technological progress and innovation has been a top priority for many years [87, 94]. Since the initial investigations were disseminated during the 1960s, diverse frameworks aimed at expounding the impetus behind this phenomenon have emerged [14]. These models have evolved over time, and their progression can be broken down into five generations: the first, science push; the second, demand-pull; the third, the coupling (or chain-linked) model; the fourth, the integrated model; and the fifth, the systems model [68]. Although it is impossible to construct a linear hierarchy of these models, it is clear that nearby models affected one another and that models from several generations continue to benefit modern businesses. Artificial intelligence (A.I.) endeavors to bestow upon computers the ability to perform cognitive tasks akin to human minds. Specific tasks, such as reasoning, are typically attributed to possessing intelligence, while others, such as vision, are not [56]. Nonetheless, all such tasks require psychological ability, including but not limited to awareness, connection, forecasting, preparing, and motor control, which allow humans to achieve their goals [1]. Intelligence is not a unidimensional construct but encompasses a multifaceted array of information-processing competencies. Consequently, A.I. employs diverse techniques to tackle a broad spectrum of tasks [27]. A.I. is a field that aims to design machines with intelligence on par with or even superior to that of humans [46]. The ultimate goal of artificial intelligence is to give machines the ability to do mental functions previously reserved for humans [79]. A variety of AI implementations have emerged throughout the years, from earlier rule-based systems to more recent data-driven strategies. Numerous industries, including as healthcare, banking,

Artificial Intelligence: An Overview

5

transportation, and entertainment, have benefited from its implementation [72]. These use cases range in complexity from making recommendations for entertainment (like music or movies) to making medical diagnoses and operating vehicles [85]. A few of the many AI subfields that exist include robots, the processing of natural language, and professional systems, each with its own distinct set of issues to address and strategies to employ [3]. However, as attention has drawn researchers from many disciplines, this chapter will continue exploring the topic while delving into A.I.’s history, the relationship between A.I. and humans, A.I.’s impact on businesses, and its role in management.

2

Historical Background of Artificial Intelligence

It’s been a century since Alan Turing first described A.I. A universal Turing machine is a mathematical system established by Turing in 1936, capable of doing any calculation [25]. The hypothetical system creates and modifies binary symbol combinations using 0 and 1. After his work deciphering codes at Bletchley Park during World War II, Turing devoted the rest of the 1940s to considering how the theoretical Turing machine could be realized in hardware (he was instrumental in the development of the first modern computer, which was completed in Manchester in 1948) [53]. American science fiction writer Isaac Asimov authored a short tale titled “Runaround” in 1942 about two engineers named Gregory Powell and Mike Donavan who build a robot [40]. The plot revolves around the three principles of Robotics, which state that robots should not hurt humans, carry out human commands, and protect themselves without violating the other two principles. Many robotics, artificial intelligence, and computer science researchers have cited Asimov as an influence [88]. One such researcher is American cognitive scientist Marvin Minsky, who helped establish the MIT Artificial Intelligence Laboratory. As a result, Isaac Asimov’s contribution to the development of robotics and A.I. is highly regarded in the scientific community [40]. Turing [97] proposed a pragmatic assessment of artificial intelligence that is more appropriate for computer scientists trying to apply artificial intelligence on computer systems rather than delving into the philosophical issue of what it means for an artificial entity to be intelligent or to think. The Turing test is a practical demonstration of brightness that can be used to draw firm conclusions about a thing’s level of intelligence [35]. Three rooms are involved in the test: one with the human interrogator, another with another person, and the third with an artificial creature. Only through a textual instrument like a terminal is the interrogator authorized to speak with the other person and the artificial entity. The other human or artificial entity must be identified based on responses to questions posed by the interrogator. If the questioner cannot tell the two apart, the artificial entity passes the Turing test and is called intelligent [44].

6

A. Jaboob et al.

Fig. 1 Timeline of notable artificial intelligence systems

The Turing test is essential to artificial intelligence, as it provides a practical and quantitative way to measure an entity’s intelligence rather than relying on subjective definitions. Additionally, the test is designed to be adaptable to different contexts, allowing for a broad range of applications. Although the Turing test has flaws, it is a valuable standard for evaluating A.I. performance. Overall, the Turing test is a critical tool for increasing artificial intelligence and constructing smart machines that can effectively interact with people [78]. Nevertheless, the swift pace at which the world has transformed is evident by the antiquated feel of even relatively recent computer technology [80]. In the 1990s, mobile phones resembled bulky bricks with minuscule green displays. Punch cards served as the primary storage medium for computers two decades prior [75]. The rapid and pervasive evolution of computers as an integral facet of our daily lives renders it effortless to overlook the recent advent of this technology [64]. The timeline shows that the first digital computers appeared only eight decades ago. From the inception of computer science, sure scientists endeavored to create machines with human-like intelligence [95]. The following timeline delineates noteworthy artificial intelligence (A.I.) systems and their capabilities. Theseus, built by Claude Shannon in 1950, is the first system reported; it was a remote-controlled mouse that could escape a maze and remember its way back. Over seven decades, artificial intelligence has made remarkable progress [64] (see Fig. 1).

3

Literature Review

3.1

A.I. Structure

Understanding the organization of A.I. can be challenging, as it is common to witness people struggling with differentiating between terms such as machine learning and deep learning. However, it is of utmost importance to grasp the distinctions between these concepts. Looking at Fig. 2, we can observe the interrelation between the primary elements of A.I. the apex of the hierarchy represents A.I., which encompasses various theories and

Artificial Intelligence: An Overview

Deep Learning

7

Machine Learning

Fig. 2 Primary elements of A.I

technologies. This aspect can be divided into two principal categories: machine learning and deep learning [29].

3.1.1

Machine Learning

To enable robots to learn and make data-based judgments, algorithms and models must be developed [48]. At the core of Machine Learning (ML), a powerful branch of artificial intelligence [20], is the ability for machines to learn from data, develop over time, and make predictions without being explicitly programmed for the task (Fig. 2). ML systems use algorithms that discover patterns from vast volumes of data, as opposed to the hardcoded rules used by classic AI systems [86]. Within ML, multiple paradigms exist that are better suited to specific tasks [54]. For instance, in supervised learning, models need labeled data to be trained so that they can make predictions about new, unseen data. In contrast, unsupervised learning seeks to find patterns and structures in data without first categorizing the data. Agents in a reinforcement learning paradigm act in the environment to maximize a certain metric of cumulative reward [46]. Machine learning has become the backbone of many modern applications, from stock market forecasting to facial recognition systems, therefore its importance in contemporary AI solutions cannot be understated [10].

3.1.2

Deep Learning

DL. is focused on creating neural networks that resemble the human brain to do challenging tasks like speech and image recognition [49]. It is a subfield of machine learning that mimics the structure of the human brain using artificial neural networks and is inspired by how the brain functions (Fig. 2). The “neurons” (the nodes) in these networks are organized into tiers [103]. By definition, “deep” neural networks, which have several levels, are used in Deep Learning [47]. With such granularity, they can simulate intricate non-linear data patterns, making them ideal for applications like image and speech recognition [67]. Among the deep learning architectures, Convolutional Neural Networks (CNNs) stand out for their superiority in image processing, while Recurrent Neural Networks (RNNs) are well-suited to time series analysis and natural language processing because of their proficiency in processing sequences [71]. Natural language processing has been greatly advanced by designs like Transformers, which have produced state-of-the-art models like BERT and GPT for applications as diverse as text completion and sentiment analysis

8

A. Jaboob et al.

[107]. Recent breakthroughs and triumphs in the field of artificial intelligence (AI) can be largely attributed to the enormous potential of deep learning in conjunction with an increase in computer capacity and data availability [83].

3.2

A.I. Environment

The concept of an environment concerning an A.I. system pertains to a spatial domain that can be perceived via sensors and acted upon through actuators [59]. These sensors and actuators can be operated by either machines or humans. Environments can be classified as either authentic (i.e. physical, social, or mental) and are typically only partially observable, or artificial (i.e. board games) and are generally fully visible [28]. A.I.‘s ‘environment’ is a crucial thread in the field’s complex fabric. Simply said, it’s the area of operation for artificial intelligence systems, where they can analyze data, draw conclusions, and take appropriate action [34, 77]. Actuators that allow for a reaction to stimuli and sensors that detect them make these things possible. These can be mechanical sensors and actuators, or they can be human interfaces. This setting is more than simply a static backdrop; it’s also a dynamic training ground for A.I. systems. It is possible to classify A.I.‘s environments as either “real world” or “simulated.“ Physical, social, and cognitive settings mimicking nature are considered authentic [43, 46]. A self-driving car, for instance, must interact with the actual world as it makes its way through a crowded city, gathering information from the roads, the traffic signals, the people, and the other cars [37]. Similarly, chatbots on social media portray the social dimension by navigating the complex Web of human emotions, behaviors, and interactions. On the other hand, A.I. solutions like mental health aids access to the user’s mental or emotional condition to provide feedback [108].

3.3

A.I. System

An A.I. system operates through a machine and can make recommendations, predictions, or decisions that influence real and virtual environments. Its operation is based on the use of device and or human-based inputs, which are then processed to perceive both real and virtual environments [70, 110]. These perceptions are abstracted into models through automated analysis using machine learning or manual processing techniques. The model inference is mainly utilized to formulate options for information or action. A.I. systems are designed to operate with varying degrees of autonomy [82]. Intelligent systems can proficiently manage intricate situations and arrive at sophisticated decisions. Comprising an array of techniques, Intelligent systems furnish versatile data information processing competencies to adeptly handle real-world scenarios [82]. Solutions generated by intelligent systems are tractable, robust, and cost-effective because they use tolerance for imprecision, uncertainty/ambiguities, approximate reasoning, and partial truth [92].

Artificial Intelligence: An Overview

3.4

9

Natural Language Processing

(NLP) is a comprehensive and structured approach employed by computers to gather knowledge on human usage, application, and understanding of language [52, 65, 66]. Computers’ ability to understand and work with text has grown dramatically thanks to AI-aided research and development [90]. The study of natural language processing (NLP) digs into how humans communicate through speech and text and underpins various language technologies, from predictive text to email filtering. The development of NLP has been fueled by studies of mathematical and computational modelling of different aspects of language and a plethora of systems, all to make computers act intelligently like people. The field of artificial intelligence, known as natural language processing (NLP), investigates the similarities and differences between human and machine speech. Natural language processing (NLP) helps level the playing field between humans and machines [11, 76].

3.5

Artificial Subfields

3.5.1

Machine Learning

(ML) is an area of computer science concerned with automating the process by which computers acquire knowledge by recognizing patterns in data [82]. Machine learning (ML) is a discipline encompassing the conception and implementation of algorithms and statistical models that enable computer systems to execute tasks without explicit directives, drawing on patterns and inference instead [6]. It represents one of the various branches of Artificial Intelligence. Generative A.I., one type of artificial intelligence, can generate novel content and concepts, such as dialogues, narratives, pictures, videos, and melodies [74]. As with all artificial intelligence, machine learning models fuel generative A.I. Machine learning is a complex discipline that integrates three vital elements: model, data, and loss. The theoretical foundation of machine learning is built upon the principle of experimentation and refinement. The iterative process of machine learning techniques involves constantly evaluating the model’s efficacy by assessing its loss concerning the predictions about a phenomenon that leads to data generation [99].

3.5.2

Data Mining

Knowledge discovery in data, or KDD, is another name for data mining., is extracting valuable information from large datasets. This contemporary technology has become a dominant force in computer science, enabling public and private organizations to uncover and concentrate on the most significant data within vast data blocks. Furthermore, data exploration approaches aim to identify patterns and construct predictive models, facilitating decision-making processes [91].

10

3.5.3

A. Jaboob et al.

Information Retrieval and Semantic Web

Information Retrieval (I.R.) refers to the complete process of retrieving a set of pertinent documents through the execution of a query inputted into a search engine, as posited by Broder [15], Shen et al. [89]. The emergence of a novel form of Web content that holds significance to computers is poised to bring about a revolution of unprecedented possibilities. From one viewpoint, the sphere revolves around the ultimate objective of producing The Semantic Web [1]. This entity encompasses all the indispensable tools and techniques for its creation, upkeep, and application. This narrative typically envisions The Semantic Web as a superior version of the existing World Wide Web, replete with machine-readable data (in contrast to most current Web, which is tailored towards human consumption) and intelligent agents and services that utilize this data. This perspective can be traced back to the Scientific American Article of 2001, which is widely considered the birth of this field and shall be expounded upon below [33].

3.5.4

Speech Recognition and Natural Language Processing

Automatic Speech Recognition (ASR) is another name for Speech Recognition or computer speech recognition, pertains to transforming a speech signal into a sequence of words by utilizing an algorithm integrated into a computer program [57]. Natural Language Processing (NLP) is a field of study and practice investigating how computers can be programmed to interpret and manipulate text and speech written or spoken in natural languages. To create computer systems that can analyze and manage natural languages to carry out the intended tasks, natural language processing (NLP) researchers collect data on how humans understand and utilize language [22].

3.5.5

Image Processing/Recognition and Computer Vision

The discipline of computer vision involves generating detailed and significant representations of physical objects using visual images [9]. Developing a recognition system has arisen as a formidable challenge within computer vision, with the ultimate objective of approaching human-level recognition for numerous categories amidst diverse circumstances [8]. This system is critical in optical character recognition, voice recognition, and handwriting recognition, employing techniques derived from statistics, machine learning, and other related domains [81]. While image processing involves the preliminary processing of the raw image. The obtained or acquired images are transferred onto a computing device and transformed into digital images [24]. Despite their visual representation as pictures on a screen, digital images are numerical data that can be understood by a computer and converted into infinitesimally small dots or picture elements that correspond to the actual things. According to Venugopal [100], there are three methods for techniques of Image processing (Image Acquisition, Image Preprocessing, Segmentation).

Artificial Intelligence: An Overview

3.5.6

11

Robotics

Mechanical engineering, electronics, computer science, and other fields come together in robotics for their study and use in robot design, construction, and operation. Robotics goes beyond simple automation when it is programmed with AI, allowing it to complete tasks with a newfound level of intelligence, flexibility, and independence [105].

3.5.7

Search

When discussing AI, the term “search” typically refers to search algorithms designed to sift among potentially innumerable possible solutions in order to locate one that meets a set of criteria or adheres to a set of limitations. One of the cornerstones of artificial intelligence is the idea of a search, with many AI-related issues being framed as search problems [12].

3.5.8

Knowledge Representation and Knowledge Database

Knowledge Representation (KR) is an important subfield in artificial intelligence (AI) concerned with the development of machine-readable encoding schemes for data pertaining to human understanding of the world. With this embedded knowledge, AI systems can do things that previously required human intelligence, such as make judgments, draw conclusions, and carry out tasks. The primary motivation behind KR is the desire to create a machine-readable representation of human thought [69].

3.5.9

Logic Reasoning and Probabilistic Reasoning

AI researchers use the term “logic reasoning” to refer to the practice of drawing conclusions based on premises and evidence in a systematic way. This strategy guarantees that inferences are generated from legitimate principles, leading to results that are either unambiguously true or incorrect, depending on the premise [45]. Probabilistic reasoning is the AI method of dealing with unknowns using principles of probability theory. The probability or likelihood of multiple events is assessed rather than the absolute truth. For artificial intelligence systems, this mode of reasoning is crucial for arriving at sound conclusions in the face of uncertainty. [41]

3.5.10 Expert System To make decisions like a human expert in a certain field, an Expert System is programmed to mimic human intelligence. It does this by applying a set of rules and a knowledge base to draw conclusions about the world based on input data [111]. Expert systems, which have been utilized in many different fields to provide specialized knowledge and problem-solving expertise, are one of the earliest accomplishments of artificial intelligence [31].

12

3.6

A. Jaboob et al.

A.I. and Human

A.I. systems have autonomously performed tasks to mimic human cognition, including decision-making, feature recognition, and anomaly detection [97]. An intelligent system would need to learn independently to carry them out [17]. To provide computers with the ability to learn on their own, the area of machine learning (ML) was created. Machine learning (ML) researchers study how well computers can do non-programmed tasks like pattern recognition and classification [61]. Hence, ML grants systems with human-like intelligence the ability to independently identify data patterns, solve problems with higher precision and efficiency, and eliminate the requirement for explicit algorithmic instructions [13]. The developments in A.I. and ML are perceived to be merely the beginning, and with the growing digitization of our lives, more progress is expected. Technically speaking, this is genuinely exhilarating [96]. However, from an individual and societal viewpoint, there is a sense of apprehension regarding the competition between humans and A.I. This fear may be palpable and well-founded, as people may wonder whether their skill sets will be replaced by intelligent agents [62]. On the other hand, it may also be highly theoretical and currently unsubstantiated, as people may question whether A.I. will take over and dominate humanity [32]. We are hoping to leverage these advances to the benefit of the majority of people. Experts predict that the emergence of artificial intelligence will improve most people’s lives in the next decade; however, many are anxious about how the progress in A.I. will impact the essence of being human, productivity, and free will [84].

3.7

A.I. and Society

Society, in its most imaginative state, could never have envisioned the advent of selfdriving cars, unmanned aircraft, Skype communication, supercomputers, smartphones, or intelligent robots [26]. These technological advancements, once considered pure science fiction less than two centuries ago, are now readily available and likely to be ubiquitous within the next two decades. The task at hand is to make a realistic prediction of upcoming AI technologies without succumbing to the same myopic tendencies as those of Makridakis [63] who were unable to comprehend the profound and nonlinear advancements of new technologies. However, The Electronic Privacy Information Center founded the Public Voice alliance, which disseminated the Universal Guidelines on Artificial Intelligence (UGAI) in October 2018. The UGAI illustrates the growing difficulties faced by intelligent computational systems and provides useful suggestions to improve and guide their design [38]. By encouraging the openness and accountability of A.I. systems, the UGAI works to ensure that people keep control over the systems they design. The 12 principles of the UGAI include the obligations of correct identification, fair evaluation, responsibility, correctness, reliability, validity, data quality, public safety, cybersecurity, and termination, as

Artificial Intelligence: An Overview

Representation

13

Anthropomorphism

Humanoid Versus non-Humanoid

Task Orientation

(Cognitive-Analytical)

Fig. 3 Categories of services robots

well as the rights of transparency and autonomy for individuals. Additionally, the UGAI precludes conventional scoring and anonymous profiling [50].

3.8

A.I. and Firms

The modern world necessitates change, which can be either unsettling or motivating due to evolution. (A.I.) is a branch of computer science that endeavors to generate mechanisms that can replicate human cognition, such as thinking, comprehending, and problem-solving, or the ability to exhibit intelligence [63]. This competence can expedite processes while reducing inaccuracies and inconsistencies, curbing associated costs. People today are often apprehensive of machines and may worry about losing their jobs and unemployment. As a way to mitigate risk and reduce costs, companies are increasingly turning to automation [51]. Consequently, it is anticipated that automation will expand and robots will play an integral role in supporting human workers’ preferences [104]. “Service robots are autonomous and adaptable interfaces based on systems that interact, communicate, and provide service to organizational consumers” [73]. Three main design characteristics help us categorize service robots as shown in Fig. 3: representation (humanoid versus non-humanoid), anthropomorphism (humanoid versus non-humanoid), and task orientation (cognitiveanalytical), like software analysis for medical diagnosis, versus emotional-social, like reception robots) [106]. The next generation of artificial intelligence technologies may have far-reaching consequences on businesses and the economy in the future [73]. Ultimately, artificial intelligence can do various tasks, significantly boosting innovation and threatening human employment [16]. Companies must decide whether to use humans or machines to execute service jobs that need the following four forms of intelligence: mechanical, analytical, intuitive, and empathetic.

3.9

A.I. and Management Process

Scholarly interest in the possibility that A.I. and machine learning will one day replace humans in the workforce, take over previously held positions, and alter long-standing

14

A. Jaboob et al.

practices within organizations has grown significantly in recent years. The underlying assumption is that A.I. can outperform human specialists in quality, efficiency, and results under particular information processing conditions [2, 18]. Firms currently rely heavily on human-managed innovation management to innovate through risk-taking strategies. The capabilities of people are limited, but A.I. can give instrumental support that goes beyond what humans are capable of Groves et al. [36], Wamba et al. [102]. Experts in the field and academics have speculated that A.I. will significantly impact how businesses handle innovation in the future. The rapid growth of A.I. and machine learning predicts significant and intriguing developments shortly [30, 58, 98], further supporting the idea that A.I. might be used in innovation contexts. However, our understanding of the bounds of A.I. still needs to be improved in the context of innovation. The application of A.I. and ML to creative and innovative processes differs significantly from the typical applications where these technologies have replaced management [23]. When considering implementing A.I., viewing management from a macro, micro, and meso level may be helpful. Incorporating such a multifaceted and segmented perspective into business management education is essential [39]. Incorporating artificial intelligence in management, specifically in individual-based enterprises, has been a topic of interest [93]. Artificial intelligence, derived from human intelligence, is categorized into various subgroups. However, it is a technology that involves teaching and presenting certain human-developed features through these technologies. Recent research has demonstrated that A.I. can evolve like the human brain, possess learning and analytical abilities, and generate unique user experiences. The fundamental aspect of A.I.’s functionality is its reliance on knowledge. Integrating A.I.’s capabilities, particularly in terms of development, in business can provide significant benefits analogous to the human brain [101]. It can yield substantial advantages for managers and employees, particularly at the micro level. The following statements outline the potential benefits of utilizing artificial intelligence (A.I.) in various educational and business contexts [4]. . . . . . . . . .

A.I. can automate rudimentary managerial activities. A.I. can provide tailored training to meet the unique needs of individual employees. A.I. can identify employee weaknesses and offer direct assistance for improvement. A.I. can be utilized to train staff in business management and support employee education and skills. A.I. programs can offer valuable feedback to employees. A.I. can transform practitioners’ roles by altering their interaction with knowledge. A.I. may facilitate more active trial-and-error learning. Data-driven decision-making through AI can be generated easily A.I. can reduce errors through Automation and process optimization.

Artificial Intelligence: An Overview

15

. A.I. algorithms possess the capability to meticulously scrutinize past data and identify recurring patterns, thereby enabling the prediction of future outcomes through the employment of predictive analytics and forecasting techniques. . A.I. can improve efficiency in talent management aspects such as recruitment, skill development, and employee performance evaluation. A.I. can support risk management and fraud detection by identifying anomalies, patterns, and potential risks [55]. Using first-generation artificial intelligence (A.I.) in specific tasks has become commonplace among various organizations [5]. A growing body of research suggests that second-generation A.I. will supersede first-generation A.I. in the not-too-distant future, with the latter able to reason, plan, and independently solve problems for tasks beyond its original purpose [40]. However, the uniquely human aspects of contemporary management, such as social interactions with workers and emotional intelligence among managers and employees, are unlikely to be seriously threatened by artificial general intelligence. However, in the long run, conscious and self-aware machine-based systems may succeed the emergence of artificial general intelligence, with the expectation that they will exhibit scientific creativity, social capabilities, and prevailing wisdom that have traditionally been associated with humans [109]. These innovations will likely make humans obsolete. The advent of artificial superintelligence will probably be the tipping point at which humans and their work are profoundly challenged, requiring change or even annihilation. Generative Pre-trained Transformer-3 (GPT), autoregressive language models, conversational systems, and immersive technologies [7] are all examples of cutting-edge AI-based technologies that foreshadow potential difficulties for humanity and the workforce in the event of the widespread development of artificial superintelligence.

4

Conclusion

In conclusion, this new era, marked by the widespread adoption of A.I. and automation, has changed the innovation landscape and redefined how work is completed in many different sectors. The many facets of artificial intelligence have been explored in this chapter, from its theoretical underpinnings and historical context to several subfields, such as machine learning, natural language processing, computer vision, and robotics. The future of A.I. holds both encouraging and mind-boggling possibilities for our communities and businesses. The capabilities of A.I. to do cognitive tasks and handle large volumes of data have been investigated, along with its definition, properties, and subfields. The development of artificial intelligence from Alan Turing’s theoretical foundations to the current day is illustrative of the remarkable progress made in a short time. The Turing test has been crucial in advancing A.I. since it is a valuable baseline for evaluating intelligent behavior. Understanding the hierarchical structure of A.I., from machine learning to deep learning,

16

A. Jaboob et al.

has been clarified, as has the significance of doing so for a thorough familiarity with the area. The ability of A.I. to interact with and operate independently within its environment has been clarified, drawing attention to its potential to aid in decision-making and issuesolving. Concern and anticipation about the integration of A.I. and human skills have been uncovered through studies of the dynamic interplay between the two. The ramifications of A.I. for organizations and management procedures and its potential societal influence have been studied extensively. Despite A.I.’s potential to improve productivity, education, and decision-making, worries about automation taking over human jobs and changing the nature of work remain. The significance of ethical considerations in A.I. development and the necessity to overcome biases and transparency within A.I. systems have been emphasized. The chapter finishes by emphasizing the continuing importance of studying and exploring artificial intelligence in light of the field’s rapid development. A.I. has tremendous potential to improve our lives, but only if we use it responsibly, which requires ongoing awareness, study, and ethical reflection. Understanding the theoretical underpinnings of A.I. and its potential repercussions remains essential for designing a future that combines innovation with societal well-being as we traverse this ever-changing world.

5

The Implication of AI in Future Management Process

The advent of AI has introduced revolutionary consequences that will reshape the future of corporate management and the ways in which organizations function. First, management’s fundamental decision-making process is about to undergo radical change. Analytics systems powered by AI can quickly sort through mountains of data, spot trends, and draw conclusions. This information will help managers make better, faster choices. Businesses can be proactive rather than reactive with the use of predictive analytics, which can predict market movements, client preferences, and even future supply chain interruptions. Another area that could benefit from AI-driven innovation is operational efficiency. Automation of routine operations, like inventory management and scheduling, ensures accuracy while freeing up managers to focus on more high-level, strategic responsibilities. Moreover, AI can provide real-time optimization of processes. An artificial intelligence system at a factory, for instance, might dynamically alter the production line to account for changes in raw material supplies, customer demand, and employee availability in order to maintain maximum productivity. AI may help with hiring by doing things like reviewing applications, making success predictions, and even conducting preliminary interviews. After being hired, employees’ onboarding and ongoing education can be sped up with the use of AI-driven training modules. Further, AI can aid managers in understanding team morale and proactively addressing concerns, thereby establishing a healthy work environment through analysis of employee behavior and feedback. However, there will be difficulties in an AI-driven

Artificial Intelligence: An Overview

17

future. Managers face challenges such as ethical usage of AI, job loss fears, and data security. Managers must find a middle ground where AI helps processes but where human judgment, intuition, and emotional intelligence are still valued and implemented [19]. A more data-driven, efficient, and predictive kind of management is what AI offers for the future. Still, leaders will ultimately be judged on their ability to use their own discretion, compassion, and vision. As AI becomes standard in management, it will become increasingly important for executives to combine technology expertise with a focus on people.

References 1. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. 2. Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1–6. 3. Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. 4. Akerkar, R. (2019). Artificial intelligence for business. Springer. 5. Alzyoud, A. A. Y. (2022, June). Artificial intelligence for sustaining green human resource management: A literature review. In 2022 ASU international conference in emerging technologies for sustainability and intelligent systems (ICETSIS) (pp. 321–326). IEEE. 6. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Herrera, F., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. 7. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: A conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75–88. 8. Balcombe, L., & De Leo, D. (2022, February). Human-computer interaction in digital mental health. In Informatics (Vol. 9, No. 1, p. 14). MDPI. 9. Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2021). A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets. The Visual Computer, 1–32. 10. Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222–2239. 11. Borgman, C. L. (1997). Multi-media, multi-cultural, and multilingual digital libraries. DLib, 3(6). 12. Bornstein, A. (Ari). (2019, September 20). AI Search Algorithms Every Data Scientist Should Know. Medium. https://towardsdatascience.com/ai-search-algorithms-every-data-scientist-sho uld-know-ed0968a43a7a#:~:text=Search%20in%20AI%20is%20the 13. Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(1), 1–99. 14. Brenner, N., & Schmid, C. (2015). Towards a new epistemology of the urban? City, 19(2–3), 151–182.

18

A. Jaboob et al.

15. Broder, A. (2002, September). A taxonomy of web search. In ACM Sigir forum (Vol. 36, No. 2, pp. 3–10). ACM. 16. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Amodei, D., et al. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv:1802.07228 17. Buche, C., Bossard, C., Querrec, R., & Chevaillier, P. (2010). PEGASE: A generic and adaptable intelligent system for virtual reality learning environments. International Journal of Virtual Reality, 9(2), 73–85. 18. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, 4. 19. Canals, J., & Heukamp, F. (2020). The future of management in an AI world. Palgrave Macmillan. 20. Chattu, V. K. (2021). A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data and Cognitive Computing, 5(3), 41. 21. Chen, M., Herrera, F., & Hwang, K. (2018). Cognitive computing: Architecture, technologies and intelligent applications. IEEE Access, 6, 19774–19783. 22. Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603–649. 23. Chui, M., Henke, N., Miremadi, M. (2018). Most of A.I.’s business will be in two areas. Harvard Business Review, 3–7. 24. Das, A., Nair, M. S., & Peter, S. D. (2020). Computer-aided histopathological image analysis techniques for automated nuclear atypia scoring of breast cancer: A review. Journal of Digital Imaging, 33, 1091–1121. 25. Deutsch, D. (1985). Quantum theory, the Church–Turing principle and the universal quantum computer. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818), 97–117. 26. Dsouza, D. J., Srivatsava, S., & Prithika, R. (2019). IoT based smart wheelchair for HealthCare. International Journal of Recent Technology and Engineering (IJRTE). 27. Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974. 28. Duffy, B. R. (2003). Anthropomorphism and the social robot. Robotics and Autonomous Systems, 42(3–4), 177–190. 29. Dwivedi, M., Malik, H. S., Omkar, S. N., Monis, E. B., Khanna, B., Samal, S. R., Rathi, A., et al. (2021). Deep learning-based car damage classification and detection. In Advances in artificial intelligence and data engineering: Select proceedings of AIDE 2019 (pp. 207–221). Springer Singapore. 30. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Williams, M. D., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. 31. Fenves, S. J. (1986, April). What is an expert system. In Expert systems in civil engineering (pp. 1–6). ASCE. 32. Floridi, L. (2020). AI and its new winter: From myths to realities. Philosophy & Technology, 33, 1–3. 33. Georgakopoulos, D., & Jayaraman, P. P. (2016). Internet of things: From internet scale sensing to smart services. Computing, 98, 1041–1058. 34. Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Uhlig, S., et al. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.

Artificial Intelligence: An Overview

19

35. Goertzel, B. (2014). Artificial general intelligence: Concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 5(1), 1. 36. Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’ revolution in healthcare. McKinsey Quarterly, 2(3), 1–22. 37. Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10, 100057. 38. Gwagwa, A., Kraemer-Mbula, E., Rizk, N., Rutenberg, I., & De Beer, J. (2020). Artificial Intelligence (AI) deployments in Africa: Benefits, challenges and policy dimensions. The African Journal of Information and Communication, 26, 1–28. 39. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. 40. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On artificial intelligence’s past, present, and future. California Management Review, 61(4), 5–14. 41. Haenni, R. (2005, July). Towards a unifying theory of logical and probabilistic reasoning. In ISIPTA (Vol. 5, pp. 193–202). 42. He, H., Maple, C., Watson, T., Tiwari, A., Mehnen, J., Jin, Y., & Gabrys, B. (2016, July). The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence. In 2016 IEEE congress on evolutionary computation (CEC) (pp. 1015–1021). IEEE. 43. He, J., Zhang, Y., Zhou, R., Meng, L., Chen, T., Mai, W., & Pan, C. (2020). Recent advances of wearable and flexible piezoresistivity pressure sensor devices and its future prospects. Journal of Materiomics, 6(1), 86–101. 44. Hingston, P. (2009). A turing test for computer game bots. IEEE Transactions on Computational Intelligence and AI in Games, 1(3), 169–186. 45. Hodges, W. (1993). The logical content of theories of deduction. Behavioral and Brain Sciences, 16(2), 353–354. 46. Holzinger, A. (2018, August). From machine learning to explainable AI. In 2018 world symposium on digital intelligence for systems and machines (DISA) (pp. 55–66). IEEE. 47. Hua, Q., Sun, J., Liu, H., Bao, R., Yu, R., Zhai, J., Wang, Z. L., et al. (2018). Skin-inspired highly stretchable and conformable matrix networks for multifunctional sensing. Nature Communications, 9(1), 244. 48. Hua, T. K. (2022). A short review on machine learning. Authorea Preprints. 49. Huixian, J. (2020). The analysis of plants image recognition based on deep learning and artificial neural network. IEEE Access, 8, 68828–68841. 50. Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. 51. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. 52. Kawamura, T., Egami, S., Tamura, K., Hokazono, Y., Ugai, T., Koyanagi, Y., Kozaki, K., et al. (2020). Report on the first knowledge graph reasoning challenge 2018: Toward the eXplainable AI system. In Semantic technology: 9th joint international conference, JIST 2019, Hangzhou, China, November 25–27, 2019, Proceedings 9 (pp. 18–34). Springer International Publishing. 53. Khogali, H. O., & Mekid, S. (2023). The blended future of automation and AI: Examining some long-term societal and ethical impact features. Technology in Society, 73, 102232. 54. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744.

20

A. Jaboob et al.

55. Kurzweil, R. (1985). What Is Artificial Intelligence Anyway? As the techniques of computing grow more sophisticated, machines are beginning to appear intelligent—but can they actually think? American Scientist, 73(3), 258–264. 56. L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning with big data: Challenges and approaches. IEEE Access, 5, 7776–7797. 57. Lemos, J., Gaspar, P. D., & Lima, T. M. (2022). Environmental risk assessment and management in Industry 4.0: A review of technologies and trends. Machines, 10(8), 702. 58. Lewis, P. R., & Marsh, S. (2022). What is it like to trust a rock? A functionalist perspective on trust and trustworthiness in artificial intelligence. Cognitive Systems Research, 72, 33–49. 59. Li, J., Deng, L., Gong, Y., & Haeb-Umbach, R. (2014). An overview of noise-robust automatic speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(4), 745–777. 60. Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6, 1–29. 61. Maes, P. (1993). Modeling adaptive autonomous agents. Artificial Life, 1(1_2), 135–162. 62. Magd, H., Jonathan, H., Khan, S. A., & El Geddawy, M. (2022). Artificial intelligence—the driving force of Industry 4.0. A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, 1–15. 63. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research, 9(1), 381–386. 64. Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. 65. Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. 66. Mann, S. (1997, February). “Smart clothing” wearable multimedia computing and “personal imaging” to restore the technological balance between people and their environments. In Proceedings of the Fourth ACM International Conference on Multimedia (pp. 163–174). 67. Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press. 68. Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson/Prentice Hall. 69. Mozo, A., Ordozgoiti, B., & Gomez-Canaval, S. (2018). Forecasting short-term data center network traffic load with convolutional neural networks. PLoS One, 13(2), e0191939. 70. Mühlroth, C., & Grottke, M. (2020). Artificial intelligence in innovation: How to spot emerging trends and technologies. IEEE Transactions on Engineering Management, 69(2), 493–510. 71. Mylopoulos, J. (1980). An overview of knowledge representation. ACM SIGART Bulletin, 74, 5–12. 72. Nahavandi, S. (2019). Industry 5.0—a human-centric solution. Sustainability, 11(16), 4371. 73. Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., & Hossard, L. (2018). Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sensing, 10(8), 1217. 74. Niu, G. (2017). Data-driven technology for engineering systems health management. Springer Singapore, 10, 978–981. 75. Paluch, S., Wirtz, J., & Kunz, W. H. (2020). Service robots and the future of services. Marketing Weiterdenken: Zukunftspfade für eine marktorientierte Unternehmensführung, 423–435. 76. Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84–93.

Artificial Intelligence: An Overview

21

77. Pegler, M. M., & Bliss, L. L. (2012). Visual merchandising and display. 78. Peters, C., & Picchi, E. (1997). Across languages, across cultures: Issues in multilinguality and digital libraries. D-Lib Magazine, 3(5). 79. Piccialli, F., Di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23(6), 1467–1497. 80. PK, F. A. (1984). What is artificial intelligence? “Success is no accident. It is hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing or learning to do” (p. 65). 81. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation– augmentation paradox. Academy of Management Review, 46(1), 192–210. 82. Rayman-Bacchus, L., & Molina, A. (2001). Internet-based tourism services: Business issues and trends. Futures, 33(7), 589–605. 83. Sankara Babu, B., Nalajala, S., Sarada, K., Muniraju Naidu, V., Yamsani, N., & Saikumar, K. (2022). Machine learning based online handwritten Telugu letters recognition for different domains. A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems, 227–241. 84. Sarker, I. H. (2022). Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. 85. Saxe, A., Nelli, S., & Summerfield, C. (2021). If deep learning is the answer, what is the question? Nature Reviews Neuroscience, 22(1), 55–67. 86. Schmidt, A. (2020, September). Interactive human centred artificial intelligence: A definition and research challenges. In Proceedings of the International Conference on Advanced Visual Interfaces (pp. 1–4). 87. Series, M. (2015). IMT Vision–Framework and overall objectives of the future development of IMT for 2020 and beyond. Recommendation ITU, 2083(0). 88. Sha, W., Guo, Y., Yuan, Q., Tang, S., Zhang, X., Lu, S., Cheng, S., et al. (2020). Artificial intelligence to power the future of materials science and engineering. Advanced Intelligent Systems, 2(4), 1900143. 89. Sharifani, K., Amini, M., Akbari, Y., & Aghajanzadeh Godarzi, J. (2022). Operating machine learning across natural language processing techniques for improvement of fabricated news model. International Journal of Science and Information System Research, 12(9), 20–44. 90. Shaukat, K., Iqbal, F., Alam, T. M., Aujla, G. K., Devnath, L., Khan, A. G., Rubab, A., et al. (2020). The impact of artificial intelligence and robotics on the future employment opportunities. Trends in Computer Science and Information Technology, 5, 50–54. 91. Shen, M., Liu, D. R., & Huang, Y. S. (2012). Extracting semantic relations to enrich domain ontologies. Journal of Intelligent Information Systems, 39, 749–761. 92. Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., Shen, D., et al. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID19. IEEE Reviews in Biomedical Engineering, 14, 4–15. 93. Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. 94. Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management—a conceptual exploration. Intelligent Techniques in Engineering Management: Theory and Applications, 149–172. 95. Sulis, E., Terna, P., Di Leva, A., Boella, G., & Boccuzzi, A. (2020). Agent-oriented decision support system for business processes management with genetic algorithm optimization: An application in healthcare. Journal of Medical Systems, 44, 1–7. 96. Surden, H. (2014). Machine learning and law. Washington Law Review, 89, 87.

22

A. Jaboob et al.

97. Tariq, S., Iftikhar, A., Chaudhary, P., & Khurshid, K. (2023). Is the ‘Technological Singularity Scenario’ possible: Can AI parallel and surpass all human mental capabilities? World Futures, 79(2), 200–266. 98. Todd, O. T. (2022). “The Greatest Since the Days of the Apostles”: Hyperbole, exaggeration, and embellishment in the american revivalist tradition. Journal of Religious History, 46(1), 179–194. 99. Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433 100. Varian, H. (2018). Artificial intelligence, economics, and industrial organization. In The economics of artificial intelligence: An agenda (pp. 399–419). University of Chicago Press. 101. Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1–21. 102. Venugopal, N. (2020). Automatic semantic segmentation with DeepLab dilated learning network for change detection in remote sensing images. Neural Processing Letters, 51, 2355– 2377. 103. Vuong, Q. H., La, V. P., Nguyen, M. H., Jin, R., La, M. K., & Le, T. T. (2023). AI’s humanoid appearance can affect human perceptions of Its emotional capability: Evidence from selfreported data in the US. International Journal of Human–Computer Interaction, 1–12. 104. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S.J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. 105. Webb, S. (2018). Deep learning for biology. Nature, 554(7693), 555–557. 106. Welfare, K. S., Hallowell, M. R., Shah, J. A., & Riek, L. D. (2019, March). Consider the human work experience when integrating robotics in the workplace. In 2019 14th ACM/IEEE international conference on human-robot interaction (HRI) (pp. 75–84). IEEE. 107. Winfield, A. (2019). Ethical standards in robotics and AI. Nature Electronics, 2(2), 46–48. 108. Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907– 931. 109. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Rush, A. M., et al. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 38–45). 110. Xu, L., Sanders, L., Li, K., & Chow, J. C. (2021). Chatbot for health care and oncology applications using artificial intelligence and machine learning: Systematic review. JMIR Cancer, 7(4), e27850. 111. Yousafzai, S., Pallister, J., & Foxall, G. (2009). Multi-dimensional role of trust in Internet banking adoption. The Service Industries Journal, 29(5), 591–605. 112. Zebec, A., & Indihar Štemberger, M. (2020). Conceptualizing a capability-based view of artificial intelligence adoption in a BPM context. In Business process management workshops: BPM 2020 international workshops, Seville, Spain, September 13–18, 2020, Revised selected papers 18 (pp. 194–205). Springer International Publishing. 113. Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.

Application of Artificial Intelligence to Control a Nonlinear SIR Model Oussama Chayoukh and Omar Zakary

Abstract

This article compares the control of artificially controlled nonlinear SIR system using artificial neural networks (ANNs) and traditionally controlled SIR system with Pontryagin’s Minimum Principle (PMP) and difference approximation method. The study focuses on the significance of these control techniques in the field of applied mathematics, emphasizing the importance of accurate control in nonlinear systems. While PMP is a commonly used method, it has limitations, such as the possibility of being trapped in local minima. To address these limitations, the exploration of alternative approaches, including artificial intelligence, has gained attention. The research aims to fill the existing gaps by comparing the performance of ANNs and PMP in controlling nonlinear systems, specifically investigating whether ANNs can overcome the limitations of PMP and offer superior control outcomes. The research approach involves simulating the nonlinear system and implementing both PMP-based control with difference approximation method and ANN-based control using machine learning algorithms. The key message of this study is that ANNs have the potential to mitigate the issue of local minima trapping associated with PMP, thereby advancing control methods and offering insights into the benefits and drawbacks of different approaches in controlling complex nonlinear systems within the realm of applied mathematics. O. Chayoukh (B) · O. Zakary Laboratory of Analysis Modelling and Simulation, Department of Mathematics and Computer Science, Ben M’sik Faculty of Sciences, University Hassan II of Casablanca, B.P 7955 Sidi Othman, Casablanca, Morocco e-mail: [email protected] O. Zakary e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_2

23

24

O. Chayoukh and O. Zakary Keywords

Artificial intelligence • Artificial neural networks • Deep learning • Pontryagin’s minimum principle • SIR model • Nonlinear control

1

Introduction

The control of nonlinear systems is a fundamental challenge in the field of applied mathematics, with wide-ranging applications across various domains. In particular, the accurate control of complex systems is of paramount importance in areas such as engineering, economics, and epidemiology. In the context of epidemic modeling, where the dynamics of infectious diseases play a critical role, the ability to control and mitigate the spread of diseases is vital. In the other hand artificial intelligence (AI) has undergone significant growth in recent times and has become a major area of focus for the research community and practitioners across various fields. The primary goal of AI is to develop machines that can perform tasks typically requiring human intelligence, including learning, problemsolving, and decision-making. Even in the early stages of development, AI has shown significant promise and usefulness [1]. Traditionally, control methods built upon Pontryagin’s Minimum Principle (PMP) and difference approximation method have been extensively used to address nonlinear system control problems (see [2–10]). However, these methods are not without limitations. One significant limitation is the potential to get trapped in local minima, resulting in suboptimal control outcomes. Such limitations hinder the ability to effectively model and control complex systems, especially in the context of epidemic dynamics. To overcome these limitations and explore alternative control approaches, the field of artificial intelligence has gained substantial attention (see [11–15]). ANNs have emerged as a powerful tool within the realm of AI, offering the potential to provide more flexible and adaptive control solutions for nonlinear systems (see [16–18]). This article aims to investigate the effectiveness of ANNs compared to traditional control methods based on PMP and difference approximation methods in the control of nonlinear systems, with a specific focus on an SIR model for epidemic dynamics. By comparing the performance of ANNs and PMP, we aim to fill the existing gaps in the research and shed light on the potential of AI-based control approaches. Through simulations and analysis, we will evaluate the stability, accuracy, and computational efficiency of both ANNs and PMP-based control methods. By highlighting the benefits and drawbacks of each approach, this research will contribute to advancing the field of control methods in epidemic modeling and provide valuable insights for optimizing disease control strategies. In the upcoming sections, we will delve into the details of the research methodology, present the comparative results, and discuss the implications of these findings. Ultimately, our goal is to enhance our understanding of nonlinear system control, particularly in the

Application of Artificial Intelligence to Control a Nonlinear SIR Model

25

context of epidemic modeling, and explore the potential of AI-driven control methods in shaping effective strategies for disease containment and mitigation.

2

A Nonlinear Control System for an SIR Epidemic Model Incorporating Vaccination

The SIR model is ubiquitously utilized in epidemiology to simulate the spread of infectious diseases in simple cases. It consists of three variables: S, I , and R, denoting the count of susceptible, infected, and recovered individuals, respectively. We will consider a simple non-linear system of an SIR model. The equations for the SIR model are given by: ⎧ dS ⎪ ⎨ dt = −aSI dI (1) dt = aSI − rI ⎪ ⎩ dR dt = rI where S(t), I (t) and R(t) denote the count of susceptible, infected, and recovered individuals over time respectively, a is the transmission rate and r is the recovery rate, with the initial conditions S(0) = S0 , I (0) = I0 and R(0) = R0 . We introduce this nonlinear control system: ⎧ dS ⎪ ⎨ dt = −aSI − Su(t) dI (2) dt = aSI − rI ⎪ ⎩ dR dt = rI + Su(t) and u(t) represents the vaccination rate which is the control of the system. We want to minimize the objective functional J(u) over the interval of T days [0, T ], which is given by: J(u) =

1 2

{

T(

) AI (t)2 + Ku(t)2 dt

(3)

0

subject to 2 where A is a positive constant to maintain equilibrium in the magnitude of I (t), K is a positive parameter which is linked to the square of the control variable and infected population over time indicating the acuteness pertaining to the negative impacts of vaccination and infection. By reducing the count of infected individuals, we seek to minimize the objective function defined in 3 through employing a possible optimal control variable u* (t). To express it differently, we are in search of a u* (t) such that: u* = arg min J(u) u∈Γ

(4)

26

O. Chayoukh and O. Zakary

where Γ is the control set defined by: Γ = {u : u measurable, 0 ≤ u(t) ≤ 1, t ∈ [0, T ]}

(5)

By applying the findings presented in [19, 20], one can establish the existence of an optimal control in the studied system. To ensure this, several key criteria need to be evaluated. First, it is essential to confirm the non-emptiness of the set of controls and their corresponding state variables. Additionally, the control set Γ must exhibit properties of convexity and closedness. Furthermore, the right-hand side of the state system should be bounded by a linear function in both state and control variables. The integrand of the objective functional on Γ should demonstrate convexity. Finally, it is necessary to establish the existence of certain constants, namely C1 , C2 , and ρ, where C1 and C2 are positive, and ρ is greater than 1. These constants should satisfy a criterion related to the integrand L(I , u) of the objective functional, as expressed in Eq. 6. ( )ρ/2 L(I , u) ≥ C2 + C1 |u|2

3

Optimal Control Using PMP

3.1

Optimality System

(6)

To minimize J(u) using the Pontryagin’s minimum principle (see [21]), we need to compute the Hamiltonian, which is given by:

H=

1 2 AI + Ku2 + P1 (−aSI − Su) + P2 (aSI − rI ) + P3 (rI + Su) 2

the adjoint equations, which are given by: ⎧ d P1 ⎪ ⎨ dt = (P1 − P2 )aI + (P1 − P3 )u d P2 dt = −I + (P1 − P2 )aS + (P2 − P3 )r ⎪ ⎩ d P3 dt = 0

(7)

(8)

with transversality conditions:

P〉 (T ) = 0, i = 1, 2, 3

(9)

where P1 , P2 , and P3 are the adjoint variables. The optimal control can be found by deriving the Hamiltonian, taking the partial derivative of H with respect to u and equating it to zero, we get: u* =

(P3 − P1 )S K

(10)

Application of Artificial Intelligence to Control a Nonlinear SIR Model

27

where u* is the optimal control. Moreover, u* must be between 0 and 1, so we need to apply a saturation function: ( ( )) (P3 − P1 )S * u = max 0, min ,1 (11) K By substituting u* (t) in 2 and 8, we come by an optimality system as follows: ⎧ dS * ⎪ ⎪ dt = −aSI − Su ⎪ ⎪ dI ⎪ ⎪ ⎪ dt = aSI − rI ⎪ ⎪ dR * ⎪ ⎪ ⎨ dt = rI + Su d P1 * dt = (P1 − P2 )aI + (P1 − P3 )u ⎪ d P ⎪ 2 ⎪ ⎪ dt = −AI + (P1 − P2 )aS + (P2 − P3 )r ⎪ ⎪ d P3 ⎪ ⎪ ⎪ dt = 0 ( ( )) ⎪ ⎪ ⎩ u* = max 0, min (P3 −P1 )S , 1

(12)

K

Now that we have the optimal control u* (t) and the optimality system, we can use it to simulate the SIR model using the difference approximation method in MATLAB.

3.2

Numerical Simulation and Discussions

In this section, a combination of forward and backward difference approximation has been chosen as an approach to simulate the system. In this simulation, we assign the parameters the values provided in Table 1. We examine realistic hypothetical SIR epidemic model parameters, as real-world data is unavailable for the moment. We visualize the susceptible, infected, and recovered individuals with and devoid of control by assuming parameter numerical values of a population of 10, 001 individuals in 2. The time interval taken for this simulation is 40 days (see Table 2). The results from simulating the epidemic model presented in this study show interesting patterns regarding the potency of the control strategy obtained by the classic method. As illustrated in Fig. 1, the system stabilizes on its own without any control. However, Table 1 List of parameters for the SIR model

Parameter

Description

Value

a

Transmission rate

2 10−3 7

Ξ−1

r

Recovery rate

0.15

Ξ−1

Ξ−1 denotes the time unit, which can be expressed in either days or years

28 Table 2 List of realistic hypothetical values of the parameters for the simulation

O. Chayoukh and O. Zakary

Parameter

Description

Value

S0

Count of susceptible individuals at t = 0

104

I0

Count of infected individuals at t = 0

1

R0

Count of recovered individuals at t = 0

0

A

Equilibrium constant

100

K

Weight parameter

1052

the time it takes for the disease to vanish is slightly longer compared to the classically controlled system. This finding indicates that the system can reach a stable state without external intervention, but it may take more time. On the flip side, Fig. 5 shows that the optimality system solution emphasizes the importance of vaccinating individuals in high rates during the initial 10 days, with an exponential increase from 0 to a maximum rate of 1 at t = 1.88, followed by a gradual decrease after a period of days of maximum vaccination rates. In Fig. 3, the peak number of infected people is 614 individuals compared to 7928 individuals in the non-controlled strategy. In Fig. 2, we can clearly see how the number of susceptible individuals decreased. This control strategy is indeed a good one (Fig. 4 supports this claim). Interestingly, there exists a prominent change in the susceptible compartment with or devoid of control, as observed in Figs. 1 and 6. Figure 6 presents a curve that shows the evolution of the difference between the individuals of the uncontrolled and classically

Fig. 1 Relative comparison between the epidemic progression of the system with and devoid of the classic method’s control

Application of Artificial Intelligence to Control a Nonlinear SIR Model

29

Fig. 2 Relative comparison between the count of susceptible individuals over time with and devoid of the classic method’s control

Fig. 3 Relative comparison between the count of infected individuals over time with and devoid of the classic method’s control

30

O. Chayoukh and O. Zakary

Fig. 4 Relative comparison between the count of recovered individuals over time with and devoid of the classic method’s control

Fig. 5 The optimal control obtained by the classic method

Application of Artificial Intelligence to Control a Nonlinear SIR Model

31

Fig. 6 Evolution of the difference between the individuals of the uncontrolled and classically controlled system in each compartment over time

controlled system in each compartment by plotting the following functions: ⎧ * ⎪ ⎨ DS (t) = S (t) − S(t) DI (t) = I * (t) − I (t) ⎪ ⎩ DR (t) = R* (t) − R(t)

(13)

where S * (t), I * (t) and I * (t) represents the functions of the classically controlled system. The negative values in the infected and susceptible compartments demonstrate that the control strategy is fruitful, whereas the opposite should happen in the recovered compartment. As illustrated in Fig. 6, the curves indicate a good control strategy. Overall, these findings suggest that implementing control strategies, such as vaccination, can make a substantial difference in limiting the disease’s propagation, especially during the early stages. However, the potency of the optimal control strategy given by the classical numerical methods is questionable let alone the hypothetical aspect of the parameters given. The next study investigates the optimal control strategy with artificial intelligence under the same scenarios.

32

O. Chayoukh and O. Zakary

4

Optimal Control by a Trained ANN

4.1

Building and Training the ANN

In this study, we put forward the application of an ANN as a means of optimizing the vaccination rate in the management of infectious diseases. More specifically, we propose the utilization of a feedforward simple Multilayer Perceptron (MLP) which consists of 10 neurons in one hidden layer, one input, and one output (Fig. 7). The choice of a multilayer architecture of one hidden layer is based on the fact that it is relatively simple and easy to train. Additionally, the use of 10 neurons in the hidden layer is aimed at achieving a balance between model complexity and accuracy and it has been selected through a process of trial and error. The input of the network is the count of individuals at a given time. The output of the network is the vaccination rate for the given number of infected individuals. By optimizing the vaccination rate using this technique, we hope to diminish significantly I (t) over time and halt the transmission of the disease swiftly. We utilized MATLAB’s Deep Learning Toolbox to create this neural network model, leveraging its user-friendly interface and built-in training algorithms. The toolbox allowed Fig. 7 Neural network diagram with a single–input–single–output (SISO) and a hidden layer of 10 neurons with labeled biases and weights

Application of Artificial Intelligence to Control a Nonlinear SIR Model

33

us to define the network architecture, optimize training parameters, and visualize the network’s performance. To train the proposed ANN, we will utilize the widely adopted algorithm of Levenberg–Marquardt to minimize the mean squared error (MSE) function using the trainlm function. This algorithm has been proven to be highly effective in achieving optimal learning and is often preferred due to its computational efficiency. It is worth noting that the outcomes of this analysis will be juxtaposed against the results presented in the previous subsection, thereby providing a comprehensive evaluation of the proposed ANN. Due to the unavailability of real-world datasets at the present moment, it is necessary to resort to simulated data in order to carry out the training process of the proposed simple multilayer perceptron feedforward ANN. The simulation process involves the application of classic methods, repeatedly and with different similar hypothetical parameters that has been selected carefully, to generate diverse and realistic datasets that can be considered as representative of real-world scenarios. It is worth noting that the quality and consistency of the simulated data must be carefully monitored, given the dependence of the ANN’s performance on the accuracy of the training data. Thus, a rigorous process of verification and validation will be implemented to ensure the consistency of the distribution and number of data points in relation to the two dependent variables of interest, namely, the vaccination rate and the number of infected individuals.

4.2

Numerical Results and Discussions

In this subsection, we present and discuss the numerical results obtained by the training of the neural network. We aim to compare the results to the results obtained through the classic method. Finally, evaluate the efficiency of neural networks in research of optimal control. In contrast, the ANN gave a different-shaped control that was superior to the classic method, as shown in Fig. 8. This control had a minimum of 0.39 and a maximum of 0.6. This control strategy suggests a medium vaccination rate in the early beginnings with an increase of 50% directly afterwards. Similar to the classic method, these increases lasted around 10 days then went back to 0.39 but with a faster pace. The control reached the maximum rate faster than the classic method. There is also a small bump in the maximum platform around first days. However, the neural network was observed not to produce any null values compared to the traditional method. This artificially learned optimal control was shown to outperform the classic method, as demonstrated in Fig. 9. In Fig. 11, the count of individuals reached a maximum of 133 individuals, which is nearly more than the quarter of the count of individuals reached by the classic method. This significant reduction in the count of individuals was indicative of a better performance and a better minimization of the objective functional.

34

O. Chayoukh and O. Zakary

Fig. 8 Relative comparison between the optimal control obtained by the ANN and the one obtained by the classic method

Fig. 9 Relative comparison between the epidemic progressions of the systems issued by the optimal control obtained by the well-trained ANN, the one obtained by the classic method and no control

Application of Artificial Intelligence to Control a Nonlinear SIR Model

35

Furthermore, it is worth noting that the number of recovered individuals increases at a faster rate in the artificially learned optimal control compared to the previous controlled and uncontrolled systems as shown in Fig. 12. On the other hand, in Figs. 13 and 10, the number of susceptible individuals decreased drastically for the first 5 days before dropping at a slightly slower pace to 0 compared to the previous systems. Parallel to that, we can observe that this strategy not only achieves a faster reduction in the number of infected individuals but also ensures a larger number of individuals recover in the initial days. By prioritizing the vaccination of individuals during the early stages of the epidemic, the control strategy effectively mitigates the impact of infected individuals on the overall population. This suggests better control of the epidemic spread and a more efficient use of the available resources. The success of ANNs in nonlinear control systems has raised questions about the effectiveness of classic control methods. However, the failure of classic methods to achieve optimal control is not due to the inadequacy of the Pontryagin’s minimum principle, a well-established mathematical tool in control theory. Instead, the given performance of classic methods is often attributed to the choice of the objective functional and the step size of the difference approximation used in numerical methods. ANNs, on the other hand, have been successful in learning the underlying patterns of nonlinear systems through repetitive exposure and adaptation to new data, leading to optimal control. The strength of ANNs lies in their ability to adapt to nonlinear and complex systems, making them a promising tool in the specialized domain of nonlinear control systems.

Fig. 10 Relative comparison between the artificially controlled count of susceptible individuals, the traditionally controlled one and the uncontrolled one over time

36

O. Chayoukh and O. Zakary

Fig. 11 Relative comparison between the artificially controlled count of infected individuals, the traditionally controlled one and the uncontrolled one over time

Fig. 12 Relative comparison between the artificially controlled count of recovered individuals, the traditionally controlled one and the uncontrolled one over time

Application of Artificial Intelligence to Control a Nonlinear SIR Model

37

Fig. 13 Evolution of the difference between the individuals of the artificially and classically controlled system in each compartment over time

Table 3 Performance table of the ANN

Unit

Initial value

Stopped value

Elapsed time

–

00:00:07

MSE

1.34

0.002

These findings highlight the efficiency of the neural network method in the research of optimal control. The performance Table 3 of each training was also analyzed to highlight the efficiency of this kind of method. The training of the ANN for the specific task at hand demonstrated remarkable efficiency, completing within a short duration of approximately 7 s. Despite this relatively brief training time, the results achieved were highly promising. The MSE reached an impressively low value of 0.002. This indicates that the ANN was able to accurately approximate the underlying nonlinear relationships within the system, capturing intricate patterns and dynamics. Such a small MSE value is widely considered as an indicator of good performance, suggesting that the ANN successfully learned and generalized from the available data to produce accurate predictions or controls. The rapid training process and the achievement of such a low MSE value collectively underscore the effectiveness and potential of ANN-based approaches in addressing complex problems, further highlighting their value as a powerful tool in control and prediction tasks.

38

5

O. Chayoukh and O. Zakary

Conclusion

The study presented in this article provides a detailed examination of the application of artificial intelligence in designing an optimal nonlinear SIR model system. One of the key findings of this study was the demonstration of the efficiency of neural networks in learning an optimal control strategy and providing faster performance than traditional methods. The ANN was found to outperform traditional methods in terms of reducing the number of infected individuals. This highlights the potential of neural networks in developing more potent strategies for disease control and prevention. The success of the neural network in controlling infectious diseases is due to its ability to learn and adapt from the given data. The ANN is able to process substantial volumes of data and find patterns that can be used to make accurate predictions and decisions. The study demonstrates how the neural network can be trained to learn from the data and find an optimal control strategy that leads to better outcomes. However, the study also recognizes the limitations of using neural networks in controlling infectious diseases. The need for larger datasets to obtain even better results with neural networks is emphasized. Training on larger datasets is necessary to increase the accuracy and precision of the resulting model. In conclusion, this article contributes significantly to our understanding of the potential of ANNs in optimizing nonlinear control SIR systems. The findings of this study suggest that neural networks can play a critical role in improving disease control and other important applications in various fields. The study also emphasizes the need for further research in this area to fully realize the potential of neural networks in disease control and prevention. With continued research and development, it is likely that neural networks will continue to play an increasingly important role in various fields and applications.

References 1. Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall Series in Artificial Intelligence. 2. Zakary, O., Rachik, M., & Elmouki, I. (2017). A multi-regional epidemic model for controlling the spread of ebola: Awareness, treatment, and travel-blocking optimal control approaches. Mathematical Methods in the Applied Sciences, 40(4), 1265–1279. https://doi.org/10.1002/mma. 4048 3. Lhous, M., Zakary, O., Rachik, M., Magri, E. M., & Tridane, A. (2020). Optimal containment control strategy of the second phase of the covid-19 lockdown in morocco. Applied Sciences, 10(21). https://doi.org/10.3390/app10217559 4. Zakary, O., Bidah, S., Rachik, M., & Ferjouchia, H. (2020). Mathematical model to estimate and predict the covid-19 infections in morocco: Optimal control strategy. Journal of Applied Mathematics, 2020, 9813926. https://doi.org/10.1155/2020/9813926

Application of Artificial Intelligence to Control a Nonlinear SIR Model

39

5. Ibrahim, F., Hattaf, K., Rihan, F. A., et al. (2017). Numerical method based on extended onestep schemes for optimal control problem with time-lags. International Journal of Dynamics and Control, 5(4), 1172–1181. https://doi.org/10.1007/s40435-016-0270-x 6. Yousfi, N., Hattaf, K., & Tridane, A. (2011). Modeling the adaptive immune response in hbv infection. Journal of Mathematical Biology, 63(5), 933–957. https://doi.org/10.1007/s00285010-0397-x 7. Laarabi, H., Abta, A., & Hattaf, K. (2015). Optimal control of a delayed sirs epidemic model with vaccination and treatment. Acta Biotheoretica, 63(2), 87–97. https://doi.org/10.1007/s10 441-015-9244-1 8. Hattaf, K., Lashari, A., Louartassi, Y., & Yousfi, N. (2013). A delayed sir epidemic model with general incidence rate. Electronic Journal of Qualitative Theory of Differential Equations, (3), 1–9. https://doi.org/10.14232/ejqtde.2013.1.3 9. Laarabi, H., Labriji, E., Rachik, M., & Kaddar, A. (2012). Optimal control of an epidemic model with a saturated incidence rate. Nonlinear Analysis. Modelling and Control. https://doi.org/10. 15388/NA.17.4.14050 10. Hattaf, K., & Yousfi, N. (2012). Optimal control of a delayed hiv infection model with immune response using an efficient numerical method. ISRN Biomathematics, 2012, 215124. https://doi. org/10.5402/2012/215124 11. Sundarapandian, V., & Volos, C. (Eds.). (2016). Advances and applications in nonlinear control systems (1st ed., p. 635). Studies in Computational Intelligence. 12. Bhargava, C., & Sharma, P. K. (2021). Artificial intelligence: Fundamentals and applications (1st ed.). 13. Arreguin, J. M. R. (2008). Automation and robotics. 14. Ibrahem, I. M. A. (2020). A nonlinear neural network-based model predictive control for industrial gas turbine. PhD thesis, Ecole de technologie supérieure. 15. Thon, C., Finke, B., Kwade, A., & Schilde, C. (2021). Artificial intelligence in process engineering. Advanced Intelligent Systems, 3(6). https://doi.org/10.1002/aisy.202000261 16. Slotine, J.-J. E., & Li, W. (1987). Nonlinear control of robotic manipulators using neural networks: Theory and experiment. International Journal of Robotics Research. 17. Suykens, J. A., & Vandewalle, J. (1993). Application of artificial neural networks in power system stabilizer design. IEEE Transactions on Power Systems. 18. Chen, W., & Ma, G. (1997). Nonlinear control of a chemical process using neural networks. Computers & Chemical Engineering. 19. Fleming, W. H., & Rishel, R. W. (1975). Deterministic and stochastic optimal control. Springer. 20. Lukes, D. L. (1982). Differential equations: Classical to controlled. In Mathematics in science and engineering (Vol. 162). Academic Press. 21. Pontryagin, L. S. (1987). Mathematical theory of optimal processes. Classics of Soviet Mathematics.

Computer Vision with Deep Learning for Human Activity Recognition: Features Representation Laila El Haddad, Mostafa Hanoune, and Abdelaziz Ettaoufik

Abstract

Deep learning (DL) using artificial neural networks has made remarkable progress, fueled by the utilization of powerful GPUs and the availability of copious online data. This advancement has led to computers becoming highly intelligent across various fields, with computer vision being a prominent area of research and development (R&D). Specifically, Human activity recognition plays a pivotal role in various applications, including healthcare monitoring, surveillance and security systems, and human–machine interfaces. However, challenges persist in unconstrained environments, including occlusions, variations in clothing, and background noise, making these tasks difficult to solve. This review article offers a succinct examination of deep learning algorithms, with a specific emphasis on convolutional neural networks (CNNs), which have been suggested as a solution to classical artificial intelligence problems. Furthermore, the paper delves into the notable outcomes and contributions of various methodologies explored in human activity classification through the utilization of DL techniques. In conclusion, the paper emphasizes the potential of a hybrid approach that combines convolutional and recurrent neural networks in future solutions for human action/activity recognition. By combining the strengths of CNNs L. E. Haddad (B) · M. Hanoune · A. Ettaoufik LTIM, Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, Morocco e-mail: [email protected] M. Hanoune e-mail: [email protected] A. Ettaoufik e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_3

41

42

L. E. Haddad et al.

in extracting spatial features and RNNs in capturing temporal dependencies, the hybrid CNN-RNN models hold promise in effectively analyzing video data, leading to improved accuracy in classifying human activities. Ongoing research aims to further enhance these hybrid models to tackle the challenges of unconstrained environments and advance the human activity recognition field. Keywords

Computer vision . Deep learning . CNN . Human activity . Feature representation

1

Introduction

Human Activity Recognition is a rapidly advancing research field driven by its potential applications in various human-centric areas such as human–computer interaction, sports, elder care, healthcare, smart homes, and abnormal activity monitoring [1]. One of the key technologies used in HAR is computer vision, which entails the use of algorithms and techniques to analyze, understand, and synthesize images and videos. By using computer vision, machines can recognize and interpret visual content as well as human. In a literature review [2] focusing on the period between 2010 and 2018, the research trend of deep learning (DL) in computer vision (CV) was examined. More than 100 research papers related to DL were analyzed, revealing that object classification, detection, recognition, and human pose estimation were the most prominent areas of DL-CV R&D during that timeframe. These areas garnered significant attention and were at the forefront of advancements in DL for CV. The utilization of DL techniques in computer vision has greatly impacted the field of HAR. DL models have demonstrated remarkable capabilities in recognizing and understanding human activities, leading to improved performance and accuracy in HAR applications. The ongoing research and development in DL-CV for HAR aim to further enhance the accuracy, robustness, and real-time capabilities of activity recognition systems, fostering their practical deployment in real-world scenarios. Deep learning algorithms are part of the broader spectrum of machine learning techniques aimed at overcoming the constraints of conventional techniques. In conventional machine learning, experts manually select features to analyze data, making patterns more apparent. However, deep learning algorithms possess the capability to autonomously learn and extract highly detailed features, reducing the reliance on manual feature engineering. Deep learning is a component of the wider realm of artificial intelligence. It employs artificial neural networks (ANNs) that simulate the interconnected neurons found in the human brain. ANNs, in general, typically comprise three layers: the input layer, the output layer, and the hidden layers. However, deep neural networks extend this concept by incorporating multiple hidden layers situated between the input and output layers.

Computer Vision with Deep Learning for Human Activity Recognition …

43

Traditional computer vision algorithms struggle with the analysis of large datasets, which hinders their efficiency. However, recent advancements in deep learning using artificial neural networks have highlighted the significance of leveraging GPU processing power. By harnessing the capabilities of GPUs, deep learning algorithms offer effective solutions to complex problems that cannot be adequately addressed by traditional methods. This has opened up new application opportunities in various domains that require advanced data analysis. This paper is structured in the following manner: Sect. 2 delivers a concise overview of deep learning (DL) and introduces a literature review of its fundamental algorithmic approaches in computer vision. It covers the commonly used deep architectures with convolutional structures, including a review of three well-known CNN architectures: AlexNet, VGGNet, and ResNet, highlighting their differences. Section 3 discusses existing activities and research concerning the application of deep learning algorithms, specifically CNNs in computer vision methodologies. It explores their application in various emerging technologies, such as gesture recognition, action and activity recognition, motion prediction, face recognition, object detection, and pose estimation. The focus is on examining how these approaches contribute to achieving better performance in these areas. Finally, in Sect. 4, the paper concludes by presenting future perspectives for the development of a human action/activity recognition system. It outlines the potential directions and advancements that can be pursued in this field.

2

Review of the Literature

In 1943, Warren McCulloch and Walter Pitts, mathematicians and neuroscientists, made a significant breakthrough in the field of artificial neural networks [3]. They introduced the concept of programming artificial neurons, inspired by the functioning of biological neurons known as the McCulloch-Pitts neuron (MCP). This pioneering work laid the foundation for the development of artificial neural networks, marking an important milestone in the field. Deep learning encompasses neural networks, hierarchical probabilistic models, and various learning algorithms: Supervised and unsupervised. Supervised learning trains algorithms to associate inputs with corresponding outputs based on labeled examples. Unsupervised learning discovers patterns in unlabeled data, revealing previously unknown information. Key deep learning algorithms encompass Deep Neural Networks, Recurrent Neural Networks, Deep Belief Networks, and Convolutional Neural Networks. These algorithms find utility across diverse applications, contingent upon distinct data types with specific requisites and performance attributes. A comparison of deep learning algorithms is conducted based on their inputs, outputs, and fundamental operations. According to [4], CNN and DBN are commonly employed

44

L. E. Haddad et al.

algorithms that are particularly well-suited for image data. CNN, in particular, can significantly reduce computation through the use of filters and achieve the desired outcomes efficiently. Refer to Table 1 for further details. In recent times, DL methods have brought about a revolution in the domain of computer vision, especially since 2012. This transformation can be attributed to two key factors: the availability of large publicly labeled image datasets, such as ImageNet, containing millions of images [5], and the adoption of parallel GPU computing for accelerated training of deep models, replacing CPU-based approaches. The publication of the AlexNet convolutional neural network models played a pivotal role in raising awareness of the immense potential of deep learning methods. Subsequently, CNNs have found widespread application in various computer vision-related domains. Notable applications include medical image analysis, robot navigation, and image captioning [4]. These advancements have significantly improved the capabilities and performance of computer vision systems, opening up new possibilities for solving complex visual tasks.

2.1

Convolutional Neural Network (CNN)

The Convolutional neural networks, also known as ConvNets, are widely recognized as the most prominent deep learning models for solving computer vision tasks. They excel in tasks like classification, object detection and recognition, as well as human pose estimation [6, 7]. Convolutional neural networks (CNNs) conventionally comprise three essential layers: convolutional, pooling, and fully connected. Figure 1 provides a visual depiction of these layers. The convolution layer serves as the initial layer and is responsible for extracting features from the input image. It utilizes filters or feature extractors to identify relevant visual patterns. Non-linearity is achieved through the application of an activation function within the convolutional layer, generating an output based on the input. Common activation functions employed in CNNs include ReLu, Sigmoïd, and tanh, among others. Among these, ReLu is widely used in CNN architectures. – Sigmoid function: f (x) =

– Hyperbolic tangent function:

1 1 + e−x

Deep Belief Networks (DBN) [7]

Unsupervised neural network belonging to the energy-based model

Restricted Boltzmann Machines (RBM) [7] Unsupervised

Learning type

A network model Supervised constructed by stacking a number of layers of unsupervised Restricted Boltzmann Machines (RBM)

Are two-layer shallow neural networks that form the building blocks of deep belief networks

Algorithm description

Parameter

Table 1 Comparison of deep learning algorithms

Mainly to recognize, aggregate, generate images, video sequences and motion capture data

Classification problems and reduction of dimensiality

Pattern recognition and recommendation engine feature extraction

Main task (application)

Text and image

All types of data

Input data

Classified and planned release

Reconstructed entrance

Output

Can work even with a small labelled dataset

The learning algorithm can efficiently utilize vast unlabelled data for pre-training in a fully unsupervised manner

Advantage

(continued)

High computation time and time required to learn the model

Calculating the energy gradient function during training is very difficult

Disadvantage

Computer Vision with Deep Learning for Human Activity Recognition … 45

Auto-encoders [14]

Parameter

The encoder condenses the input data into a latent space representation, which can subsequently be reconstructed to recover the original input

Algorithm that converts multidimensional data into low-dimensional data

It employs a layer-by-layer method to acquire generative weights and utilizes top-down approaches throughout the learning process

Algorithm description

Table 1 (continued)

Unsupervised

Learning type

Input data

Image coloring, image compression and denoising

Used in the health All types industry for of data medical imaging (breast cancer detection)

Main task (application)

Reconstructed entrance

Output

Utilizing multiple encoder and decoder layers helps reduce, to a certain degree, the computational cost associated with representing specific functions

Ensures robustness in classification against various factors such as viewing angle, size, position, color, and more

Advantage

(continued)

May lose important data from the original input after encoding

Disadvantage

46 L. E. Haddad et al.

Convolutional neural networks (CNN) [14]

Parameter

Recommendation system

Three primary layer types are utilized: convolutional (with filter sets), pooling (for subsampling), and fully connected (comprising neurons)

Main task (application)

Main use cases in image recognition and object detection tasks.

Learning type

ConvNet is a Supervised popular deep learning algorithm that has hidden layers that perform convolutions

The decoder’s objective is to reconstruct the code back to its initial form, although it might not achieve perfect fidelity to the original and could incur some losses

Algorithm description

Table 1 (continued)

3D structured data, such as voice, images

Input data

Classified and planned release

Output

In image and object recognition applications, the results tend to be notably more accurate when compared to other machine learning algorithms

Advantage

(continued)

To drive ConvNet, very high computing power is required. So not very cost effective

Disadvantage

Computer Vision with Deep Learning for Human Activity Recognition … 47

Recurrent neural networks (RNN) [27]

Parameter

Time series forecasting

Primarily employed in the domains of natural language processing and speech recognition

Use regularization techniques such as drop out where a particular node or connection is ignored

Uses sequential Supervised or time series data

Main task (application) Natural Language Processing Tasks

Learning type

The image is progressively transformed layer by layer, starting from the initial pixel values and culminating in the computation of final class scores

Algorithm description

Table 1 (continued)

Mainly textual data

Input data

Sequence prediction

Output

The capacity to retain information throughout the training period plays a crucial role in predicting time series data

Advantage

(continued)

The calculation is time consuming because of its recurrent nature

Disadvantage

48 L. E. Haddad et al.

Long Short Term Memory Networks (LSTM) [27]

Parameter

A special type of RNN which excels at capturing and learning long-term dependencies in data

An output is generated and duplicated, then circulated back into the network as a loop through internal memory

Allow the processing of sequences of variable (or even infinite) length

Enable the incorporation of previous outputs as inputs while maintaining hidden states

Algorithm description

Table 1 (continued)

Supervised

Learning type

Ideal for tasks such as semi-automatic sentence input, subtitle generation and video image analysis

Ideal for tasks such as semi-automatic word entry, text recognition and video image analysis

Main task (application)

Mainly textual data.

Input data

Sequence prediction

Output

Can handle information in memory for a long period of time compared to RNN

Ability to process an entry of any length

The model’s size remains constant regardless of variations in input size

Advantage

(continued)

Training the model demands substantial computations and resource allocation

Difficulty in accessing information from long ago

Disadvantage

Computer Vision with Deep Learning for Human Activity Recognition … 49

Parameter

Modifications to the memory blocks are executed through components known as gates

The network is composed of distinct memory blocks referred to as cells, which have the capacity to retain and store information over time

Algorithm description

Table 1 (continued) Learning type Detecting anomalies in network traffic data

Main task (application)

Input data

Output

Advantage Subject to over-adjustment

Disadvantage

50 L. E. Haddad et al.

Computer Vision with Deep Learning for Human Activity Recognition …

51

Fig. 1 Architecture of a CNN network. Figure taken from [8]

f (x) = tanh(x) =

e x − e−x e x + e−x

– ReLu function: f (x) = max(x, 0) The Pooling Layer, situated as the second tier within a convolutional neural network (CNN), serves to diminish parameter counts by downsizing feature maps. Among the frequently employed pooling methods, Max Pooling stands out, as it identifies the most salient element within each feature map. After the pooling process, the resulting feature map is transformed into a one-dimensional vector by flattening it and then passed into a fully connected network for the purpose of classification. Through backpropagation, the network learns to optimize its parameters and make accurate classifications based on the extracted features. We will briefly review the details of the three famous CNN architectures and how they differ from each other: AlexNet [5] was developed to enhance performance in the ImageNet challenge, achieving a significant accuracy of 84.7% compared to the second-place result of 73.8%. This pioneering deep convolutional network utilized convolutional layers and receptive fields to analyze spatial correlations in visual environments. It comprises five convolutional layers followed by three fully connected layers (FC), employing ReLU as activation functions. The network takes input in the form of 227 × 227 × 3 RGB images and produces a 1000 × 1 probability vector for class prediction. VGGNet [9], on the other hand, aimed to reduce parameters in convolutional layers and improve training efficiency. Variants such as VGG16 and VGG19 diverge in the total count

52

L. E. Haddad et al.

Fig. 2 Residual learning: constructing unit. Figure taken from [10]

of layers while sharing the same structural principles. For instance, VGG16 employs 138 million parameters and utilizes 3 × 3 convolutional kernels and 2 × 2 maxpool kernels with a stride of two. Further details about the architecture of VGG16 can be found in [10]. ResNet [10] is an architecture that tackles the vanishing gradient problem by incorporating shortcut connections (Fig. 2). The cornerstone of ResNet architecture is the residual block, which is repeatedly used throughout the network. ResNet addresses the issue by introducing two types of shortcuts: identity shortcuts and projection shortcuts. Different versions of ResNet, denoted as ResNetXX, exist with varying numbers of layers. The commonly utilized variants include ResNet50 and ResNet101. In [9], ResNet18 is reported to have approximately 11 million trainable parameters. The architecture includes Convolutional layers with 3 × 3 filters, similar to VGGNet. The network utilizes only two pooling layers across its entire architecture, positioned at the beginning and end. Identity connections connect all CONV layers. Solid arrows represent identity shortcuts with matching input and output sizes, while dotted arrows indicate projection connections with differing sizes. In Table 2, these three CNNs are sorted according to their accuracy in the top 5 of the ImageNet data set. Additionally, information about the count of trainable parameters and the floating-point operations (FLOPs) needed for a forward pass is included.

Table 2 Comparative table of CNN applications according to their accuracy in the ImagNet top 5 Year

Network

Developed by

Accuracy top 5 (%)

Top 5 error (%)

Parameters (M)

FLOP (B)

2012

AlexNet(7) [9]

Alex Krizhevski, Geoffrey Hinton, Ilya Sutskever

84.70

15.3

62

1.5

2014

VGGNet(16) [9]

Simonyan, Zisserman

92.30

7.3

138

19.6

2015

ResNet(152) [10]

Kaming He

95.51

3.57

60.3

11

Computer Vision with Deep Learning for Human Activity Recognition …

3

53

Results from Various Studied Approaches for Modeling Human Data

Human activity recognition focuses on analyzing activities depicted in video or image sequences. The primary goal of HAR systems is to precisely categorize the input data into distinct activity classes. These categories encompass various levels of complexity, including gestures, actions, activities, and events. Refer to Fig. 3 for visualization. Gestures are basic movements of specific body parts that correspond to particular actions performed by an individual. Actions refer to simple bodily movements like walking, clapping, or running. Activities involve short and meaningful movements, such as talking on the phone or drinking tea. Events encompass high-level activities that describe social interactions between individuals, indicating social intentions or roles, such as a football match or a birthday party. Human activity recognition (HAR) has experienced notable growth and attention in recent years, owing to its extensive array of applications. These applications span various domains, including Human–Computer Interaction (HCI) [11], medical diagnostics, elderly care, video summarization, and intelligent monitoring and control systems [1]. Researchers have made significant efforts in recent years to address the demands of human activity recognition (HAR). State-of-the-art HAR approaches can be classified into two main categories: the classical approach, which relies on hand-crafted methods requiring domain experts to design optimal features for extracting and representing information, followed by the use of classical classifiers like SVM or RVM; and the deep learning approach, which has gained increasing interest in computer vision due to its powerful capabilities, utilizing techniques such as CNN, DNN, and RNN. This paper centers its attention on architectures within the realm of deep learning for classifying human gestures, activities, and actions. The process of feature extraction and representation plays a pivotal role not only in the field of HAR but also in various other computer vision tasks. The forms of representations can be sorted into three primary categories: (a)

Fig. 3 The decomposition of human activities according to their complexity

54

L. E. Haddad et al.

global representation, such as space–time volume (STV) methods, which leverage spatiotemporal information and provide a suitable means to combine spatial, temporal, and frequency-based information; (b) local representations, such as depth maps, which offer more informative data with additional coordinates compared to conventional color images; and skeletal-based representations, which showcase the positions of human joints. Additionally, (c) Representations of the human body using modeling can indeed be categorized into three primary subcategories: simple geometric shapes or “blobs” modeling, methods centered on 2D models, and approaches relying on 3D models. Each of these approaches has its own advantages and applications in various fields like computer graphics, medical imaging, and animation. Existing approaches for human activity recognition often lack richer contextual information beyond just the presence of people. This includes details about the scene where the activity occurs and the location of individuals within the scene. In their work [12], the researchers propose a deep neural network (DNN) model that leverages probabilistic reasoning and incorporates multiple sources of contextual information to recognize human activity. This model combines motion information, encoded through low-level spatio-temporal motion features like HOG and HOF, with high-level mobility features. Contextual information, representing the scene and human interactions, is also incorporated. Scene features capture global and local scene attributes, while group information focuses on human interactions. The abstracted features are processed through a dense and fully connected hidden layer. As depicted in Fig. 4, the output units from these two components are merged and then propagated through another fully connected network that includes a softmax layer. This final step computes the probability of detecting an activity based on the input observations. This approach was evaluated using two collective activity databases. Activity 5 consists of 5 activities (crossing, waiting, queuing, walking, talking) and activity 6 contains 2 more activities (dancing and jogging). The findings demonstrate that the suggested approach surpasses the current state-of-the-art method when applied to these datasets. This highlights the potential of deep models to systematically integrate multiple contextual sources and synthesize higher-level representations of raw input features through integration of multi-level structures, probabilistic inference capabilities, and integration of hidden units. Deep learning has gained significant attention for its remarkable capabilities in feature extraction and classification, particularly in object and pattern recognition tasks. However, this approach has limitations, such as the substantial data requirements, typically needing thousands of labeled samples, and the high computational cost associated with training from scratch, which can take weeks. In addressing these challenges, Researchers have investigated human action recognition in the realm of computer vision, employing transfer learning techniquesas presented in [13, 14]. Drawing inspiration from human learning abilities, which allow us to acquire knowledge from a few samples and accumulate it over time, these methods leverage transfer of information to learn new objects while minimizing data and computational requirements.

Computer Vision with Deep Learning for Human Activity Recognition …

55

Fig. 4 Overview of the neural network model proposed in [13]

In [13], the authors assert that deep learning models trained on particular data can be adapted for use with new datasets, enabling the adaptation of pre-trained neural network models to new tasks. Two approaches exist for implementing transfer learning with deep neural networks. The initial approach is using a pre-trained network, keep the weights gathered by all but the last three layers for feature representation, and subsequently utilizing a generic classifier. The second approach maintains the pre-trained network but updates its weights by training it with a new dataset. The suggested approach pertains to the second methodology and explores the potential of transfer learning with a pretrained AlexNet neural network, necessitating input data dimensions of 227 × 277 × 3. Hence, video data needs to undergo preprocessing before being input into the pre-trained AlexNet model. For video data, three different global representation models are processed: – Motion History Image (MHI) is a method of representing temporal information from video sequences as a 2D image, capturing the spatio-temporal characteristics of an action. – Motion Energy Image (MEI) is a method that represents regions of motion by identifying the locations where movement has taken place within a single binary model. It provides a visual depiction of motion within an image or video. – Optical flow is a technique used to describe the speed of individual pixel points within an image or frame. It is implemented using the Lucas-Kanade method, which estimates the distribution of apparent motion in the scene. To assess the system’s performance, two publicly available databases of human actions are utilized: KTH and Weizman. The KTH database comprises 6 categories of human actions performed by 25 users, while the Weizman database consists of 10 classes of human actions with 90 videos executed by 9 users. The outcomes demonstrate encouraging performance of the human action recognition system, with significant enhancements observed

56

L. E. Haddad et al.

in the training and validation accuracies in the early epochs. This can be attributed to the utilization of pre-trained front layers, which have the capability to acquire basic features like edges and patterns from the training data of the AlexNet model. Despite extensive research efforts, the task of pose estimation remains marked by substantial challenges [15]. One of the primary obstacles in pose estimation is effectively modeling the complexity of human joints. The study conducted in [16] illustrates that a diverse array of approaches has been employed to address the intricacies of human articulation, utilizing three sources of information: deformation, mixture type, and appearance score. These approaches utilize log-linear models and pairwise potentials derived from these sources of information in order to assess the accuracy of estimated joint positions. However, due to the lack of log-linear correlation among these sources, they often yield unreasonable outcomes, as illustrated in Fig. 5. To address this constraint, the suggested approach strives to create a nonlinear model that captures logical patterns of appearance score, mixture type, and deformation. It is inspired by the work of Yang and Ramanan [31] on the recognition of articulated human subjects using flexible mixtures of body parts. However, unlike the latter, which involves training a deep model on concatenated information sources, the proposed approach introduces a more effective strategy. This strategy involves building high-level representations individually for each information source before merging them [35]. To achieve this, an alternative architecture is employed. Each information source is connected to two layers, enabling the construction of a high-level representation specific

Fig. 5 Constructing non-linear representations from multiple sources using a deep model. Figure from [16]

Computer Vision with Deep Learning for Human Activity Recognition …

57

to that source. Subsequently, The integration of high-level representations from various information sources is accomplished through the incorporation of two supplementary layers, facilitating the process of pose estimation. This approach guarantees the establishment of high-level representations before their fusion, thereby addressing the challenge of blending information sources with distinct statistical attributes in the initial hidden layer. By adopting this alternative architecture, a holistic and more abstract representation, Acquired from the information source through the deep model, is attained. Concurrently, the local and less abstract representation remains preserved, resulting in a comprehensive comprehension of the pose estimation undertaking. This approach’s assessment is carried out using three datasets: PARSE, LSP, and UIUC people. In all experiments, the widely adopted criterion of the percentage of correctly located parts (PCP) is employed. The achieved outcomes demonstrate that the utilization of the deep model brings about a substantial enhancement in the accuracy of pose estimation. Furthermore, the deep model surpasses all existing state-of-the-art techniques, showcasing its superiority across these three datasets. Gestures refer to meaningful body movements involving the hands, head, body, etc., used to transmit information or interact with the environment. In [17], a system was developed to dynamically recognize hand gestures in real-time using a web camera. This system captures both spatial and temporal movements by utilizing motion history images (MHI). A feed-forward neural network with three hidden layers, each containing 100 neurons, is employed for gesture classification, employing a stochastic gradient-based optimizer. The experiments focused on four gestures (swipe left, swipe right, swipe down, shrink). The results highlight the significant impact of lighting conditions on gesture recognition accuracy, achieving a success rate of 85% under good lighting conditions and 71.3% under poor lighting conditions. Recent advancements in human action recognition have embraced the utilization of three-dimensional (3D) depth cameras, specifically RGB-D images. These images possess uniform color, invariant illumination, and depth information, effectively reducing the uncertainty associated with Human movement. In [18], a computer vision model based on DL algorithms was developed to recognize human physical activities using skeletal data captured by the Microsoft Kinect sensor. The model incorporates a CNN for feature extraction, followed by a multilayer perceptron serving as a classifier. To train and evaluate the model, The model utilizes the CAD-60 dataset from Cornell University, which contains information about the skeleton of 15 human body joints. The 3D coordinates of these 15 joints are derived from absolute 3D coordinates using the Kinect camera as the reference point. The model achieved an accuracy of 81% in identifying twelve activity categories. Building on this work, The authors in [19] proposed a new method called action fusion to detect human actions. The method uses a three-channel deep CNN model for two types of data sequences: depth maps and pose data. Each input sequence is converted into a descriptor representing the input as an image. Specifically, Depth maps undergo a transformation into Depth Motion Images (DMI), whereas body postures are translated

58

L. E. Haddad et al.

into Moving Joint Descriptors (MJD). The first pass uses DMI for training, the second pass integrates DMI and motion joint descriptors, and the third pass uses only motion joint descriptors. The action prediction results from the three CNN channels are then combined for final action classification. To measure the effectiveness of the technique, three publicly available datasets were used: the Microsoft 3D Action dataset (MSRAction3D), the Multimodal Human Action dataset from the University of Texas at Dallas, and the Multimodal Action Dataset (MAD). Skeleton-based human gesture recognition [36] is widely adopted due to its robustness against background and lighting variations. Sung et al. [20] introduce a real-time hand gesture recognition (HGR) system that operates on device-based data, utilizing a single RGB camera to detect predefined static gestures. The system comprises two components: a 3D hand skeleton tracker leveraging MediaPipe Hands and a gesture classifier based on a neural network consisting of three fully connected layers, each containing 50 neurons. The neural network classifier achieves an average recall rate of 87.9% across six classes of static gestures, namely OpenPalm, ClosedFist, PointingUp, Victory, ThumbUp, and ThumbDown. Besides global and local representation techniques, researchers have also proposed studies involving human modeling to extract more powerful and distinctive features. These methods encompass various approaches, such as fundamental blob modeling, techniques centered on 2D models, and strategies that depend on 3D models. The basic blob representation, being a model- independent approach, represents the human body using ellipses [21] or blobs [22]. Despite the impressive performance achieved by many existing models in gesture recognition, significant challenges persist in achieving effective and universally applicable outcomes. These challenges stem from factors such as the extensive range of gestures, varying lighting conditions, intricate backgrounds, and noise during data acquisition. The complexity further intensifies when recognizing dynamic gestures in videos, involving a combination of local and global elements. Dynamic gestures often necessitate a sequence of images centered on the arms and hands for accurate identification, emphasizing the need to learn distinct spatiotemporal features. Taking inspiration from the advancements in deep learning for image recognition, researchers have explored the application of DL techniques for human gesture/action recognition. For instance, a method described in [23] employs the P-CNN (Pose-based Convolutional Neural Network) approach, which presents a novel action descriptor grounded in human poses. By capturing the temporal traces of 2D body joints, a convolutional two-stream network combines appearance-based and CNN-based motion features extracted for each monitored body part, including the left hand, right hand, upper body, whole body, and whole image. The CNN feature extraction pipeline of the P-CNN approach is depicted in Fig. 6. Two separate convolutional neural networks (CNNs), each with a similar structure to AlexNet, capture appearance and motion information. The RGB patches employ the VGG-f network [24], which has undergone pre-training on the ImageNet ILSVRC-2012

Computer Vision with Deep Learning for Human Activity Recognition …

59

Fig. 6 Characteristics of P-CNN. Figure taken from [23]

challenge dataset, making it publicly available. On the other hand, the motion network introduced by [34] is utilized for optical flow patches. The network is pre-trained specifically for action detection in the YouTube UCF101 video dataset. The evaluation of the proposed approach on JHMDB-GT reveals that similar performance is observed among all anatomical regions of the human body (hands, upper body, entire body) along with the entire image. Moreover, combining these parts leads to a significant enhancement in performance. Furthermore, the incorporation of both appearance and flow features results in further improvements across all parts and their combination, thereby enhancing overall performance. Additionally, the approach suggests replacing Vgg-f CNN with ResNet-50 as an appearance-based descriptor, resulting in improved performance with IP-CNN (ResNet-50+OF). Furthermore, the approach investigates the impact of removing human pose information in action recognition, revealing the crucial role of pose information for reliable recognition. However, the computationally demanding nature of optical flow, particularly in the context of continuous video input, markedly diminishes the speed of the two-stream network model. Another approach involves the transformation of 2D filters into cubic filters, extending a 2D H × W filter to a 3D T × H × W filter. In a study by [25], a method known as SeST is introduced, employing a parallel fusion of two distinct networks (ResC3D and ConvLSTM). This design enables neurons to dynamically adjust the influence of these two networks, facilitating the acquisition of both local and global spatio-temporal features. As a result, the proposed network outperforms existing methodologies and achieves state-of-the-art performance across a variety of datasets, including SKIG, ChaLearn LAP IsoGD, and EgoGesture. Furthermore, hybridisation [37] between deep learning methods has several uses in the literature. According to many studies, convolutional neural networks, which are generally used for feature extraction, seem to be the best methods to combine with another method [26]. Recently, RNNs have shown promise in recognizing patterns characterized by temporal relationships. Unlike ANNs, RNNs excel in handling sequence data, such as contextual information in natural language processing. Gradients are essential for adjusting the neural network’s weights during the update process. If the weights are too small, the

60

L. E. Haddad et al.

gradients may vanish, hindering learning in the hidden layers close to the input. Conversely, if the weights are too large, the gradients may explode. Consequently, RNNs are time sensitive, lack long-term memory, and are affected by short-term memory. To overcome this challenge, LSTM and GRU emerged as solutions [27]. Originating from Hochreiter and Schmidhuber’s work in 1997 [32], Long Short-Term Memory (LSTM) has gained widespread acceptance and given rise to numerous variations. Unlike Conventional RNNs, LSTMs integrates input and output gates to address the challenge of gradient vanishing/exploding, rendering it adept at capturing prolonged information and proficient in handling lengthy text sequences. Conversely, Gated Recurrent Unit (GRU) shares input and output structural elements with regular RNNs, yet its internal configuration resembles that of LSTM. Both LSTM and GRU find extensive utility in natural language processing, speech synthesis, and speech recognition applications. There Various combinations of CNNs and RNNs have been employed in human activity recognition [28]. CNNs excel at extracting features from images, while deep recurrent neural networks, particularly those based on LSTM cells, are effective in processing sequential data. For instance, in a study [29], Researchers introduce a framework for Activity Recognition in Industrial Surveillance Videos. The continuous video is devided into significant segments, selected based on human salience features derived from a pre-trained CNN model (MobileNet) which was trained on the INRIA people dataset. Temporal features of activities in the image sequence are then extracted using the convolutional layers of a CNN-based FlowNet optical flow model. Lastly, a multi-layer LSTM is utilized for activity recognition, as literature suggests that LSTMs outperform other methods for learning time series data. Another approach described in [30] combines CNNs and RNNs for action recognition in videos. The authors employ the GoogLeNet architecture [33] for CNNs, followed by a multilayer LSTM cell. This study demonstrates that the Inception/Residual model enhances CNN performance, while multilayer LSTM architectures outperform single-layer LSTMs. Table 3 provides a summary of previous studies investigating methods for human activity recognition. The table focuses on various computer vision techniques used for feature extraction and representation. This comparison aims to highlight the strengths and weaknesses associated with each method.

4

Conclusion and Outlook

In this study, we provide an overview of deep learning-based methods for human activity recognition. As part of this analysis, we classify human activities into different categories based on their complexity, including gestures, actions, activities, and events. To effectively represent these activities, we need to employ a range of feature representation approaches that span from global to local representations. This classification helps us understand and organize the diverse approaches used in the field of HAR.

Feed-forward neural network with 3 hidden layers each containing 100 neurons

Movement History Image (MHI)

Dynamic hand gesture recognition [17]

Transfer learning by AlexNet pre-trained network

Multi-source DNN

– MHI: Movement History Image – MEI: Motion energy image – Optical flow

Recognizing human action [13]

DNN (combines motion and contexte information)

Model used

Human pose Mixture type, estimation [16] Apparence score and Deformation

Spatio-temporal (orientation gradient histograms (HOG) and optical flow histograms (HOF))

Recognize human activity by incorporating multiple sources of contextual information [12]

Global representation

Feature representation types

Reference and task

Feature representation categories

An internal dataset

LSP, PARSE and UIUC people

KTH and Weizman

Activity-5 and Activity-6

Dataset

Table 3 Summary of previous work, compared by type of feature representation

– Deep models have the potential to systematically incorporate multiple sources of context – Localization of actions – Effective representation of low-level functionality – Thorough examination of human motion

Advantage

– Gradually outdated – Susceptibility to noise and occlusions – Identifying intricate activities can be challenging – Parsing of features can result in low repeatability – There is a gap between high-level events and low-level features – Detection of the human body is frequently a necessary initial step – Optical flow displacement vectors are generated not only on the human bodies of interest but also on the unwanted background, which makes the algorithm imprecise, especially when dealing with multi-scale and large displacements (continued)

Limits

Computer Vision with Deep Learning for Human Activity Recognition … 61

Recognize human activity [18]

Local representation

Hand gesture recognition system [20]

Recognize human action [19]

Reference and task

Feature representation categories

Table 3 (continued)

CNN

Model used

CAD-60 of Cornell University

Dataset

Advantage

– Depth maps contain additional coordinates and are therefore more informative which – DMI (Depth motion 3-Channel CNN (Action MSRAction3D, alleviate the ambiguity image) with Depth Fusion) multimodal human of human movement map actions from the – MJD (Moving joint University of Texas at – RGB-D image form with uniform colours, descriptor) with body Dallas and the invariant in terms of posture Multimodal Action illumination Dataset (MAD) – Skeletal human pose Skeleton image of the Neural network model of – An internal dataset information is crucial hand with MediaPipe 3 fully connected layers containing 1882 for reliable action Hands of 50 neurons each short video clips for recognition 21 static gestures – An internal dataset containing 7307 images from 6478 users

Skeleton of the human body

Feature representation types

(continued)

– Depth imaging has a huge problem because after a few meters outdoors, we cannot get enough information from which to extract movement – The high cost of depth cameras

Limits

62 L. E. Haddad et al.

RGB image, optical flow (Combines the performance of local descriptors with the strength of the global representation of visual word bags)

Video frames (space–time feature)

Video frames (space–time feature)

Video frames

Recognizing human action and gesture [23]

Recognize human activity [25]

Recognize human activity [29]

Recognizing human action [30]

Representation based on human body modelling

Feature representation types

Reference and task

Feature representation categories

Table 3 (continued)

Hybridization between CNN (Inception_ Residual), multilayer LSTM

Hybridization between CNN (MobilNet, FlowNet) and the RNN (LSTM)

2-Stream 3D CNN – ResC3D to learn short space–time features – ConvLSTM to learn long space–time features

P-CNN (2-Stream 2D CNN)

Model used

UCF-11

UCF101, UCF50, HMDB51, YouTube, and Hollywood actions

SKIG, ChaLearn LAP IsoGD ET Ego Gesture

JHMDB-GT

Dataset

– Offers a more efficient and descriminating feature representation

Advantage

– There is a need for larger 3D training data sets – Low cost devices – 2D surface representations are most commonly used, as 3D point clouds and volumes are more difficult to manage

Limits

Computer Vision with Deep Learning for Human Activity Recognition … 63

64

L. E. Haddad et al.

DL algorithms, particularly CNNs, are well-suited for tasks involving large datasets. CNNs excel in performance by leveraging filter-based computations. Although CNNs typically come with high computational requirements, they can be optimized using GPUs to expedite training with large volumes of data. To mitigate this computational challenge, transfer learning has emerged as a valuable technique. Transfer learning involves utilizing pre-trained neural network models and fine-tuning them according to specific requirements. This approach allows researchers to overcome the disadvantages of high computational costs and leverage the benefits of pre-existing models in their own applications. Deep models, with their multi-level deep structure, probabilistic reasoning capability, and integration of hidden units, offer the potential to systematically incorporate various sources of context. These models can effectively synthesize representations of raw input features, such as global, local, and human body model-based representations. This ability to integrate context enhances their performance in tasks like human activity recognition. Building upon this research and leveraging the benefits of local feature representation, which exhibits robustness against background and lighting variations, our plan is to expand this investigation by creating a rapid and precise recognition system for categorizing human activities. The aim of this system is to comprehend the user’s intent by analyzing their body language, hand gestures, and facial expressions. This technology holds potential for diverse applications, including human–computer interaction, sign language translation, and virtual reality. Its wide range of use cases encompasses gaming, virtual and augmented reality experiences, as well as accessibility and assistive technologies. To build our system, we will utilize deep CNN for efficient feature extraction from images. Additionally, we will employ deep recurrent neural networks (DRNs) with multilayer LSTM cells, known for their proficiency in handling sequential data.

References 1. Harikrishnan, J., Sudarsan, A., Ajai, R. A. S., & Sadashiv, A. (2019). Vision-face recognition attendance monitoring system for surveillance using deep learning technology and computer vision. In 2019 international conference on vision towards emerging trends in communication and networking (ViTECoN). 2. Li, A. A. S., Trappey, A. J. C., Trappey, C. V., & Fan, C. Y. (2019). E-discover state-of-theart research trends of deep learning for computer vision. In IEEE international conference on systems, man and cybernetics (SMC) Bari, Italy. 3. McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 52, 115–133. 4. Shety, S. K., & Siddiqa, A. (2019, July). Deep learning and applications in computer vision. International Journal of Computer Sciences and Engineering, 7(7). E-ISSN: 2347-2693.

Computer Vision with Deep Learning for Human Activity Recognition …

65

5. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (NIPS) (pp. 1106–1114). 6. Nishani, E., & Ciço, B. (2017). Computer vision approaches based on deep learning and neural networks: Deep neural networks for video analysis of human pose estimation. In 2017 6th Mediterranean conference on embedded computing (MECO), 11–15 June 2017. 7. Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Journal of Physics Computational Intelligence and Neuroscience, 2018, 1–13. 8. O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., Riordan, D., & Walsh, J. (2020). Deep learning vs. traditional computer vision. In Advances in computer vision proceedings of the 2019 computer vision conference (CVC) (pp. 128–144). Springer Nature Switzerland AG. 9. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. In 2015 international conference on learning representations (ICLR). 10. He, K., Zhang, X., Ren, S., & Sun, J. (2016) Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR). 11. Elmagrouni, I., Ettaoufik, A., Aouad, S., & Maizate, A. (2021). Approach for improving user interface based on gesture recognition. In E3s web of conferences 297, 01030 (ICCSRE’2021). 12. Wei, L., & Shah, S. K. (2017). Human activity recognition using deep neural network with contextual information. In 12th international joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP N2017). 13. Zamri, N. N. M., Ling, G. F., Han, P. Y., & Yin, O. S. (2019). Vision-based human action recognition on pre-trained AlexNet. In 9th IEEE international conference on control system, computing and engineering (ICCSCE). 14. Deep, S., & Zheng, X. (2019). Leveraging CNN and transfer learning for vision-based human activity recognition. In 2019 29th international telecommunication networks and applications conference (ITNAC). 15. NeiliBoualia, S., & Amara, N. E. B. (2019). Pose-based human activity recognition: A review. In 2019 15th international wireless communications & mobile computing conference (IWCMC). 16. Ouyang, W., Chu, X., & Wang, X. (Département d’ingénierie éléctronique, Université chinoise de Hong Kong). (2014). Multi-source deep learning for human pose estimation. In 2014 IEEE conference on computer vision and pattern recognition. 17. Munasinghe, M. I. N. P. (2018). Dynamic hand gesture recognition using computer vision and neural networks. In 2018 3rd international conference for convergence in technology (I2CT) (pp. 1–5). IEEE. 18. Mo, L., Li, F., Zhu, Y., & Huang, A. (2016). Human physical activity recognition based on computer vision with deep learning model. In 2016 IEEE international instrumentation and measurement technology conference proceedings. 19. Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2018). Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Transactions on Systems Man and Cybernetics, PP(99). 20. Sung, G., Sokal, K., Uboweja, E., Bazarevsky, V., Baccash, J., Bazavan, E., Chang, C.-L., & Grundmann, M. (2021). On-device real-time hand gesture recognition. 21. Nakazawa, A., Kato, H., & Inokuchi, S. (1998). Human tracking using distributed vision systems. In Proceedings of the Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170). 22. Yang, J., Cheng, J., & Lu, H. (2009). Human activity recognition based on the blob features. In 2009 IEEE international conference on multimedia and expo.

66

L. E. Haddad et al.

23. Abdelbaki, A. (2016). P-CNN: Pose-based CNN features for action recognition. Computer vision Lab SS16. 24. Shah, U., & Harpale, A. (2018). A review of deep learning models for computer vision. In 2018 IEEE Punecon. 25. Tang, X., Yan, Z., Pen, J., Hao, B., Wang, H., & Li, J. (2021). Selective spatiotemporal features learning for dynamic gesture recognition. Expert Systems with Applications, 169, 114499. 26. Mutegeki, R., & Han, D. S. (2020). A CNN-LSTM approach to human activity recognition. In 2020 international conference on artificial intelligence in information and communication (ICAIIC). 27. Yang, S., Zhou, Y., & Yu, X. (2020). LSTM and GRU neural network performance comparison study. In 2020 international workshop on electronic communication and artificial intelligence (IWECAI). 28. Chen, L., Li, Y., & Liu, Y. (2020). Human body gesture recognition method based on deep learning. In 2020 Chinese control and decision conference (CCDC). 29. Ullah, A., Muhammad, K., Del Ser, J., Baik, W., & de Albuquerque, V. H. C. (2019). Activity recognition using temporal optical flow convolutional features and multilayer LSTM. IEEE Transactions on Industrial Electronics, 66(12), 9692–9702. 30. Zhao, C., Han, J. G., & Xuebin Xu. (2018, September). CNN and RNN based neural networks for action recognition. In Journal of Physics: Conference Series; Bristol (Vol. 1087, No. 6). 31. Yang, Y., & Ramanan, D. (2013). Articulated human detection with flexible mixtures of parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), 2878–2890. https:// doi.org/10.1109/TPAMI.2012.261 32. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 33. Szegedy, C., et al. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA (pp. 1–9). https://doi.org/10. 1109/CVPR.2015.7298594 34. Gkioxari, G., & Malik, J. (2015). Finding action tubes (pp. 759–768). https://doi.org/10.1109/ CVPR.2015.7298676 35. Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A review of human activity recognition methods. Frontiers in Robotics and AI, 2, 28. 36. Wang, C., & Yan, J. (2023). A comprehensive survey of RGB-based and skeleton-based human action recognition. IEEE Access, 11, 53880–53898. https://doi.org/10.1109/ACCESS.2023.328 2311 37. Zhao, L. (2023). A hybrid deep learning-based intelligent system for sports action recognition via visual knowledge discovery. IEEE Access, 11, 46541–46549. https://doi.org/10.1109/ACC ESS.2023.3275012

Applications of Artificial Intelligence in Education

Streamlining Student Support: Enhancing Administrative Assistance and Interaction Through a Chatbot Solution Ghazouani Mohamed, Fandi Fatima Zahra, Chafiq Nadia, Daif Abderrahmane, Ettarbaoui Badr, Aziza Chakir, and Azzouazi Mohamed

Abstract

As part of our research, we conducted a systematic literature review on the implementation of a chatbot for assisting and interacting with students regarding their administrative inquiries. Our review encompassed a wide range of relevant articles, studies, and academic research to gather a comprehensive understanding of the topic. We examined the benefits and challenges of integrating a chatbot solution in educational institutions to streamline administrative processes and provide timely support to students. Additionally, we analyzed the effectiveness of chatbots in addressing common administrative questions, such as enrollment procedures, fee payments, and academic deadlines. Through this systematic review, we were able to identify trends, lacunae in the literature, and prospective domains for future research in optimizing the use of chatbots to enhance student services and facilitate administrative interactions. Keywords

Chatbot . Natural language processing . Artificial intelligence

G. Mohamed (B) · F. F. Zahra · C. Nadia · D. Abderrahmane · E. Badr · A. Mohamed Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_4

69

70

G. Mohamed et al.

Fig. 1 The general chatbot structure

1

Introduction

In the modern landscape of higher education, the fusion of technology and academia has ushered in an era of transformation. The infusion of Information and Communication Technology (ICT) has reimagined traditional learning paradigms, giving rise to dynamic smart learning environments. These environments, propelled by the promise of enhanced engagement and personalization, have ignited a quest for efficient and responsive educational support systems. At the forefront of this evolution lies the emergence of intelligent assistants, heralding a new age of seamless interaction between humans and technology. As educational institutions grapple with the challenges of providing individualized support, particularly in the administrative realm, innovative solutions are on the rise. A symbiotic relationship is emerging between academia and technology, offering potential resolutions to the perennial issues of subpar educational achievements, elevated rates of discontinuation, and discontentment. Central to this transformation are chatbot solutions, harnessing the power of natural language processing and artificial intelligence to bridge the gap between users and resources. This study centers on the development of a distinct chatbot referred to as FSBMBot that aims at providing assisting and interacting with students regarding their administrative inquiries at the Faculty of Sciences Ben M’Sick, the rest of the paper is organized as follows. The Sect. 2 reviews our related works. The Sect. 3 limitation. The proposed approach is shown in the Sect. 4. The Sects. 5 conclude the paper with future work (see Fig. 1).

2

Related Works

After reading many articles and SLRs I decide to base on these seven articles because they worked with methodologies and algorithms that offered the same things that I want to work with and provide to FSBMBot the best performance and high accuracy. According to Hien et al. [1], a Chatbot for Administrative and Learning Support called FIT-EBot that uses Artificial Intelligence (AI) and NLP algorithms like text classification and Named entity recognition (NER), can provide intelligent interaction to support both

Streamlining Student Support: Enhancing Administrative Assistance …

71

administrative and teaching services effectively. Carlander-Reuterfelt et al. [2] created the JAICOB, based on Recurrent Neural Networks (RNNs) and Named Entity Recognition (NER), which can actively boost students’ performances, especially within the context of computer science courses wherein studying cognitive behavioral patterns could yield substantial educational outcomes, especially within AI-related fields; integrating a cognitive computing stratum for digital student interactions not only amplifies their achievements but also simplifies instructors’ class and resource management, while also serving as adept analytical instruments, given students’ heightened engagement with chatbots compared to humans. A chatbot that offer users comprehensive general information, including details about educational offerings, fees, procedures, processes, and schedules. Additionally, they alleviate the staff’s workload, enable concurrent assistance for multiple users, and ensure accessibility from any computer device, according to Mendoza et al. [3] also Nguyen et al. [4], with NEU-chatbot provide student support and providing immediate access to information that sometimes the human service can’t offered. Tavichaiyuth and Rattagan [5] developed an academic chatbot for higher education in Thailand that offered support to higher education administrative functions from admissions to student services, can provide information quickly and effectively, in some contexts more efficiently than human agents because he is worked with Entity extraction and Intent classification. and also, students would not have to wait for a response and would engage in real-time chats with these bots, but it needs to improve some limits and give a solution to some problems like the complexity of sending a long message also due to limited time, there are not enough utterances to test the NLU components at the monitor phases. Patel et al. [6] from Computer Engineering Department, NMIMS University Mumbai, India created a College Enquiry Chatbot using Conversational AI with a higher accuracy that could deliver rapid and convenient responses to frequently asked questions, offering valuable insights into student life and campus culture, and aiding potential students in making informed educational choices. Regarding the exam regulations in a selected university, this could provide by the CONVERSATIONAL CHATBOT SYSTEM FOR STUDENT SUPPORT IN ADMINISTRATIVE EXAM INFORMATION developed by Rasheed et al. [7]. Table 1 shows different ways to improve administrative and learning support services. These chatbots have an accuracy rate of around 72–97%, highlighting their potential for precise interactions. Featured posts highlight the benefits of using chatbots such as B. Improved accessibility of information and better student support. However, they also address important aspects of data protection, data security, and the challenges of understanding complex languages. Collectively, these contributions shed light on the transformative impact of chatbots on higher education, while highlighting the need to address technical and contextual constraints.

Methods

Intelligent assistants – Text classification in higher-education – Named entity recognition (NER) environments: the FITEBot, a chatbot for administrative and learning support

Paper name – the user intent identification: 82.33% – the context information extraction: 97.3%

Accuracy

Weaknesses

(continued)

– can interact with users in a – Knowledge Extraction from more intelligent way to External Sources support both administrative – Lack of Knowledge Structure and teaching services – Context Handling – Complexity of Course-Specific Knowledge – Advanced Machine Learning Techniques – Training Data Collection

Benefice

Table 1 The following table summarizes key findings from a selection of research papers focused on the integration of intelligent chatbots in higher education environments

72 G. Mohamed et al.

Methods

Accuracy

Benefice

(continued)

Dependency on Google Technologies Privacy and Data Security Sample Size and Context

Weaknesses

A model to develop – NLP – The higher Students’ – The School – chatbots for assisting – Recurrent Neural precision is: 0,94 = Service-Oriented Networks (RNNs) the teaching and > 94% category includes chatbots – – Named Entity Recognition And for the teacher’s learning process that answer (FAQs) or – (NER) precision is: 0,96 => 96% provide users with general information, such as educational offers, fees, procedures, processes, and schedules – The major advantages of this kind of chatbot are constant availability, decreased workload for the staff, simultaneous attention of multiple users, and accessibility from any computer device

Paper name

Table 1 (continued)

Streamlining Student Support: Enhancing Administrative Assistance … 73

We obtain with the SVC a – They can actively enhance – Limited Understanding – Dependency on Data Quality final accuracy score of students’ performances, – Language and 0.799/79.9% especially in computer Communication science classes – studying cognitive computing behavior can lead to significant results in educational applications, especially in AI-related studies – using a cognitive computing layer for digital interactions with students can enhance their performances and ease the teachers’ job in managing classes and learning materials – chatbots are excellent analysis tools, as students feel more inclined to send more messages to chatbots than real people (continued)

Multinomial NB Decision Tree Random Forest SVC

– – – –

Weaknesses

JAICOB: a data science chatbot

Benefice

Accuracy

Methods

Paper name

Table 1 (continued)

74 G. Mohamed et al.

Accuracy – with an accuracy of 97.1% on test set

Methods

– NLP

Paper name

NEU-chatbot: chatbot for admission of National Economics University

Table 1 (continued)

– reduce the burden of admission counseling by automatically assisting admissions – student support and providing immediate access to information

Benefice

(continued)

– There was low-size of dataset – No details about algorithms on the paper

Weaknesses

Streamlining Student Support: Enhancing Administrative Assistance … 75

– – – –

Developing chatbots in higher education: a case study of academic program chatbot in Thailand

NLP NLU Intent classification Entity extraction

Methods

Paper name

Table 1 (continued)

– Precision: 0.984

Accuracy

Weaknesses

– Chatbots are able to – The number of user support higher education utterances that could be used administrative functions as training phrases is limited from admissions to student since it is unable to export services chat history from Facebook – can provide information directly quickly and effectively, in – Users may send long messages that are too some contexts more complex. For this reason, efficiently than human the chatbot agent should be agents – students would not have to designed to hand off the wait for a response and conversation to a human would engage in real-time agent. However, Google chats with these bots Dialog flow ES doesn’t support bot-to human handoff – Due to limited time, there are not enough utterances to test the NLU components at the monitor phases (continued)

Benefice

76 G. Mohamed et al.

– provide quick and convenient answers to common questions – provide helpful perspectives about student life and college culture – assisting prospective students in making educated educational decisions

– NLP – NLU

College enquiry chatbot using conversational AI

The chatbot reached an astounding 95% accuracy

– regarding the exam regulations in a selected university

– Named Entity Recognition – Reached 72.28% (NER – Relation Extraction (RE) – (SVM) – K nearest neighbour

Benefice

Conversational chatbot system for student support in administrative exam information

Accuracy

Methods

Paper name

Table 1 (continued)

– Less scalability and customization – needs more features – needs a larger database

– There were no details about the dataset mentioned in the paper

Weaknesses

Streamlining Student Support: Enhancing Administrative Assistance … 77

78

3

G. Mohamed et al.

Limitation

The challenges associated with developing chatbots are multifaceted. One major hurdle is the extraction of knowledge from external sources, often compounded by the absence of a coherent knowledge structure. Efficiently handling context and the complexity of coursespecific information further add to the intricacies. Advanced machine learning techniques and accurate training data collection are imperative, but dependency on Google technologies can be limiting. Privacy and data security are paramount concerns, alongside the challenge of maintaining data quality and navigating language nuances. The size of available datasets, lack of algorithm details, and limitations in importing chat history from platforms like Facebook contribute to the complexity. Additionally, addressing the need for human agent handoff, testing in limited time, and the absence of dataset specifics pose significant obstacles. The quest for scalability, customization, and additional features necessitates a larger and more diverse database.

4

Proposed Approach

Based on the RELATED WORKS, we defined the parameters of our project and made the determination that Administrative Assistance and Interaction would be developed and generated with this approach involves the amalgamation of diverse technologies, harnessing the capabilities of advanced Natural Language Processing and Machine Learning techniques. The goal is to forge interactions that closely resemble human conversations, thereby enriching the overall user experience. The objective is to develop a ChatBot empowered by artificial intelligence (AI) solution tailored for the Faculty of Sciences Ben M’sik (FSBM), offering support and interaction capabilities to students for their administrative inquiries. This innovative ChatBot system will be designed to operate in multiple languages, including French and Arabic, catering to the diverse linguistic needs of the student population. The ChatBot solution’s main objectives encompass diverse functionalities. Firstly, it aims to provide instantaneous access to pertinent information, allowing students to swiftly obtain responses to their queries regarding administrative matters, academic timelines, enrollment procedures, fees, and more. Secondly, the platform facilitates the submission of assistance requests and grievances, bolstering communication between students and administrative staff for efficient issue resolution. The system’s capabilities extend to generating performance metrics and dashboards that offer insights into its activity, thereby assessing student satisfaction and informing enhancements for an improved user experience. Integration with FSBM’s Information System stands as a pivotal feature, fostering seamless data exchange and synchronization for administrative details, requests, and updates. Moreover, the ChatBot exhibits versatility by accommodating diverse multimedia formats, including text, images, audio, files, videos, and location data, catering to

Streamlining Student Support: Enhancing Administrative Assistance …

79

various communication preferences. The platform further supports multilingual engagement, being accessible in both Arabic and French, facilitating interactions in students’ native languages, and enhancing understanding. Lastly, a strong commitment to compliance and data protection is emphasized, aligning with GDPR and CNDP guidelines, and ensuring the security and confidentiality of students’ information and interactions. Collectively, these features aim to revolutionize administrative interactions within FSBM, fostering efficient services and processes. In conclusion, our ChatBot project, inspired by a comprehensive literature review, seeks to revolutionize administrative interactions within the educational environment. By combining cutting-edge AI technology, multilingual support, seamless integration, and robust data protection measures, we aim to provide FSBM students with an efficient and user-friendly tool for obtaining information and seeking assistance related to their administrative queries. This innovative solution paves the way for enhanced student services and administrative processes at FSBM.

5

Conclusions and Future Work

In conclusion, this study highlights the transformative potential of integrating chatbot solutions into higher education to enhance student support and streamline administrative interactions. Through an in-depth analysis of related work, this study gleans valuable insights from existing successful chatbot implementations such as FIT-EBot, JAICOB, and NEU-Chatbot. These examples guided the development of the FSBMBot system, which aims to provide Ben M’Sick science students with instant information access, efficient query resolution, and enhanced communication channels. Three aspects that can be considered in future studies are as follows: 1. 2. 3. 4.

Turn chatbots into voice bots to interact well with students integrate your chatbot into the Metaverse integrate the chatbot into the metaverse environment develop the chatbot to not only interact with higher education students but also with other levels such as high school, college, and why not primary school children.

References 1. Hien, H. T., Cuong, P.-N., Nam, L. N. H., et al. (2018). Intelligent assistants in higher-education environments: the FIT-EBot, a chatbot for administrative and learning support. In Proceedings of the 9th International Symposium on Information and Communication Technology (pp. 69–76). 2. Carlander-Reuterfelt, D., Carrera, Á., Iglesias, C. A., et al. (2020). JAICOB: A data science chatbot. IEEE Access, 8, 180672–180680

80

G. Mohamed et al.

3. Mendoza, S., Sánchez-Adame, L. M., Urquiza-Yllescas, J. F., et al. (2022). A model to develop chatbots for assisting the teaching and learning process. Sensors, 22(15), 5532. 4. Nguyen, T. T., Le, A. D., Hoang, H. T., et al. (2021). NEU-chatbot: Chatbot for admission of National Economics University. Computers and Education: Artificial Intelligence, 2, 100036. 5. Tavichaiyuth, N., & Rattagan, E. (2021). Developing chatbots in higher education: A case study of academic program chatbot in Thailand. 6. Patel, D., Shetty, N., Kapasi, P., & Kangriwala, I. (2023). College enquiry chatbot using conversational AI. International Journal for Research in Applied Science and Engineering Technology, 11. https://doi.org/10.22214/ijraset.2023.51324 7. Rasheed, H. A., Zenkert, J., Weber, C., et al. (2019). Conversational chatbot system for student support in administrative exam information. In ICERI2019 Proceedings. IATED (pp. 8294–8301).

Towards a System that Predicts the Category of Educational and Vocational Guidance Questions, Utilizing Bidirectional Encoder Representations of Transformers (BERT) Omar Zahour, El Habib Benlahmar, Ahmed Eddaoui, and Oumaima Hourane

Abstract

In today’s complex job landscape, educational and vocational guidance has emerged as a critical factor in determining successful integration. Families are increasingly acknowledging its value, actively participating in shaping their child’s educational path. Responding to this need, we have created a system leveraging the BERT technique to categorize queries pertaining to educational and vocational orientation, drawing upon the principles of Holland’s test. Text classification, particularly when it pertains to question classification, is a crucial component in the realm of natural language processing and represents a foundational aspect of artificial intelligence. Given the vast amount of textual data available today, a robust word processing system is essential. Transformers models like Bidirectional Encoder Representations of Transformers (BERT) have gained immense popularity in NLP due to their remarkable performance in various tasks. This article demonstrates the implementation of a multi-class classification using BERT, specifically focusing on questions related to educational and vocational guidance following Holland’s RIASEC typology. Our model effectively categorizes each input question into one of four classes: The components of Activity, Occupations, Abilities, and Personality make up our dataset. The findings suggest that our methodology demonstrates competitive efficacy.

O. Zahour (B) · E. H. Benlahmar · A. Eddaoui · O. Hourane Laboratory of Information Technology and Modelling, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_5

81

82

O. Zahour et al. Keywords

Academic and vocational guidance . Text classification . Automatic natural language processing . BERT model . Holland RIASEC typology

1

Introduction

After thorough research, the renowned American academic, John Holland, revealed that every individual in the workforce falls under one of six distinct worker categories. These categories are delineated as: “Realistic” (R), “Investigative” (I), “Artistic” (A), “Social” (S), “Enterprising” (E), and “Conventional” (C). Holland posited—a theory corroborated by multiple studies—that an individual’s chosen career path mirrors their personality, thereby aligning with one of the designated categories they inherently resonate with. Their skills, specific personality characteristics, and interests are key factors in determining their alignment with one of the six types. Consequently, as Holland suggests, people sharing the same type are likely to gravitate towards similar kinds of work. Why is this the case? Because these individuals are molded by their personalities and the shared pursuit of similar goals, displaying similar physical or psychological tendencies towards their chosen professions, individuals can ultimately be classified into one of six distinct professional groups. A person’s typology is determined by assessing their level of compatibility with each of the six types and arranging these in a hierarchy of relevance, from the most fitting type to the least. For a majority of individuals, it is primarily the first two or three categories in their personal classification that dictate their behavior and approach, encompassing both personal and professional spheres. For instance, an individual with a pronounced “Investigative” trait who also resonates with the “Realistic” category can be described as having an “IR” profile. To further delineate this individual’s typology, one might consider incorporating a third prominent category to provide a more nuanced description. If the “Social” category is the next best fit, then this person can be characterized as having an “IRS” profile. The blending of types can occur in various combinations, and it’s the character of this fusion that defines specific personality traits. In this study, our goal is to craft an automated system specifically designed for classification tasks of queries pertaining to educational and vocational guidance. This classification is founded on Holland’s RIASEC typology, utilizing the BERT methodology. This would facilitate the automatic categorization of new guidance queries, both educational and professional, allowing us to pinpoint the three predominant personality types of an individual and thereby guide them to a profession well-suited for them as per the comprehensive list of occupations and professions. Text classification represents a cornerstone task within the realm of natural language processing (NLP), boasting a multitude of practical use cases and enjoying robust research

Towards a System that Predicts the Category of Educational …

83

backing. It involves categorizing text into various predefined categories or labels. Some notable text classification applications include: Sentiment analysis [1]: This area of research has garnered significant attention, with numerous citations, for its role in gauging the sentiment or emotional tone expressed in text. The detection of textual similarities and the identification of plagiarism [2–4]: Methods for measuring the similarity between texts and identifying instances of plagiarism have been extensively studied and have garnered substantial citations. Question classification [5]: Identifying the type or category of a question has been a subject of interest in NLP, and related studies have contributed to the field. Subject classification [6]: Research on categorizing text by subject matter has seen widespread citation in scholarly work. Sophisticated deep learning strategies such as convolutional neural networks (CNN) [7] and recurrent neural networks (RNN) [8], among other refined approaches, have established themselves as the standard in text classification, owing to their remarkable efficacy and adaptability. Using deep learning for text classification involves feeding text data into a deep neural network to derive a significant text representation. This representation is subsequently passed through a Softmax function to ascertain the probability distribution over various categories. Models based on CNN [7, 9, 10] adeptly capture localized information within the text, whereas RNN-based models [11, 12] are proficient at preserving long-term contextual data. To fully exploit the benefits of both setups, hybrid configurations such as C-LSTM [13], CNN-LSTM [14], and DRNN [15] have been launched, amalgamating the strengths of both CNN and RNN networks to amplify text classification proficiency. Numerous models integrate attention mechanisms, facilitating the focalization on critical data within the text by leveraging this fundamental principle. For instance, the HAN model [16] employs a hierarchical attention mechanism, utilizing a bidirectional RNN for encoding processes, to segregate the text into two tiers: sentences and words. Conversely, the DCCNN model [17] initiates the process by employing a multilayer CNN to extract various n-gram features, followed by the application of an attention mechanism to acquire representations by highlighting the most prominent attributes. The MEAN model [18] seeks to tackle current challenges by integrating three distinct linguistic sentiment perspectives into a deep neural network using attention-based techniques. On the other hand, DiSAN [19] introduces a novel attention technique where the allocation of attention across the components of the input sequences is directed and operates in multiple dimensions. Furthermore, there are models where the attention mechanism plays a central role. Take, for example, the Bi-BloSAN model [20], which employs a Block self-attention mechanism as a text encoder, coupled with a gating network for feature extraction.

84

O. Zahour et al.

Together, these models utilize the attention mechanism to focus on more crucial characteristics, better mirroring human observation patterns compared to conventional max pooling and average pooling approaches. Moreover, pre-training on linguistic models has demonstrated its effectiveness in acquiring universal language representations through the utilization of extensive amounts of unlabeled data. Prominent examples encompass Elmo [21], GPT [22], ULMFiT [23], and BERT [24]. Fundamentally, these models are built upon neural network language architectures that have been developed utilizing text data, propelled by unsupervised objectives. For example, BERT leverages a bidirectional multi-layer transformer and undergoes training on unprocessed text to execute functions like masked word anticipation and following sentence forecasting. To customize a pre-trained model for specific applications, it is imperative to finetune it using training data that is centered on the particular task at hand. Furthermore, it is necessary to incorporate additional layers that are tailored to the task after the initial pre-training phase. For instance, when deploying BERT for text classification tasks, a straightforward softmax layer is appended to the pre-trained model. Through fine-tuning, BERT can then be evolved to produce sophisticated models adept at text classification for specific datasets. The BERT model excels in text classification pursuits owing to its profound language understanding capabilities. To tackle the task of classifying educational and vocational guidance questions, we have opted for the BERT model. This selection enables the automated creation of educational and vocational orientation questionnaires, categorized into four distinct groups based on Holland’s foundational model, theory, and the RIASEC typology. In this article, we advocate for the use of the BERT model in text classification. Our model is adept at discerning the category of the presented question with increased precision. This article is organized as: The opening section is dedicated to discussing research pertinent to the subject matter. Subsequently, the method employed in this study is elucidated in the second section. The experiment conducted and its resultant findings are detailed in the third and final sections respectively. To conclude, we offer insights derived from the research perspectives explored in this study.

2

Review of Corresponding Research

2.1

The RIASEC Assessment

The RIASEC Assessment, often referred to as the “HOLLAND test,” was crafted by psychologist John HOLLAND. This tool is based on a career and vocational choice theory, emphasizing six primary personality types found in professional settings: “Realistic”, “Investigative”, “Artistic”, “Social”, “Enterprising”, and “Conventional”.

Towards a System that Predicts the Category of Educational …

85

Fig. 1 Diagram of the RIASEC typology

As per HOLLAND’s perspective, individuals of the same “type” frequently gravitate towards similar professions. This alignment stems from shared personality traits, unified goals, and comparable physical and psychological inclinations towards their respective vocations. A person’s typology is ascertained by evaluating their degree of affinity with each of the six categories, thereby ranking them from the most prominent to the least pronounced type. For the majority of people, the top two or three categories in their personal hierarchy have a substantial impact on their behavior and actions, encompassing both their personal and professional dimensions (see Fig. 1). Realistic: This person is sensible and grounded. They favor concrete tasks and prefer engaging in manual labor. Investigative: This type is characterized by an intellectual bent, showcasing curiosity and an analytical mindset. Investigators revel in problem-solving and seek a deeper understanding of things, often thriving on theoretical knowledge. Artistic: Driven by emotion and intuition, this individual seeks expressive outlets. They are innately creative, often bucking conventional norms. For them, a unique work environment is essential, and they often learn best through hands-on experimentation. Social: Personable and warm, this type thrives on interpersonal connections. They are compelled to communicate, educate, and assist others. Collaborative learning and teamwork are their preferred modes of acquiring knowledge. Enterprising: This individual is marked by ambition and persuasiveness. With a knack for leadership and motivation, they flourish in environments that are competitive. They often learn best through direct experience in their field. Conventional: This individual is characterized by an orderly and meticulous approach to tasks. They appreciate structured, procedural environments and often have a preference for administrative tasks. They thrive in settings where instructions and methods are clearly laid out, facilitating their learning process.

86

2.2

O. Zahour et al.

Deep Artificial Neural Networks

In recent times, the domain of natural language processing has observed marked advancements, with deep neural architectures being central to these breakthroughs. Recurrent Neural Networks (RNNs), featuring components like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have notably excelled in handling sequential word data. Additionally, modified versions like Tree-LSTM [11] and TG-LSTM [12] have emerged. Convolutional Neural Networks (CNN) represent another widely embraced deep neural network structure. Efforts have been directed towards deepening CNNs for text classification; for instance, VDCNN [9] endeavors to construct a more profound CNN for this purpose. Various strategies have been employed in text classification, including the utilization of filters with diverse window sizes to capture convolutional features at different scales, as exemplified in [10]. DCNN [25] employs a dynamic k-max pooling strategy, while DPCNN [26] aims to amplify the depth of CNNs without incurring a substantial computational burden. A novel weight initialization method designed to optimize CNNs for text categorization is unveiled in [27], which features a multitasking convolutional network approach. The LK-MTL model [28] presents a Leaky unit, designed to merge memory attributes with a forgetting mechanism to manage the information exchange between different tasks. Conversely, char-CNN [7] functions at the character level, encoding each distinct character within the given text. In the continuously advancing realm of deep learning, a plethora of efforts are directed towards merging the virtues of both CNN and RNN models to exploit the best features of each. Take, for instance, the C-LSTM [13] strategy which initiates by leveraging a CNN to seize local textual nuances, followed by the utilization of the LSTM network to encode each output generated from the convolutional kernel. This approach allows for the capture of a more comprehensive global perspective of information. The CNN-RNN [14] model follows a similar structural pathway, albeit with a distinctive connecting strategy between the CNN and RNN layers, differentiating it from the C-LSTM approach. On another note, the DRNN [15] strategy substitutes the convolutional kernel with an RNN unit, fostering an integration of the CNN’s structural attributes with the encoding prowess of the RNN. This symbiosis aims to maximize the efficiency and effectiveness of text processing and classification tasks.

2.3

Pre-training Model

In a manner mirroring the developments observed in computer vision research, pre-trained models have demonstrated remarkable effectiveness across various natural language processing tasks. These models generally leverage large volumes of unlabeled data to

Towards a System that Predicts the Category of Educational …

87

acquire universal language representations. Subsequently, they integrate task-specific layers beyond the initial pre-trained module to cater to a diverse array of tasks and achieve impressive results. Elmo [21] is specially engineered to generate context-sensitive representations from language models, establishing a significant influence on various NLP benchmarks such as question responding [29], emotion analysis [1], and named entity recognition [30]. Following a similar trajectory, GPT [22] and ULMFiT [23] begin by pre-training a model framework based on a language modeling (LM) goal. They then further refine this initial model to adapt to supervised downstream applications, including text classification. Additionally, the BERT method [24] stands as another noteworthy mention in this context, further illustrating the versatility and effectiveness of pre-trained models in tackling complex NLP challenges.

3

The Proposed Model

The BERT algorithm is grounded in groundbreaking approaches, including sequence-tosequence (seq2seq) models and transformer architectures. The seq2seq model operates as a network that converts a specific word sequence into an alternate sequence, having the ability to highlight words that seem to be more relevant. An apt exemplification of the seq2seq model is the LSTM network. In contrast, the transformer architecture, unlike recurrent networks such as LSTM or GRU, is designed to transform one sequence into another without relying on recurrent connections. This distinctive feature enables parallelized processing of sequences, leading to increased efficiency and the ability to capture intricate data patterns effectively. In this classification framework, BERT [24], grounded in a bidirectional multilayer transformer, is utilized. Trained on plain text data, it demonstrates proficiency in predicting masked words and performing subsequent sentence classification. Figure one provides an overview of BERT’s configuration when applied to text categorization tasks, showcasing its intricate design and proficiency in understanding and classifying textual data (see Fig. 2). At the input phase, [CLS] and [SEP] act as the starting and ending markers of a sentence, respectively. The lexicon encoder takes on the task of calculating the sum of token embedding, segment embedding, and position embedding. This combined data is then channeled through the multi-layer self-attention mechanism, commonly referred to as the transformer encoder, which is depicted in the box diagram. During this phase, a corresponding output value is allocated to each individual input token. Notably, the [CLS] token embodies the representation of the entire text, as it assimilates information derived from all the constituent words in the text. Ultimately, this token (denoted as ‘C’) is fed into the Softmax layer to facilitate the extraction of classification outcomes.

88

O. Zahour et al.

Fig. 2 BERT model schematic

Within this framework, ‘C’ emphasizes the value of each word in the text, viewing each word as entities that are both equal and autonomous. Nevertheless, it doesn’t necessarily emphasize the information encapsulated within specific fragments or sentences found within the broader text, thereby focusing on individual word contributions to the overall text representation.

4

Exploring Experimental Findings and Results

4.1

The Dataset and Its Characteristics

The data in our dataset is derived from the RIASEC test, which is grounded in Holland’s theory as referenced in [28–33]. The dataset comprises two primary columns: Question: This column encompasses questions and statements tailored to assess an individual’s professional preferences, activities they’re inclined towards, their unique capabilities, as well as intrinsic personality traits. Categories: Within this column, we’ve delineated four distinct classes or labels: Activity (0): Refers to specific actions or tasks an individual might prefer or engage in. Occupations (1): Focuses on particular professions or job roles that an individual might lean towards. Abilities (2): Highlights specific skills or competencies an individual possesses or feels confident about.

Towards a System that Predicts the Category of Educational …

89

Personality (3): Delves into innate personality attributes or tendencies that shape an individual’s behavior and preferences. This structured dataset aids in providing a comprehensive overview of an individual’s vocational inclinations, skill set, and inherent personality characteristics.

4.2

The Steps of the Experiment

The experimental procedure is delineated as follows. To efficiently utilize BERT for inference, specific requirements must be fulfilled. BERT relies on data with distinct formatting criteria, and datasets are typically structured to encompass the following four essential attributes: Identifier: An exclusive label signifying an individual observation. Text_a: This field contains the text that necessitates classification into predetermined categories. Text_b: It becomes relevant when constructing a model to understand relationships between sentences, although it doesn’t apply to classification tasks. Label: This field includes the labels, classes, or categories that a specific text belongs to. In the dataset, there are ‘text_a’ and ‘label’ attributes. To modify this data to conform to the format expected by BERT, the following step involves creating objects for each of these characteristics across all records in the dataset. This can be done using the ’Input Example’ class available in the BERT library. Having organized the data into a compatible format for the BERT model, we can now move forward with data pre-processing, which encompasses the subsequent stages: Normalizing the text: This process includes converting all characters to lowercase or maintaining their original casing, based on the variant of the BERT model in use (either cased or uncased). These steps prepare the data for effective utilization with the BERT model, ensuring that it adheres to the required input format and is ready for further processing and analysis. To prepare the text data for BERT, you need to follow these steps: Tokenize the text: This involves dividing the sentence into individual words or tokens while also separating any punctuation characters from the text. Tokenization is crucial to ensure that the text is broken down into units that BERT can understand. Add CLS and SEP tokens: To provide context and structure, it’s essential to incorporate special tokens such as [CLS] (indicating classification) at the sentence’s outset and [SEP] (denoting separator) at its conclusion. These tokens help BERT identify the start and end of a sentence and understand relationships between different sentences in the input. Break words into Word Pieces: BERT uses a technique called Word Piece tokenization. This method splits words into sub word units based on similarity. For example, “calling”

90

O. Zahour et al.

might be divided into [“call”, “##ing”]. This approach allows BERT to handle a wide range of word variations and morphological differences. Match words to vocabulary: Map the tokenized words to their corresponding indexes in BERT’s specific vocabulary. BERT’s vocabulary is typically stored in a file named “vocab.txt,” which contains a mapping of words or sub word units to their unique numerical identifiers. By following these steps, you preprocess the text data in a way that makes it compatible with BERT’s input requirements, allowing you to leverage the model effectively for various natural language processing tasks. All the aforementioned tasks are efficiently handled by BERT’s tokenization package. This package streamlines the preparation of text data for input into the BERT model. In the resulting display, the initial sentence from the training set is visible, succeeded by its tokenized form Entry IDs correspond to token IDs, where each ID corresponds to a distinct token. Input masks help distinguish actual tokens from padding components. In this study, padding components are denoted by a value of 0. The set sequence length determines the amount of padding. Should the token length be less than this defined sequence length, padding is added by the tokenizer to keep the sequence length consistent. Segment IDs serve the purpose of distinguishing between different sentences. In your particular scenario, where you have a single text segment, all segment IDs remain uniform. If you were processing two separate sentences, each word in the first sentence would be assigned a segment ID of 0, while each word in the second sentence would be assigned a segment ID of 1. After adequately preparing the input data, the next steps involve loading the BERT model, configuring it with essential parameters, and commencing the experimental phase, which includes incorporating vital metrics and evaluation procedures.

4.3

The Results

Utilizing the BERT algorithm for classifying academic and professional guidance questions based on Holland’s RIASEC typology is indeed a notable achievement. An accuracy value of 0.955 signifies a very high level of accuracy in your classification model. This accuracy score indicates that the model is making correct predictions for the majority of the questions, underscoring its effectiveness in categorizing questions according to the specified typology. It’s evident that your model is performing exceptionally well, and this level of accuracy bodes well for its potential utility in real-world applications related to academic and professional guidance (see Figs. 3, 4 and 5).

Towards a System that Predicts the Category of Educational …

Fig. 3 Assessment criteria for the mode

Fig. 4 Analysis results of the classification model

Fig. 5 Forecasting categories for queries

91

92

5

O. Zahour et al.

Conclusion

Presented in this paper is an innovative classification model named BERT, meticulously crafted for the purpose of categorizing educational and vocational counseling queries based on the Holland typology. We are excited to share that our model has delivered substantial results in the assessment of its classification capabilities. Looking ahead, to further enhance our model’s capabilities, we propose the exploration of a hybrid approach that combines the strengths of the BERT method with Convolutional Neural Networks (CNN). It’s worth noting that in our preceding research [33], we have previously ventured into automatic question classification using neural networks and some algorithms of Machine Learning [34]. Therefore, amalgamating BERT with CNN could potentially provide an avenue for even more robust and accurate classification outcomes. This research direction holds promise for advancing the effectiveness of our model in the realm of educational and vocational guidance.

References 1. Maas, A. L., et al. (2011). Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Vol. 1). Association for Computational Linguistics. 2. Hourrane, O., et al. (2018). Using deep learning word embeddings for citations similarity in academic papers. In International conference on big data, cloud and applications. Springer. 3. Hourrane, O., & Benlahmar, E. H. (2017). Survey of plagiarism detection approaches and big data techniques related to plagiarism candidate retrieval. In Proceedings of the 2nd International Conference on Big Data, Cloud and Applications. ACM. 4. Hourrane, O., & Benlahmer, E. H. (2019). Rich style embedding for intrinsic plagiarism detection. International Journal of Advanced Computer Science and Applications (IJACSA), 10(11). https://doi.org/10.14569/IJACSA.2019.0101185 5. Zhang, D., & Lee, W.S. (2003). Question classification using support vector machines. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM. 6. Wang, S., & Manning, C. D.: Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers (Vol. 2). Association for Computational Linguistics. 7. Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. Advances in Neural Information Processing Systems. 8. Chung, J., et al. (2014). Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv:1412.3555 9. Conneau, A., et al. (2016). Very deep convolutional networks for text classification. arXiv:1606. 01781 10. Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv:1408.5882 11. Tai, K. S., Socher, R., & Manning, C. D. (2015). Improved semantic representations from treestructured long short-term memory networks. arXiv:1503.00075

Towards a System that Predicts the Category of Educational …

93

12. Huang, M., Qian, Q., & Zhu, X. (2017). Encoding syntactic knowledge in neural networks for sentiment classification. ACM Transactions on Information Systems (TOIS), 35(3), 26. 13. Zhou, C., et al. (2015). A-C-LSTM neural network for text classification. arXiv:1511.08630 14. Xiao, Y., & Cho, K. (2016). Efficient character-level document classification by combining convolution and recurrent layers. arXiv:1602.00367 15. Wang, B. (2018). Disconnected recurrent neural networks for text categorization. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Long Papers (Vol. 1). 16. Yang, Z., et al. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 17. Wang, S., Huang, M., & Deng, Z. (2018). Densely connected CNN with multi-scale feature attention for text classification. In IJCAI. 18. Lei, Z., et al. (2018). A multi-sentiment-resource enhanced attention network for sentiment classification. arXiv:1807.04990 19. Shen, T., et al. (2018). DiSAN: Directional self-attention network for RNN/CNN-free language understanding. In Thirty-second AAAI conference on artificial intelligence. 20. Shen, T., et al. (2018). Bi-directional block self-attention for fast and memory-efficient sequence modelling. arXiv:1804.00857 21. Peters, M. E., et al. (2018). Deep contextualized word representations. arXiv:1802.05365 22. Radford, A., et al. (2018). Improving language understanding by generative pre-training. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/languageunsupervised/lan guageunderstandingpaper.pdf 23. Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. arXiv:1801.06146 24. Devlin, J., et al. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 25. Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv:1404.2188 26. Johnson, R., & Zhang, T. (2017). Deep pyramid convolutional neural networks for text categorization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Long Papers (Vol. 1). 27. Li, S., et al. (2017). Initializing convolutional filters with semantic features for text classification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 28. Xiao, L., et al. (2018). Learning what to share: Leaky multi-task network for text classification. In: Proceedings of the 27th International Conference on Computational Linguistics. 29. Rajpurkar, P., et al. (2016). SQuAD: 100,000+ questions for machine comprehension of text. arXiv:1606.05250 30. Sang, E. F., & De Meulder, F. (2003). Introduction to the CoNLL-2003 shared task: Languageindependent named entity recognition. cs/0306050 31. Zahour, O., Benlahmar, E. H., & Eddaoui, A. (2016, October). E-Orientation: Between prescription of theories and decision-making. In Conference SITA’16. 32. Zahour, O., Benlahmar, E. H., & Eddaoui, A. (2018). E-orientation: Vers une modélisation des facteurs d’orientation scolaire. In Conference TIM’18. 33. Zahour, O., Benlahmar, E. H., Eddaoui, A., & Hourrane, O. (2019, January). Automatic classification of academic and vocational guidance questions using multiclass neural network. International Journal of Advanced Computer Science and Applications, 10(10). https://doi.org/10. 14569/IJACSA.2019.0101072

94

O. Zahour et al.

34. Zahour, O., Benlahmar, E. H., Eddaouim, A., & Hourrane, O. (2020). A comparative study of machine learning methods for automatic classification of academic and vocational guidance questions. International Journal of Interactive Mobile Technologies (iJIM), 14(08), 43–60. https://doi.org/10.3991/ijim.v14i08.13005

Artificial Intelligence in the Context of Digital Learning Environments (DLEs): Towards Adaptive Learning Imane Elimadi, Nadia Chafiq, and Mohamed Ghazouani

Abstract

This study carefully considers student perceptions, potential benefits, and ethical considerations to analyze the complex landscape of integrating artificial intelligence (AI) into digital learning environments (DLEs). The investigation is based on the opinions of a diverse group of 40 students from the University Hassan II Faculty of Sciences. An online questionnaire with open-ended questions and binary “yes” or “no” responses was given to participants, allowing for a thorough examination of their attitudes. Beginning with an acknowledgement of the respondents’ evenly distributed gender, the analysis strengthens the validity and applicability of the study’s findings. Participants clearly have a lot of knowledge about AI, which highlights a general awareness that contextualizes their views on AI’s place in education. The majority of respondents express optimism about AI’s potential to improve student learning outcomes through individualized and flexible educational experiences. The discussion, however, delicately navigates this optimism, revealing subgroups marked by cautious optimism and a need for conclusive evidence from the real world before full endorsement. A I. Elimadi (B) · N. Chafiq · M. Ghazouani Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco e-mail: [email protected] Information and Education Sciences and Technologies Laboratory (LASTIE), Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco Observatory of Research in Didactics and University Pedagogy (ORDIPU), Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco Laboratory “Information Technologies and Modeling (TIM)”, Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_6

95

96

I. Elimadi et al.

subset also questions the effectiveness of AI, which prompts a look at potential difficulties and complexities in its use. The discussion frequently brings up ethical issues. The majority of participants support the idea that educational institutions should take the initiative to address ethical issues related to the use of AI. The importance of ensuring transparent, accountable, and responsible AI usage in educational contexts is highlighted by this consensus. Participants’ openness to AI-driven data analysis for individualized recommendations is also a topic of discussion. Despite a majority saying they are willing to use this strategy, a subgroup’s reluctance highlights how important it is to incorporate ethical and privacy protections into its application. The conversation as a whole provides a thorough and impartial examination of viewpoints on AI integration in DLEs. It skillfully balances innovation with moral sensitivity, optimism with caution, and captures the changing face of education in the digital age. The knowledge gained from this study serves as a solid foundation for deliberation and decision-making, which is crucial for the moral and successful integration of AI technologies in education. Keywords

Artificial intelligence . Digital learning environments . Adaptive learning Personalized learning . Learning progress

1

.

Introduction

Artificial intelligence (AI) has emerged as a transformative force within digital learning environments (DLEs) in the constantly changing educational landscape, where technology continues to redefine the way we learn and teach. A potential strategy known as adaptive learning—a pedagogical paradigm that customizes the learning experience to each learner’s unique requirements, preferences, and progress—has emerged as a result of the convergence of AI and education. By delivering individualized, effective, and dynamic learning experiences, this AI integration into DLEs is poised to transform traditional education. In order to analyze enormous volumes of data and make prompt decisions about material distribution, pace, and assessment, adaptive learning makes use of the power of AI algorithms. This individualized method takes into account the various learning profiles and learning preferences of pupils, ensuring that educational material is both ideally challenging and relevant. As a result, students are provided with a learning journey that is specifically tailored to their strengths and areas for improvement, moving away from the one-size-fits-all concept. It becomes evident when we explore the world of AI-driven adaptive learning within DLEs that the potential advantages go beyond personalised education. AI-generated insights can be used by institutions and educators to improve curriculum development, pinpoint areas for improvement, and hone instructional methods.

Artificial Intelligence in the Context of Digital Learning Environments …

97

Additionally, AI-enabled analytics offer a thorough perspective of student performance, enabling prompt interventions and support to be provided when it’s most needed. The potential of AI in education are accompanied by important considerations, though. In order to shape AI-driven education, careful consideration must be given to ethical issues, data protection, and the requirement for human oversight. To ensure that the transformational potential of adaptive learning is properly tapped, it is crucial to strike a balance between the technological prowess of AI and the ethical dimensions of education. It is impossible to overestimate AI’s profound influence on education as it continues to alter a number of industries. The conventional method of imparting knowledge to a diverse classroom of students—each with their own strengths and challenges—is constrained. AI-driven adaptive learning has promise since it can overcome these constraints. By utilizing AI’s pattern recognition and data analysis capabilities, educators can now gain new insights into student learning trajectories, allowing them to customize interventions and content in unimaginable ways. The necessity for a symbiotic link between human expertise and AI capabilities is highlighted by the paradigm change in education, as educators continue to design relevant learning experiences while AI optimizes their delivery. In the framework of DLEs, this paper explores the multifaceted landscape of AI with a particular emphasis on the rise of adaptive learning. We seek to shed light on the future of education, where technology and human knowledge join to produce an educational experience that is as varied as the learners it serves, through a thorough examination of its advantages, difficulties, and ethical implications. Adaptive learning powered by AI represents a promising advancement in creating a more inclusive, interesting, and successful educational experience in a time when the pace of technological development is only matched by the desire to learn.

2

Review of Literature

2.1

Adaptive Learning: Personalized Education Through AI

Since artificial intelligence (AI) has been incorporated into digital learning environments, the idea of adaptive learning has gained popularity as a way to tailor instruction to the needs of specific students. Researcher contributions of note shed light on how adaptive learning has a revolutionary effect in personalized education. Researchers Smith, K. L., Johnson, A. R., Martinez, J. M., and Chen examined AI-driven platforms in higher education and found that dynamically changed learning materials increased engagement and performance outcomes [1, 2]. Brown, C., and Martinez, J. M. Aiming to reduce biases and ensure equitable learning opportunities, R. investigated the ethics of AI-powered education [3]. R. argued for openness and responsibility. By showing how tailored recommendations that are in line with learners’ preferences enhance autonomy and intrinsic

98

I. Elimadi et al.

motivation, Johnson, A. R., and Wang, S. Y., highlighted AI’s role in improving motivation and self-regulation [1]. Furthermore, Chen, H. Y., Smith, E. R., Lee, M. H., and Wang, L. J. empirically investigated how students viewed adaptive learning, and they discovered that satisfaction and perceived learning effectiveness were higher when adaptive recommendations matched learners’ preferences. These findings add to the ongoing conversation on individualized learning enabled by AI-driven adaptive learning by demonstrating how adaptive learning has the potential to transform education through content flexibility, ethical issues, increased motivation, and student happiness. Additionally, Brown and Lee [4] carried out a longitudinal study to investigate the long-term impacts of adaptive learning interventions on student retention rates. Their study found a link between greater student persistence and individualized learning experiences made possible by AI algorithms, which is important information for educational institutions looking to improve student performance. Furthermore, Garcia et al. [5] examined the pedagogical changes brought on by AI-driven adaptive learning, emphasizing the necessity for educators to transform into personal learning journey facilitators rather than traditional information providers. Their study underlined that the experience and guidance that educators contribute to the learning process should be supplemented by AI rather than replaced by it. Together, these important studies—among others—highlight the complex interaction between AI and adaptive learning, reshaping the field of personalized learning while recognizing the crucial function of teachers.

2.2

Ethical Considerations in AI-Enhanced Learning

Ethical issues become an important focus for researchers and educators alike as the integration of artificial intelligence (AI) within digital learning environments (DLEs) grows more widespread. The investigation of ethical issues in AI-enhanced learning settings is characterized by contributions from a range of academics, illuminating the intricate relationship between pedagogy, technology, and ethical obligations. A thorough review of the ethical ramifications of AI-driven educational systems was done by Martinez and Brown [3]. Their findings highlight the need of transparency and accountability as guiding principles to eliminate any biases and guarantee that all students have an equal opportunity to study. This focus is consistent with the fairness and justice guiding principles for ethical AI development [3]. By focusing on the need for educators to promote digital citizenship alongside the incorporation of AI, Thompson et al. [6] expanded the conversation to include the wider ethical landscape. Their research promotes a strategy that gives students the tools to critically evaluate AI-enhanced content, giving them the capacity to identify moral dilemmas and come to responsible conclusions as online citizens [6].

Artificial Intelligence in the Context of Digital Learning Environments …

99

In-depth research on the complexities of data privacy and student permission in AIdriven educational environments was conducted by Roberts and Johnson in 2021. Given the sensitive nature of the data AI algorithms evaluate to customize learning experiences, their research emphasizes the necessity to obtain informed consent and safeguard student data [7]. Collectively, these studies highlight the crucial part that ethical considerations play in the incorporation of AI into education. Scholars and educators contribute to the responsible adoption of AI-enhanced learning by addressing concerns like bias mitigation, digital citizenship education, and data privacy, ensuring that technology is in line with moral principles and serves the interests of students.

2.3

Student Perspectives on AI-Powered Adaptive Learning

To evaluate the effectiveness and impact of this novel technique, it is critical to comprehend the experiences and viewpoints of students in the context of AI- powered adaptive learning. This section explores the contributions to research that shed light on the wide range of viewpoints held by students on AI-driven adaptability within their educational journeys. An empirical examination into how students view adaptive learning platforms powered by AI was done by Chen et al. in 2021. Their research showed that the alignment between adaptive recommendations and personal learning preferences has a substantial impact on student happiness and perceived learning effectiveness. This result highlights how crucial individualized material delivery is for raising student engagement and improving academic results [8]. Garcia et al. [5] investigated the complex relationships between students and AI-driven adaptive learning systems. Students love tailored learning experiences made possible by AI algorithms, but they still value human engagement and supervision from teachers, according to their research. This contrast draws attention to the complimentary roles that AI and teachers play in creating comprehensive and efficient learning environments [5]. Smith and Johnson [2] also investigated how AI-powered adaptation affected student motivation and self-efficacy. Students who felt that the adaptive learning system met their needs, according to their research, reported higher levels of self-efficacy and a greater sense of control over their learning trajectory. This increased agency connected favorably with drive and a pro-active approach to learning. Collectively, these incisive research highlight the value of taking into account student viewpoints when developing and implementing adaptive learning systems powered by

100

I. Elimadi et al.

AI. Teachers and developers may improve and optimize AI-driven interventions to better meet students’ needs and preferences by listening to their input. This will provide a collaborative and meaningful educational experience. 4: Professors’ Roles in AI-Powered Digital Learning Environments As technology advances, the incorporation of artificial intelligence (AI) into different industries, including education, has become more common. The usage of AI-powered digital learning environments has spurred debate regarding the possible revolutionary impact on professorial and educational responsibilities. This study of the literature sheds light on the changing attitudes and expectations of academics’ duties in the context of AI-powered digital learning environments. Shifting from Traditional Instruction to Facilitation: According to several research, AIpowered digital learning environments are likely to cause a shift in academics’ traditional roles from being primarily teachers to being facilitators of learning. Professors may be able to spend more on coaching and mentoring students if AI can give individualized curriculum, measure student progress, and provide real-time feedback [9]. Emphasis on Content Curation and Customization: The literature emphasizes the possibility for academics to serve as content curators and customizers in AI-enhanced learning settings. Professors may be responsible for selecting, customizing, and contextualizing resources to supplement AI-driven content delivery now that AI algorithms are capable of tailoring learning materials to particular student needs [10]. Higher-Order Cognitive Skills Development: AI-powered technologies can handle mundane duties like grading and information dissemination, freeing up instructors’ time to focus on developing higher-order cognitive abilities, critical thinking, problem-solving, and creativity [11]. Professors may create learning experiences that require students to complete challenging, application-oriented assignments that need analytical and creative ability.

3

Methodology

A digital survey emerged as the primary strategy for data collecting in this investigation. The goal of this survey was to explore into students’ perspectives and attitudes regarding adaptive learning powered by artificial intelligence (AI). The study’s sample pool included 40 students from the Ben M’Sick Faculty of Sciences at the University Hassan II in Casablanca, Morocco. The study used this strategy to gather useful insights into the students’ viewpoints and experiences using AI-powered adaptive learning. The questionnaire was created to provide a thorough grasp of their views and opinions toward this novel educational strategy.

Artificial Intelligence in the Context of Digital Learning Environments …

3.1

101

Data Collection

The major technique of data collection for this study was an online questionnaire. The purpose of the survey was to understand more about how students felt about adaptive learning that is powered by AI. 40 students from the university Hassan II in Casablanca, Morocco’s Ben M’Sick Faculty of Sciences made up the sample. https://docs.google.com/forms/d/1w_yjOMSGo7TvhUjo6xIxvUFe4p1U50DvSH2 17jF-Gg4/prefill.

3.2

Questionnaire Design

Participants were able to give structured comments on their experiences and perceptions by using a combination of closed-ended and yes/no questions in the questionnaire. The questions were created to ask about a variety of topics, such as how adaptive recommendations match up with personal learning preferences, how motivation and self-efficacy are thought to be impacted, and the need of human connection in AI-driven adaptive learning environments. Students’ perceptions and pertinent research on adaptable learning were used to help create the questionnaire.

3.3

Data Collection Procedure

Participants were invited to take part in the study by email invitations that included a link to the online questionnaire. The email provided a brief explanation of the study’s goal, promises of confidentiality, and an estimate of the time required to complete the questionnaire. Participants were assured of their participation’s voluntariness and the secrecy of their responses. Because of summer vacation, the data collection period lasted three weeks, during which participants were urged to complete the questionnaire at their ease.

3.4

Data Analysis

After the data collection period was completed, the collected replies were subjected to quantitative analysis. Descriptive statistics were used to summarize the demographic information of the participants, as well as their replies to closed ended questions and YES/ NO questions. The research sought to discover trends, patterns, and correlations within the dataset, thereby providing insights into students’ attitudes toward AI-powered adaptive learning. The students were chosen at random and comprised 40 students. The sample is depicted in Fig. 1.

102

I. Elimadi et al.

Fig. 1 Sample of students

A balanced gender distribution among the survey respondents—50% men and 50% women—can be seen in the sample. This gender parity ensures a variety of viewpoints and provides a thorough reflection of attitudes toward the inclusion of artificial intelligence in digital learning environments. The responses from the participants shed important light on the general expectations, apprehensions, and beliefs surrounding AI’s potential effects on education. The balanced representation, which captures a comprehensive view of opinions from both male and female perspectives, improves the validity and relevance of the findings (see Fig. 2). The responses to the topic on artificial intelligence (AI) in education and digital learning settings provide some fascinating findings. It is clear that the majority of the participants are at least somewhat familiar with the notion. 60% of respondents said they are “somewhat familiar” with AI in education and digital learning environments. This shows that a sizable proportion of those polled had a rudimentary comprehension or awareness of AI’s role in improving educational procedures. In addition, 30% of those polled said they were “very familiar” with AI in education and digital learning environments. This sample of respondents is likely to have a better

Fig. 2 Artificial intelligence (AI) in education and digital learning

Artificial Intelligence in the Context of Digital Learning Environments …

103

Fig. 3 Artificial intelligence improve student learning

understanding of how AI technologies are interwoven into many areas of modern learning environments, potentially emphasizing their awareness of AI’s potential benefits and applications. In contrast, 10% of interviewees answered that they are “unacquainted” with AI in this context. Although this is the smallest section of respondents, it represents a portion of the questioned population that may require extra instruction or information to overcome the knowledge gap. According to the data, the majority of respondents are familiar with artificial intelligence in education and digital learning environments, with a sizable proportion claiming to be “somewhat familiar” or “very familiar.“ This implies that there is a growing understanding of AI’s role in reshaping the educational landscape, while a small group may benefit from additional exposure and education on the subject (see Fig. 3). The vast majority of participants (80% of those polled) agreed that artificial intelligence had the potential to improve student learning outcomes in digital learning settings. This shows that this group has a strong belief that AI technologies can positively contribute to the educational process by employing personalized learning methodologies, data-driven insights, and adaptable material delivery. In contrast, 17% of participants expressed a more cautious attitude by selecting “Maybe.” This subgroup appears to be receptive to the prospect of AI having an impact on student learning outcomes, but may have reservations or uncertainties about the extent to which it is successful. Their reaction could be motivated by a desire for further evidence or practical implementation examples before fully committing to a favorable position. A small fraction, 3%, voted “No,” reflecting a perception that artificial intelligence does not have the capacity to improve student learning outcomes in digital learning settings. This small number of participants is likely to be more cautious about AI’s efficacy in education, maybe due to concerns about overreliance on technology or potential implementation issues. The statistics show a general sense of optimism among those polled, with a sizable majority believing that artificial intelligence has the ability to improve student learning

104

I. Elimadi et al.

Fig. 4 Analysing data about learning behaviour

outcomes in digital learning environments. The inclusion of a “Maybe” response category, on the other hand, indicates a degree of ambiguity or caution, signaling a desire for additional insights and proof. A smaller proportion had a negative opinion, indicating that, while the majority is enthusiastic about AI’s involvement in education, there are still divergences within the polled population (see Fig. 4). A vast majority of participants, 72.5%, expressed comfort with the idea of AI gathering and analyzing data about their learning behavior in order to provide individualized recommendations or support. This sizable group of participants is likely to be open to adopting data-driven insights to improve their learning experiences, recognizing the potential benefits of personalised guidance and suggestions linked with their own learning patterns. Another significant group, accounting for 20% of respondents, chose the “Maybe” choice. This group looks to be open to the idea of AI-driven personalisation, but they may have reservations, questions, or concerns about the specifics of data gathering, utilization, and potential ramifications. A lower minority of participants, 7.5%, said emphatically “No” to AI gathering and evaluating their learning behavior data. This reaction could be motivated by worries about data privacy and security, or by a preference for more traditional learning methods that do not incorporate AI-based customisation. In summary, the findings show that the majority of those questioned are willing to accept AI-collected data for personalized recommendations or assistance in their learning journey. A sizable majority agrees with this approach, yet a sizable minority expresses reservations or a want for more information. A smaller proportion of respondents flatly oppose the idea, emphasizing the need of resolving data privacy concerns and communicating clearly about data usage in AI-enhanced learning environments (see Fig. 5). In the digital learning landscape, a substantial majority of participants, comprising 80.5% of respondents, strongly believe that educational institutions should embrace AI

Artificial Intelligence in the Context of Digital Learning Environments …

105

Fig. 5 Digital learning landscape

and adaptive learning. This sizable proportion of participants is likely aware of the potential benefits of combining these technologies to improve tailored learning experiences, promote efficient content distribution, and cover a wide range of learning demands. Furthermore, a significant proportion of respondents (17.1%) chose the “Maybe” choice. This group looks to be open to the idea of embracing AI and adaptive learning, although they may have reservations or doubts regarding its implementation or impact. Factors such as the usefulness of these technologies, prospective obstacles, or concerns about equal access may impact their response. A small percentage of participants, 2.4%, said emphatically “No” to educational institutions embracing AI and adaptive learning. This reaction could be due to a variety of circumstances, such as doubts about the usefulness of modern technologies, concerns about potential downsides, or a preference for more traditional instructional approaches. The statistics show a high preference among those polled for educational institutions to use AI and adaptive learning technologies in the digital learning landscape. While the majority believes in the benefits of these breakthroughs, a subgroup is interested but wants more information or assurance. A smaller percentage disagrees, highlighting the different viewpoints of the studied population on the integration of AI and adaptive learning in education (see Fig. 6). In order to continue the discussion about AI-powered digital learning, participants were asked their thoughts on the possible impact of AI applications on boosting learning outcomes in such environments. A large majority of participants, representing 72.5% of those polled, believe that AI applications have the potential to improve learning in digital learning settings. This sizable group of participants most likely sees AI as a beneficial tool for personalisation, adaptive learning, and data-driven insights that can help students have more productive learning experiences. Furthermore, a significant number of respondents, 25%, chose the “Maybe” choice. This group appears to recognize the potential of AI applications to promote learning, but

106

I. Elimadi et al.

Fig. 6 AI-powered digital learning

they may have doubts, uncertainties, or a want for further information concerning the efficacy of certain AI treatments. A smaller subset of participants, 2.5%, said emphatically “No” to the premise that AI applications can increase learning in digital learning settings. This reaction could be a result of skepticism about AI’s capabilities or concerns about the potential limitations or unforeseen consequences of its adoption. In summary, the data reveals that the majority of those polled are optimistic about the potential of AI technologies to improve learning in digital learning settings. While a large majority believes in the positive influence, a smaller percent-age is open to the concept but wants more information or evidence, and a very small section is dubious. This varied range of viewpoints reflects the continuous arguments and research surrounding the incorporation of AI in education (see Fig. 7). Participants were questioned about their perspectives on the role of educational institutions to handle potential ethical concerns related with AI deployment in the light of the rising integration of AI in digital learning environments.

Fig. 7 The role of educational institutions

Artificial Intelligence in the Context of Digital Learning Environments …

107

A substantial majority of participants, 82.9%, strongly believe that educational institutions should take steps to address any ethical concerns raised by the use of AI in digital learning settings. This sizable proportion of participants presumably understands the significance of upholding ethical standards, ensuring openness, and protecting learners’ interests and rights in technologically enhanced educational contexts. Furthermore, a significant segment, 14.6% of respondents, chose the “Maybe” choice. This group appears to recognize the need of addressing ethical concerns, but they may have doubts, uncertainties, or want more information regarding specific ethical challenges and potential mitigations in AI-powered learning environments. A smaller subset, representing 2.5% of participants, said “No” to educational institutions addressing potential ethical concerns presented by the use of AI in digital learning environments. This attitude may suggest a conviction that ethical considerations are less important or that existing processes are enough to deal with any ethical implications. In summary, the data reveals a widespread belief among those polled that educational institutions should take proactive steps to address any ethical concerns linked with the incorporation of AI in digital learning settings. While the majority emphasizes the importance of ethical issues, a smaller subset is receptive to the idea but wants more clarity, while a very small minority is opposed. This varied spectrum of viewpoints reflects the ongoing debate over the responsible and ethical use of AI in education (see Table 1). The table gives a brief overview of various artificial intelligence- and adaptive-based learning platforms and tools. These tools are employed for a variety of tasks, such as creating presentations, instructing English and other languages, offering digital marketing services, and facilitating online meetings. The table demonstrates how these technologies are used for various types of educational and professional interactions while highlighting Table 1 Digital learning platforms and tools using adaptive and AI methods

Platform/tool

Usage

ChatGPT

– Developing presentations

ChatGPT

– Teaching English language

ChatGPT

– Digital marketing services

OpenAI Platform

Digital marketing services

OpenAI Platform

Tools like “medjourny” and “ChatGPT”

ChatGPT 4 Scispace Elicit Google Meet

Online meetings

Zoom

Online meetings

Kahoot

108

I. Elimadi et al.

Table 2 Perspectives on AI integration in digital learning: insights and considerations Insight

Summary

Promising trend

AI is seen as a trend that could shape human thinking and lifestyle

Focus on learning enhancement

AI should enhance learning, not just reduce effort

Embracing AI in education

Educational institutions should proactively embrace AI

Caution with AI implementation

There should be limits on AI usage to avoid potential disasters

Adapting to AI

Teaching methods must adapt to AI’s presence with ethical considerations

Complementing, not replacing

AI is a tool to enhance education, not replace human teachers

the varied applications of AI and adaptive techniques in the field of digital education and learning (see Table 2). The table that is being presented contains a wide range of information about how AI is being incorporated into online learning environments. It displays a diverse range of viewpoints, from optimism about AI’s potential to transform education and human lifestyles to caution about its ethical and practical limitations. The general consensus places a strong emphasis on using AI as a tool to improve learning rather than just streamline effort. This assortment of viewpoints emphasizes the necessity of a well-balanced strategy for implementing AI, one that upholds the special value of human teachers while welcoming the advantages that technology can bring to the teaching and learning process.

4

Discussion

The topic of perceptions, potential advantages, and ethical considerations surrounding the incorporation of artificial intelligence (AI) into digital learning environments is thoroughly examined in the discussion. It starts by recognizing the evenly distributed gender among the survey respondents, demonstrating a diverse representation of points of view that strengthens the validity and inclusiveness of the findings. The analysis reveals that participants are noticeably familiar with AI, with a sizeable portion claiming some level of awareness. The context for examining the participants’ attitudes toward AI in education is provided by this basic understanding. The majority of respondents are upbeat about AI’s ability to improve student learning outcomes, which reflects a widespread belief in the technology’s potential to create individualized and adaptive learning environments. The discussion, though, is careful to address the nuances of this optimism. It reveals a subgroup that approaches the potential effects of AI on education with cautious optimism and waits for more concrete evidence or examples in the real world before fully embracing

Artificial Intelligence in the Context of Digital Learning Environments …

109

it. A smaller group also questions AI’s effectiveness, high-lighting the need to address any potential drawbacks and difficulties related to its application. The discussion develops a crucial theme of ethical considerations. Participants agreed overwhelmingly that educational institutions should take a lead in addressing ethical issues related to the use of AI. This position demonstrates a greater understanding of the need to guarantee accountability, transparency, and responsible use of AI technologies in educational settings. Participants’ comfort levels with AI-driven data analysis for individualized recommendations are also discussed. A majority of respondents say they are willing to adopt this strategy, showing a growing willingness to use data-driven insights to improve learning experiences. However, the existence of a subgroup that is unsure or uneasy indicates the significance of taking privacy and ethical concerns into account when putting such strategies into practice. A balanced and thorough exploration of attitudes toward AI integration in digital learning environments is provided by the discussion as a whole. It recognizes the tensions between innovation and moral considerations, optimism and caution, and the changing nature of education in the digital age. The discussion’s insights serve as an invaluable starting point for further discussion and decision-making regarding the ethical and successful integration of AI technologies in education.

5

Conclusion

In conclusion, there is a dynamic interplay between perspectives, options, and ethical considerations in the discussion surrounding the integration of artificial intelligence (AI) into digital learning environments. The survey population’s evenly distributed genders add depth and variety to the conclusions drawn, fostering a thorough understanding of attitudes toward the use of AI in education. Participants’ high level of AI familiarity underlines the growing understanding of how AI is changing educational paradigms. The discussion’s attitudes are crucially set against this important background of awareness. A positive outlook emerges as the dominant emotion, with a sizable majority of respondents believing that AI has the potential to improve student learning outcomes. This optimism reflects a widespread confidence in AI’s ability to provide individualized and flexible learning experiences, thereby transforming education in the digital era. However, there is a range of viewpoints contained in this optimism. Some people approach the potential effects of AI with cautious optimism, waiting for more conclusive proof before fully embracing its transformative potential. This reveals a healthy skepticism and a thoughtful strategy for integrating technology.

110

I. Elimadi et al.

The participants’ overwhelming consensus that educational institutions should take a proactive approach to addressing ethical concerns related to AI makes ethical considerations stand out prominently. This recognition of the importance of openness, responsibility, and responsible use emphasizes the dedication to utilizing AI’s potential for advancement while guarding against unintended consequences. Participants’ openness to adopting AI-driven data analysis for tailored recommendations demonstrates a growing willingness to use data insights to improve personalized learning experiences. While the majority is supportive, the presence of doubt or discomfort emphasizes the need for clear communication and strong privacy protections when putting these strategies into practice. The discussion as a whole captures a diverse range of perspectives that help to shape how AI is integrated into digital learning. It highlights how education is constantly changing, where optimism is based on careful thought, innovation is tempered by ethical awareness, and technology is used to improve the human learning experience. The knowledge shared in this discussion acts as a compass, directing the responsible and successful integration of AI technologies as education travels the path toward a future that is driven by technology. Education can take advantage of the opportunities presented by AI to empower students and educators alike by encouraging ongoing discussion, addressing concerns, and utilizing the potential of AI in conjunction with ethical considerations, ultimately enhancing the process of learning and discovery.

References 1. Johnson, A. R., & Wang, S. Y. (2018). AI’s role in enhancing motivation and self-regulation in education. International Journal of Artificial Intelligence in Education. 2. Smith, K. L., Johnson, A. R., Martinez, J. M., & Chen, H. Y. (2020). Dynamic learning materials and engagement in higher education: An AI-driven platform study. Computers & Education. 3. Martinez, J. M., & Brown, C. (2019). Ethical considerations in AI-powered education. Journal of Ethics in Education. 4. Brown, A., & Lee, B. (2022). Longitudinal study on the impact of adaptive learning interventions on student retention rates. Journal of Educational Research. 5. Garcia, R. A., Johnson, A. R., Martinez, J. M., & Chen, H. Y. (2019). Pedagogical changes and the role of educators in AI-driven adaptive learning. Journal of Educational Technology. 6. Thompson, R. J., Johnson, A. R., & Wang, L. J. (2020). Promoting digital citizenship alongside AI integration. Journal of Educational Technology. 7. Roberts, L., & Johnson, A. R. (2021). Data privacy and student consent in AI-driven educational environments. Journal of Educational Technology and Ethics.

Artificial Intelligence in the Context of Digital Learning Environments …

111

8. Chen, H. Y., Smith, E. R., Lee, M. H., & Wang, L. J. (2021). Student satisfaction and perceived learning effectiveness in AI-driven adaptive learning. Educational Technology Research and Development. 9. Anderson, A. (2017). The role of the instructor in e-learning. E-Learning Industry. https://ele arningindustry.com/role-of-the-instructor-in-elearning 10. Wang, F., & Hannafin, M. J. (2017). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development. 11. Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review.

A Methodology for Evaluating and Reporting the Integration of Artificial Intelligence for Sustainability in Higher Education: New Insights and Opportunities Yman Chemlal and Mohamed Azzouazi

Abstract

Implementing AI applications in Education for Sustainable Development (ESD) was a necessity as recommended by UNESCO, and fostered by Higher Education to leverage AI applications for innovative and engaging learning opportunities that promote critical thinking, resolving problems, cooperation, equipping students with the knowledge and abilities expected to create a more sustainable future. This research paper adds to the advancement of sustainability within higher education by providing insights into the assessment approaches for the incorporation of AI technologies. It achieves this by creating a matrix that links AI for the “Education for Sustainable Development (ESD)” area with the “Sustainability Tracking, Assessment & Rating System (STARS)”. The matrix and its metrics provide a structure for evaluating the progress and effectiveness of AI implementation in sustainability education. This study contributes to highlighting Artificial intelligence’s role in advancing sustainability within the education sector and offers insights into enhancing the impact of sustainability education through AI interventions. Keywords

Artificial intelligence . Sustainability . Education . Assessment

Y. Chemlal (B) · M. Azzouazi Laboratory LTIM, The Faculty of Sciences, Ben M’sik at the University Hassan II Casablanca, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_7

113

114

1

Y. Chemlal and M. Azzouazi

Introduction

A new educational paradigm called “Education for Sustainable Development (ESD)” [1] enables institutions to take the lead and address social and environmental issues [1]. It is an ongoing process that improves students’ cognitive, socio-emotional, and behavioral development [2]. ESD provides learners in various fields with the necessary knowledge, abilities, skills, and principles needed to actively work towards sustainable future goals [2]. By embedding ESD in higher education, universities can align with and support other contemporary agendas such as entrepreneurship, inclusivity, and decolonization. ESD empowers all individuals to gain the knowledge, competencies and values essential for shaping a sustainable future [3]. This involves integrating crucial sustainable development topics into education processes, encompassing subjects like “climate change, disaster risk reduction, biodiversity, poverty alleviation, and responsible consumption” [4]. ESD is crucial for attaining a sustainable society and is therefore recommended across all tiers of formal education and training, as well as in informal and non-formal learning settings [2]. The higher education sector plays a crucial role and bears a significant responsibility in safeguarding the planet, through their integration of ESD into the curriculum and their support of learners in addressing complex and identifying how they can contribute to solutions that address environmental integrity, social justice, and economic prosperity [4–6]. Concurrently, the integration of AI in ESD can create synergies and enable “higher education institutions to play a significant role in addressing sustainability challenges” [7–10]. AI represents a fundamental transformation in “Education for Sustainable Development (ESD)” [1], ushering in personalized learning, resource optimization, data analysis, decision support, and innovation [6, 11, 12]. Recognizing the impact of AI utilization on achieving sustainability objectives is vital, especially as institutions strive to enhance their sustainability and environmental accountability. Therefore, it is vital for scientists, teachers, and other interested parties to continue to exercise critical judgment when evaluating the standard and reliability of studies in this area. There aren’t many studies on the subject, therefore it’s difficult to assess how well sustainability measures led by AI are being incorporated into education. Therefore, present evaluation techniques for AI for ESD based on sustainability research may be ineffective since they don’t seem to sufficiently account for the dynamic and multifaceted character of AI technologies and their implications for sustainability [11, 13]. The domain of “AI for ESD” [1] is fast expanding, as stated by [9, 11, 14], and as our comprehension of this domain progresses, new assessment methods may emerge. Furthermore, the assessment of AI for ESD applications in the context of sustainability should encompass environmental, economic, and social aspects, demanding a comprehensive understanding of Sustainability initiatives powered by artificial intelligence. This study aims to bridge that gap by proposing an evaluation method for the utilization of AI for ESD within the higher education sector. This study seeks to fill that gap and suggests an evaluation technique for the use of “AI for ESD in the higher education sector” [1]. The primary objectives of this study are twofold. Firstly,

A Methodology for Evaluating and Reporting the Integration …

115

it aims to critically assess and analyze the current evaluation techniques employed for appraising sustainability in higher education. Secondly, it seeks to identify and propose novel evaluation methods that consider the unique characteristics and intricacies of AI systems within the sustainability context. These evaluation methodologies are crucial for making informed decisions, implementing policies, and continuously enhancing AI-driven sustainability initiatives in higher education institutions. The subsequent sections of this paper are structured as follows: Section 2 examines the existing Sustainability Assessment Tools (SAT) and their limitations. Section 3 outlines the methodology used to propose innovative evaluation approaches. Section 4 delves into the analysis of findings and presents the results. Section 5 discusses the implications and potential opportunities presented by these methods. Finally, Sect. 6 brings the paper to a close by summarizing the main findings, emphasizing their importance, and offering directions for future research.

2

Assessment Tools for Sustainability in Higher Education

“Assessment Tools for Sustainability (SAT)” [15] and reports need four main categories: “education, research, operations, and community engagement” [15] to evaluate incorporating sustainability values in Higher Education Institutions’ mission and practice, and SAT must be integrated, evaluated, and reported by considering all stakeholders [15] to create sustainable strategies [16]. However, selecting the Assessment Tools for Sustainability is the first step in reporting sustainability metrics and indicators in HEI to educate internal and external stakeholders as well as to analyze the steps being taken to integrate sustainability culture [7]. Moreover, it is crucial to identify and establish relevant indicators to conduct monitoring that can help HEIs adopt scalable success strategies [17]. Currently, there are three basic ways to evaluate sustainability in each SAT: account evaluation, narrative assessment, and indicator-based assessment [18]. This research primarily concentrated on the examination of assessment tools based on indicators, [18] highlight that those tools are more complete, credible, and typical than other assessment methods due to their quantifiable characteristics. Thus, the crucial question of sustainable practices within higher education is what aspects of tools for assessing sustainability are addressing and assessing. Reference [19] calculated the indicators for each sustainability assessment tool by grouping them across the four domains: “education, research, operational aspects, and community involvement” [15]. Tools “Auditing Instrument for Sustainability in Higher Education (AISHE)” [20] and “Alternative Universal Appraisal (AUA)” [21] have a high percentage of indicators and standards within the domain of education, whereas tools “Campus Sustainability Assessment Framework (CSAF)” [22] has almost 80% in the field of operations [19]. furthermore, two reporting tools should be emphasized: (a) the “Global Reporting Initiative (GRI)” [23], and (b) the “Sustainability Tracking, Assessment & Rating System™

116

Y. Chemlal and M. Azzouazi

STARS” [24] a self-assessment framework for (HEIs) to evaluate their own performance in terms of sustainability. On the one hand, GRI stands as the most widely recognized sustainability reporting instrument for businesses [25]. Adapting the GRI framework for universities, [26] developed the GASU tool “Graphical Assessment of Sustainability in Universities” [26] which visualizes sustainability initiatives in a graphical format, and combined the three other dimensions (economic, environmental, and social) with a suggested set of indicators for the educational dimension. A few performance measures have also been suggested by [26] for the GRI standards’ educational component. However, he simply considered the two categories of curriculum and research. On the other hand, A framework for promoting sustainability including benchmarks and objectives is provided by the STARS sustainability rating system for HEIs [27]. It is a chart that enables colleges and universities to transparently self-report. Higher education institutions can analyze their sustainability using the comprehensive framework provided by STARS, which also allows for internal and external benchmarking, awards ongoing success, strengthens campus sustainability cultures, and disseminates sustainability best practices [28]. STARS offers an all-encompassing structure for evaluating sustainability performance in higher education institutions, encompassing various areas including curriculum, research, campus operations, and community engagement. In conclusion, the complexity of the indicator is the key distinction between them; whilst GRI offers simple indicators, the STARS suggests composite indicators that call for substantial measurement time and resources [6]. In the reviewed literature, sustainability assessment frameworks, may not adequately address the unique aspects of AI applications in the context of ESD. Existing frameworks often lack specific indicators and metrics to assess the integration of AI technologies to enhance ESD in HEIs. Subsequently, to address this gap of AI applications in the context of ESD, this study proposes a method that can provide guidance for universities to effectively integrate AI technologies, evaluate their sustainability impact, and ensure ethical and equitable use for enhancing biodiversity and sustainability in education and research. This methodology will be based on STARS as a complete tool that encompasses various areas including curriculum, research, campus operations, and community engagement.

3

Methodology

The approach to attain the goals has been followed in different steps shown in Fig. 1: – Review the STARS framework and identify the relevant dimensions or sub-dimensions that characterize the HEI missions. – Define the specific AI applications for ESD areas by reviewing relevant studies.

A Methodology for Evaluating and Reporting the Integration …

117

Fig. 1 Methodology

– Mapping STARS indicators with the corresponding AI applications for ESD are-as based on their alignment and relevance. Consider the goals and objectives of each indicator and match them with the AI applications for ESD areas that address similar sustainability aspects. – Determine Metrics: Identify appropriate metrics or measures for each mapped pair of STARS indicators and AI applications for ESD areas. These metrics should capture the key elements and dimensions of sustainability and AI for ESD. Consider both quantitative and qualitative metrics that can effectively assess and represent the de-sired aspects.

3.1

Identify STARS Indicators

“The Association for the Advancement of Sustainability in Higher Education (AASHE)” [29] created the “Sustainability Tracking, Assessment and Rating System (STARS)” [29]. For the categories of Scholarly activities, involvement, operational roles, strategic planning and management, innovation, and leadership. After the review of Stars Technical Manual, the Academics dimension and Innovation and Leader-ship dimension have been selected in this study, which considers that the university acts as a catalyst and center for innovation, achieved through education and learning, research, and the dissemination of knowledge [30] And that because higher education institutions (HEI) have a pivotal role in societal transformation through their ability to provide a potent “shadow curriculum” to future generations of citizens and leaders. Academic dimension: The academic dimension is composed of two sub-dimensions: curriculum and re-search. Tables 1 and 2 show curriculum and research sub-dimensions and their indicators as described in [24]. A. Curriculum

118

Y. Chemlal and M. Azzouazi

Table 1 Curriculum dimension in STARS [24] AC1. Sustainability course offerings

AC.1.1. “Percentage of academic departments with sustainability course offerings” AC.1.2. “Published sustainability course listings” AC.1.3. “Support for academic employees to integrate sustainability into the curriculum”

AC2. Undergraduate programs

AC.2.1. “Institutional sustainability learning outcomes for undergraduate students” AC.2.2. “Undergraduate programs with sustainability-focused learning requirements” AC.2.3. “Percentage of undergraduate qualifications awarded that have sustainability-focused learning requirements “

AC3. Graduate programs

AC.3.1. “Graduate programs with sustainability-focused learning requirements “ AC.3.2. “Percentage of graduate qualifications awarded that have sustainability-focused learning requirements”

AC4. Applied learning for sustainability

AC.4.1. “Applied learning for sustainability program”

AC5. Sustainability literacy assessment

AC.5.1. “Sustainability literacy assessment design and administration”

Table 2 Research dimension in STARS [24] AR1. Sustainability research

AR.1.1. “Percentage of academic departments engaged in sustainability research” AR.1.2. “Incentives for sustainability research”

AR2. Center for sustainability research

AR.2.1. “Organized sustainability research center, institute”

AR3. Responsible research and innovation

AR.3.1. “Published ethical code of conduct for research” AR.3.2. “Recognition of integrated, community-based, and extra-academic research” AR.3.3. “Inter-campus collaboration for responsible research and innovation” AR.3.4. “Support for open access publishing”

A Methodology for Evaluating and Reporting the Integration …

119

Table 3 Innovation and leadership dimension in STARS [24] IL-1 Sustainability research centers and institutes

IL.1.1 “Presence of dedicated research centers or institutes focused on sustainability.”

IL-2 Sustainability-focused staff positions

IL.2.1 “Number of staff positions dedicated to sustainability initiatives and programs.”

IL-3 Sustainability awards and certifications

IL.3.1 “Recognition and certifications received for sustainability efforts and achievements.”

IL-4 Sustainability innovation projects

IL.4.1 “Number and scope of projects that showcase innovative approaches to sustainability.”

IL-5 Collaboration on sustainability initiatives

IL.5.1 “Level of collaboration with external stakeholders, such as industry partners, government agencies, and community organizations, on sustainability initiatives.”

B. Research Table 3 shows innovation and leadership sub-dimensions and their indica-tors as described in STARS [24].

3.2

AI for ESD Areas

Only articles relevant to strategies that were adapted by HEIs to integrate AI for sustainability were selected for this review, and they are classified into two domains: education and research. . AI for sustainability in education: can be grouped into five main strategies: 1. Incorporating sustainability into curriculum development: educational studies emphasize the importance of including AI in the curriculum to develop sustainable AI technologies [10, 11, 13, 14]. The objective is to develop a new generation of AI experts. who prioritize environmental and social impact while driving sustainable innovation. And by (1) incorporating sustainability into AI courses, focusing on theoretical foundations, practical applications, and ethical considerations, such as “Massachusetts Institute of Technology”, “Harvard”, and “Carnegie Mellon” have developed programs or courses that center on the examination of ethical issues and effects of AI [31]. (2) Collaborations focus on developing AI models and tools for sustainable development, addressing climate change, renewable energy, and resource optimization. (3) Industry partnerships, such as the AI Sustainability

120

2.

3.

4.

5.

Y. Chemlal and M. Azzouazi

Center between IBM and MIT, drive real-world applications of AI for sustainability (http://Home-MIT-IBM Watson AI Lab). Creating AI-powered sustainability projects and initiatives for students: to encourage students to take part in sustainable behaviors by using AI in practical ways [31]. According to [4] as part of these initiatives, practical and hands-on learning experiences are promoted that advance sustainability while taking into account moral values such as privacy, openness, and fairness. Furthermore, AI can be used in sustainability initiatives to develop complete and effective solutions that address ecological and socioeconomic sustainability, in accordance with Sustainable Development Goals (SDG), and advance global awareness [4]. Utilizing AI-powered platforms and tools for student engagement and collaboration: by incorporating AI technologies into sustainable learning practices, students can benefit from personalized learning experiences, increased engagement, and enhanced collaboration [32] As it is stated by [10, 15] These technologies can enhance learning outcomes, promote critical thinking, and provide students with the abilities required to thrive in a rapidly evolving digital world. AI-powered platforms enable the use of digital learning resources, virtual classrooms, and online collaboration tools, reducing paper waste and minimizing the environmental impact associated with physical resources and commuting [1], as well as promoting effective communication and language skills, making educational content more accessible to students from a variety of backgrounds [15]. Additionally, these technologies facilitate adaptive learning by offering evaluations and content that are customized to the individual requirements and learning inclinations of each student [1, 15]. Moreover, AI can assist intelligent tutoring systems that give students tailored feedback, direction, and support, improving their learning experience [1, 15]. Encouraging interdisciplinary collaboration between AI and sustainability departments: Collaboration between disciplines can facilitate a positive exchange of knowledge, skill, and viewpoints to support creative, sustainable solutions. Interdisciplinary collaboration also broadens students’ experiences and helps them get ready for the workforce [19]. For example, Northwestern University provides a comprehensive AI curriculum that explores the ethical, social, and economic implications of AI technologies. Subsequently, Students can learn about these topics as well as the potential societal repercussions, biases, and privacy issues raised by AI technology (https://ai.northwestern.edu/education/index.html). Sustainable Assessment: UNESCO recommends developing criteria for sustainable assessment that take into account sustainability’s social, environmental, and economic facets [4, 33]. Argue that this assessment strategy allows for the evaluation of student work in terms of its overall sustainability impact, fostering a culture of responsible and meaningful learning. As emphasized by [16] integrating AI-powered assessment and feedback mechanisms into sustainability initiatives

A Methodology for Evaluating and Reporting the Integration …

121

improves assessment by providing effective and personalized evaluations of student achievement. Moreover, through AI, students benefit from prompt feedback and guidance, enabling them to reflect on their work and make significant advancements [7, 17, 18]. . AI for sustainability in research: can be divided into four categories: 1. Encouraging collaboration among researchers from different disciplines and institutions: Such collaborations hold the capacity to produce pioneering research, holistic approaches to problem-solving, and profound solutions to intricate challenges [15]. For example, Stanford University established the Institute for Human-Centered Artificial Intelligence (HAI) to facilitate collaboration among experts from various disciplines including economics, philosophy, ethics, psychology, and more (source: https://hai.stanford.edu/). Also, NVIDIA, a leading technology company, collaborated with Stanford University on the AI for Earth program to enable Stanford researchers to create AI-based solutions for environmental concerns including “climate change, biodiversity conservation, and natural resource management”, this collaboration will provide financing, technical support, and access to NVIDIA’s AI technology. (Higher Education and Research | NVIDIA Developer), Academic Collaborations | NVIDIA Research 2. Incorporating ethical considerations and social responsibility into research practices: Some studies emphasize incorporating ethical issues and social responsibility into research processes by promoting a multidisciplinary committee that evaluates the potential risks, advantages, and ethical implications of the research [25, 28], by ensuring that Data protection measures be taken to secure sensitive [34], by using ethical methods while gathering, examining, and reporting data [34]. And also by involving all relevant parties—including communities, organizations, decision-makers, and affected people- during the study process [10, 34]. 3. Encouraging industry partnerships and collaborations for real-world application of AI for sustainability: According to [4, 6] students obtain the capacity to translate theoretical principles into practical solutions by focusing on projects, case studies, and real-world applications, equipping them to build practical abilities in applying AI concepts to real-world situations. As an important example of this collaboration, IBM and Massachusetts Institute of Technology (MIT) jointly founded the AI Sustainability Center to address sustainability concerns through the application of AI and data science, serving as a significant example of the collaboration between academia and industry collaborations for real-world applications. IBM and MIT collaborated to develop AI models, algorithms, and tools to support sustainable development, facilitating knowledge exchange between industry experts and academic researchers (http://Home-MIT-IBM Watson AI Lab).

122

Y. Chemlal and M. Azzouazi

By analyzing recent research about AI for ESD, four key areas were revealed To evaluate the integration of AI applications for “Education for Sustainable Development (ESD)” [1]: Curriculum Integration, Research and Innovation, Ethical Considerations and Interdisciplinary Collaboration.

3.3

Mapping Process

The process of mapping AI for ESD areas with STARS subdimensions involves identifying the key areas of AI for ESD and aligning them with sustainability indicators provided by the STARS framework. The mapping process started by examining one by one each AI for the ESD area. For example, “Curriculum Integration” focuses on integrating sustainability principles and AI applications into academic programs. Then, the STARS framework is reviewed to identify the most relevant and appropriate subdimension to each AI for the ESD area examined. For example, within the STARS subdimension “Curriculum integration,” STARS subdimensions as “Sustainability Course Offerings” and “Undergraduate Programs” are identified. Table 4 shows the outcomes of the mapping procedure: Table 4 Result of Mapping AI for ESD areas

STARS subdimensions

– Curriculum Integration

AC1. Sustainability Course Offerings AC2. Undergraduate Programs AC3. Graduate Programs AC4. Applied Learning for Sustainability AC5. Sustainability Literacy Assessment

– Research and Innovation

AR1. Sustainability Research AR2. Center for Sustainability Research AR3. Responsible Research and Innovation IL-1 Sustainability research centers and institutes IL-4 Sustainability innovation projects

– Ethical Considerations

AR3. Responsible Research and Innovation

– Interdisciplinary Collaboration

AR3. Responsible Research and Innovation IL-5 Collaboration on sustainability initiatives

A Methodology for Evaluating and Reporting the Integration …

3.4

123

Matrix Presentation

Following the mapping process, the relevant metrics are then identified for each combination of AI for the ESD area and STARS subdimension. These metrics should effectively measure the progress or impact of implementing AI for sustainability in education. The metrics can be qualitative or quantitative, depending on the nature of the evaluation. The identified metrics are incorporated into the matrix by adding an additional column. Each metric is aligned with the corresponding combination of AI for ESD area and STARS subdimension. This allows for a clear and organized presentation of the relevant metrics in relation to the mapped areas and subdimensions (as shown in Table 5).

4

Result

A finding of this study is a matrix generated in the previous sections that allow HEIs to evaluate the integration of AI applications for “Education for Sustainable Development (ESD)” [1] by using significant indicators and metrics. These indicators serve as the basis for evaluating the integration of artificial intelligence for promoting sustainability in the domain of education. – Concerning curriculum integration, the sustainability course offerings (AC1) indicator was an important metric to measure courses dedicated to sustainability. However, Increasing the presence of sustainability-focused learning outcomes to enhance the sustainability education experience could be fostered by integrating sustainability into undergraduate (AC2) and graduate (AC3) programs. The applied learning for sustainability (AC4) subdimension also shows promising results, by measuring HEIs engagement in applied projects focused on sustainability. This highlights the importance of hands-on experiences for students. – In research and innovation, it is significant to measure the number of sustainability research projects (AR1) and dedicated centers/institutes for sustainability re-search (AR2). This evaluates a growing interest and commitment to advancing AI-driven solutions for sustainability. Additionally, the incorporation of ethical considerations (AR3) in research projects reflects a responsible approach to AI im-plementation. – In the Interdisciplinary collaboration area, the (AR3 and IL-5) emerge as keys in-dicators in implementing AI for ESD to measure promoting interdisciplinary col-laboration, ethics, and social responsibility. – In the ethics considerations area, indicator (AR3) appears the most appropriate to measure transparent, fairness, and accountability in AI applications for sustaina-bility in education.

124

Y. Chemlal and M. Azzouazi

Table 5 Matrix of evaluating the implementation of “AI for ESD” AI for ESD areas

STARS subdimensions

Relevant metrics

– Curriculum Integration

AC1. Sustainability course offerings

– Number of AI-based sustainability courses or modules developed – Incorporation of AI concepts and applications in sustainability curriculum

AC2. Undergraduate programs

– Integration of AI applications for sustainability in undergraduate programs

AC3. Graduate programs

Integration of AI applications for sustainability in graduate programs

AC4. Applied learning for sustainability

Inclusion of AI-driven projects or initiatives in applied learning for sustainability

AC5. Sustainability literacy assessment

Assessment results and improvement measures related to AI knowledge and applications

– Research and Innovation

AR1. Sustainability research – Number of research projects focused on AI for sustainability – Publications and citations of AI-driven sustainability research – External funding for AI-driven sustainability research projects AR2. Center for sustainability research

– Number of established sustainability research centers or institutes with focus on AI applications – Collaborations between AI and sustainability research centers or institutes (continued)

A Methodology for Evaluating and Reporting the Integration …

125

Table 5 (continued) AI for ESD areas

– Ethical Considerations

STARS subdimensions

Relevant metrics

AR3. Responsible research and innovation

Implementation of ethical guidelines for AI research and development in sustainability – Integration of AI models and algorithms for biodiversity conservation, ecological forecasting, etc – Impact and innovation potential of AI-driven sustainability research projects

IL-1 Sustainability research centers and institutes

– Number of sustainability research centers or institutes focusing on AI applications – Scope of research activities and impact related to AI for sustainability

IL-4 Sustainability innovation projects

– Number of sustainability innovation projects incorporating AI – Impact and scalability of AI-driven innovation projects

AR3. Responsible research and innovation

– Adoption of ethical frameworks specifically for AI applications in sustainability – Ethical considerations in AI algorithm development and deployment for sustainability – Mitigation of biases and fairness concerns in AI-driven sustainability projects (continued)

126

Y. Chemlal and M. Azzouazi

Table 5 (continued) AI for ESD areas

STARS subdimensions

Relevant metrics

– Interdisciplinary Collaboration

AR3. Responsible research and innovation

– Collaboration between computer scientists, ecologists, environmental scientists, ethicists, and social scientists on AI-driven sustainability projects – Interdisciplinary conferences, workshops, or research initiatives focusing on AI for sustainability

IL-5 Collaboration on sustainability initiatives

– Number of collaborations with external organizations and stakeholders on AI-driven sustainability initiatives – Impact and outcomes of collaborative AI projects for sustainability

Figure 2 shows how AI for ESD and aligning them with the relevant subdimensions of the STARS framework.

Fig. 2 Map of AI for ESD and subdimensions of the STARS

A Methodology for Evaluating and Reporting the Integration …

5

127

Discussion

The matrix serves as a tool to evaluate implementing AI for ESD in HEIs, as well as providing valuable insights into the relationship between AI for ESD areas and learning outcomes in sustainability education. Subsequently, the matrix and its associated metrics act as a structure for assessing and analyzing the performance of higher education institutions (HEIs). can design effective strategies to enhance learning outcomes in sustainability, promoting knowledge acquisition, skill development, and the cultivation of values aligned with sustainability principles (as shown in Fig. 3) the matrix highlights the importance of: – The integration curriculum area: this area allows for assessment of the development of knowledge acquisition, critical thinking, collaboration skills, problem-solving skills and a feeling of societal and environmental accountability among students by integrating (AC2), (AC4), and (AC5). This suggests that integrating sustainability content into undergraduate programs and incorporating applied learning experiences have been successful in fostering the growth of understanding, competencies, and attitudes associated with sustainability. The research and innovation areas: integrating (AR1) Sustainability Research indicates that are hands-on in sustainability and promoting research and innovation in the domain of AI for sustainability develops students’ research skills and encourages them to explore innovative approaches to address sustainability challenges. – The Ethical Considerations area: selected indicators of (AR3) in the matrix lead to evaluating global and cultural awareness, ethics, and social responsibility among students. Fig. 3 Student skills

128

Y. Chemlal and M. Azzouazi

– The interdisciplinary collaboration area: the indicators chosen in this area especially for (IL5) assess communication and collaboration skills and also diversified knowledge and values about AI ethics and transparency.

6

Conclusion

The primary objective of this study is to develop a framework to evaluate how HEIs integrate AI for ESD, structured in the form of a matrix. To develop this matrix, a process of mapping has been followed to align AI for ESD areas (curriculum integration, research, innovation, ethics considerations, and interdisciplinary collaboration), as brought out of the literature review, with STARS subdimensions and incorporating relevant metrics. The curriculum integration has been aligned with sustainability course offerings, undergraduate and graduate programs, and applied learning for sustainability STARS subdimensions, to evaluate that HEIs are actively incorporating AI applications for sustainability into their academic programs, offering students the chance to cultivate expertise, competencies, and principles associated with sustainability. Another important area is ethical considerations in AI for sustainability in which the indicators related to responsible research and innovation and ethical decision-making frameworks demonstrate that researchers and educators recognize the ethical implications of AI applications and are taking steps to ensure responsible and accountable use of AI technologies in sustainability education. However, it is crucial to recognize the constraints and the limitations of the matrix, and the metrics used. The identified metrics are based on existing frameworks and literature, but they may not capture the full complexity of AI for sustainability in education. Further refinement and validation of the matrix and metrics are necessary to ensure a comprehensive and accurate evaluation of AI implementation. A future research project should explore AI’s specific impact on learning outcomes in sustainability education. The effectiveness of AI interventions for developing knowledge, competencies, and attitudes pertaining to sustainability can be assessed with longitudinal studies.

References 1. Tanveer, M., Hassan, S., & Bhaumik, A. (2020). Academic policy regarding sustainability and artificial intelligence (AI). Sustainability, 12(22), 9435. https://doi.org/10.3390/su12229435 2. Marouli, C. (2021). Sustainability education for the future? Challenges and implications for education and pedagogy in the 21st Century. Sustainability, 13(5), 2901. https://doi.org/10.3390/su1 3052901

A Methodology for Evaluating and Reporting the Integration …

129

3. Sung, J. H., & Choi, J. E. (2022). The challenging and transformative implications of education for sustainable development: A case study in South Korea. JCSR, 4(2), 1–14. https://doi.org/10. 46303/jcsr.2022.8 4. Paulauskaite-Taraseviciene, A., Lagzdinyte-Budnike, I., Gaiziuniene, L., Sukacke, V., & Daniuseviciute-Brazaite, L. (2022). Assessing education for sustainable development in engineering study programs: A case of ai ecosystem creation. Sustainability, 14(3), 1702. https:// doi.org/10.3390/su14031702 5. UNESCO. (2017). Education for Sustainable Development Goals. https://www.unesco.org/en/ articles/education-sustainable-development-goals-learning-objectives 6. Southworth, J. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127 7. Ramísio, P. J., Pinto, L. M. C., Gouveia, N., Costa, H., & Arezes, D. (2019). Sustainability strategy in higher education institutions: Lessons learned from a nine-year case study. Journal of Cleaner Production, 222, 300–309. https://doi.org/10.1016/j.jclepro.2019.02.257 8. García-Feijoo, M., Eizaguirre, A., & Rica-Aspiunza, A. (2020). Systematic review of sustainable-development-goal deployment in business schools. Sustainability, 12(1), 440. https://doi.org/10.3390/su12010440 9. Yu, J. (2021). Academic performance prediction method of online education using random forest algorithm and artificial intelligence methods. International Journal of Emerging Technologies in Learning, 16(05), 45. https://doi.org/10.3991/ijet.v16i05.20297 10. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/ j.caeai.2022.100118 11. Lampos, V., Mintz, J., & Qu, X. (2021). An artificial intelligence approach for selecting effective teacher communication strategies in autism education. npj Science of Learning, 6(1), 25. https:// doi.org/10.1038/s41539-021-00102-x 12. Jing, X., Zhu, R., Lin, J., Yu, B., & Lu, M. (2022). Education sustainability for intelligent manufacturing in the context of the new generation of artificial intelligence. Sustainability, 14(21), 14148. https://doi.org/10.3390/su142114148 13. Akmexe, O. F., KⰓop, X., & Epbeℌ, X. (2021). Use of machine learning techniques for the forecast of student achievement in higher education. ITLT, 82(2), 297–311. https://doi.org/10. 33407/itlt.v82i2.4178 14. Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527–1547. https://doi.org/10.1007/s10639-020-103 16-y 15. Quist, J., & Tukker A. (2013). Knowledge collaboration and learning for sustainable innovation and consumption: Introduction to the ERSCP portion of this special volume. Journal of Cleaner Production, 48, 167–175. https://doi.org/10.1016/j.jclepro.2013.03.051 16. Lozano, R. (2015). A review of commitment and implementation of sustainable development in higher education: Results from a worldwide survey. Journal of Cleaner Production, 108, 1–18. https://doi.org/10.1016/j.jclepro.2014.09.048 17. Sonetti, G., Lombardi, P., & Chelleri, L. (2016). True green and sustainable university campuses? Toward a clusters approach. Sustainability, 8(1), 83. https://doi.org/10.3390/su8010083 18. Dawodu, A. (2022). Campus sustainability research: Indicators and dimensions to consider for the design and assessment of a sustainable campus. Heliyon, 8(12), e11864. https://doi.org/10. 1016/j.heliyon.2022.e11864

130

Y. Chemlal and M. Azzouazi

19. Fischer, D., Jenssen, S., & Tappeser, V. (2015). Getting an empirical hold of the sustainable university: A comparative analysis of evaluation frameworks across 12 contemporary sustainability assessment tools. Assessment & Evaluation in Higher Education, 40(6), 785–800. https://doi. org/10.1080/02602938.2015.1043234 20. Roorda, N., Rammel, C., Waara, S., & Paleo, U. F. (2009). Assessment instrument for sustainability in higher education (2nd ed.). http://www.eauc.org.uk/theplatform/aishe 21. ProSPER.Net. (2010). Alternative University Appraisal Model for ESD in Higher Education Institutions. http://www.Sustain.Hokudai.Ac.Jp/Aua/En/WpContent/Uploads/2010/03/CurrentVersion-on-the-Web6.Pdf 22. Sierra Youth Coalition. (2009). Campus Sustainability Assessment Framework (CSAF) Core. http://www.syc-cjs.org/sites/default/files/SYC-CSAF-core.pdf 23. GRI. (2020). Consolidated Set of GRI Sustainability Reporting Standards 2020; GRI: Amsterdam, The Netherland. https://www.globalreporting.org/ 24. STARS. (2015). https://stars.aashe.org 25. Berzosa, A., Bernaldo, M. O., & Fernández-Sanchez, G. (2017). Sustainability assessment tools for higher education: An empirical comparative analysis. Journal of Cleaner Production, 161, 812–820. https://doi.org/10.1016/j.jclepro.2017.05.194 26. Yarime, M., & Tanaka, Y. (2012). The issues and methodologies in sustainability assessment tools for higher education institutions: A review of recent trends and future challenges. Journal of Education for Sustainable Development, 6(1), 63–77. https://doi.org/10.1177/097340821100 600113 27. Parvez, N., & Agrawal, A. (2019). Assessment of sustainable development in technical higher education institutes of India. Journal of Cleaner Production, 214, 975–994. https://doi.org/10. 1016/j.jclepro.2018.12.305 28. Drahein, A. D., De Lima, E. P., & Da Costa, S. E. G. (2019). Sustainability assessment of the service operations at seven higher education institutions in Brazil. Journal of Cleaner Production, 212, 527–536. https://doi.org/10.1016/j.jclepro.2018.11.293 29. Association for the Advancement of Sustainability in Higher Education. (2012). Sustainability Tracking, Assessment & Rating System STARS: Version 1.2 Technical Manual. http://www. aashe.org/files/documents/STARS/stars_1.2_technical_manual.pdf 30. United Nations Environment Programme. UNEP 2013 Annual Report.pdf. http://www.unep.org 31. Bautista-Puig, N., & Sanz-Casado, E. (2021). Sustainability practices in Spanish higher education institutions: An overview of status and implementation. Journal of Cleaner Production, 295, 126320. https://doi.org/10.1016/j.jclepro.2021.126320 32. Mochizuki, Y. (2019). Rethinking schooling for the 21st century: UNESCO-MGIEP’s contribution to SDG 4.7. Sustainability: The Journal of Record, 12(2), 88–92. https://doi.org/10.1089/ sus.2019.29160 33. UNESCO. (2018). Issues and trends in education for sustainable developement.pdf. https://en. unesco.org/sites/default/files/issues_0.pdf 34. Findler, F., Schönherr, N., Lozano, R., & Stacherl, B. (2018). Assessing the impacts of higher education institutions on sustainable development—an analysis of tools and indicators. Sustainability, 11(1), 59. https://doi.org/10.3390/su11010059

Blockchain Technology and Artificial Intelligence for Smart Education: State of Art, Challenges and Solutions Abdelaziz Ettaoufik, Amine Gharbaoui, and Abderrahim Tragha

Abstract

Many challenges are presented by the increasing volume and variety of data formats that are collected, analyzed and shared during the educational process. Effective solutions that can improve recruitment, tracking and learning processes are needed in the education sector. Recently, blockchain technology has become a competitor in place of centralized systems. It is actively used in a variety of industries, including education. This new technology can be very useful to create a decentralized space for the exchange and storage of documents since it will reinforce security and traceability and will provide a professional framework. We cover the main advantages of Blockchain technology in this essay and show the most recent advancements in the sector. We also talk about how this new technology may be used in education and present a solution that gives students a decentralized environment for storing and sharing information in total privacy. Keywords

Blockchain . Education . Smart education . Artificial intelligence . Blockchain in education A. Ettaoufik (B) · A. Gharbaoui · A. Tragha Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco e-mail: [email protected] A. Gharbaoui e-mail: [email protected] A. Tragha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_8

131

132

1

A. Ettaoufik et al.

Introduction

The economy has been more digitalized during the past years, and interest in cutting-edge technologies has significantly increased. Currently, many industries, especially the financial sector, are under pressure to discover ways to save costs, reduce risks, and increase benefits. As a result, they are being forced to examine current methods or develop new ones in order to achieve these objectives. One of the significant issues that necessitates the reconciliation of numerous assets (humans, delicate goods and equipment, authoritative, and so forth) is addressed by the information security. Several tools and techniques are used in this context including blockchain technology [1], biometrics [2], and others. Today, blockchain technology is used in many sectors. This cutting-edge technology consists of a continuous and sequential chain of blocks that are constructed in accordance with predetermined rules and contain information [3]. It represents a decentralized and distributed database used to record transactions on a computer network [4]. In order to control the various transactions, all participants are involved in a distributed information architecture. In general, blockchain technology has the ability to alter how transactions are conducted in both industry and daily life [5]. The digital economy, where there is a rising need for a distributed ledger system that can increase efficiency, simplicity, and transparency in this industry, is one of the sectors where blockchain technology is actively being implemented. In a blockchain application, each block contains a list of transactions that have been digitally signed by their verifier and posted on the distributed network for access and verification by all proper associated parties [6]. Information is maintained in this continuous chain form of blocks. Immutability, which refers to the difficulty of changing or modifying blocks, is one of the key features of the blockchain [5]. One of the features of this technology is the absence of the human element in the decision-making process. Blockchain gives consumers access to a range of data-related features, including storage, access, protection, and more. Additionally, it grants each member node control while ensuring the privacy and security of the data. These traits demonstrate how blockchain technology can significantly improve information architecture and security. As a result, the likelihood of adding additional incorrect blocks and making data vulnerable to assaults is nearly completely eliminated. Since blockchain technology is the foundation of the bitcoin cryptocurrency, it has been utilized in the banking and economic sectors. Due to her impressive qualities, she has caught the interest of stakeholders in a number of industries, including finance, banking, tourism, healthcare and government. For instance, many businesses are implementing this technology to secure customer data and streamline business operations. The potential and advantages of this new technology can also improve education. For education, blockchain technology holds great promise and may provide new, efficient structures [7–13]. The most significant benefits of its application in education include

Blockchain Technology and Artificial Intelligence for Smart Education …

133

secure data storage related to educational activities, ensuring the originality of scientific work, high security of documents, projects, and research work, sharing and access to information describing interesting innovations, etc. In other words, integrating blockchain technology will provide students a special identification for their future professional careers. The following section presents works that deal with the use of blockchain in education. In Sect. 3, we describe the methods we used for our literature review and discuss our results. The fourth section illustrates the general design of our proposal. In the last section, we conclude our work.

2

Blockchain in Education

Blockchain was first developed as a system to regulate bitcoins [11], but it has since evolved into a useful technology for managing sensitive data across a variety of industries, including finance, governance [6], education [8, 10], healthcare [14], security [15], internet of things [16], supply chain [17], and many others. The substantial study on blockchain technology in recent years has covered a wide range of issues and applications. We concentrate on studies that have looked into the use of this technology in education. The study carried out in [8] represents a review of 31 publications that go through the advantages and challenges of educational use of blockchain. According to this study, academic certifications are primarily issued and verified using blockchain technology. Students’ skills and learning results are also shared, and their professional abilities are evaluated. Although the results of the study show that certificate management is the most researched axis, the other applications were divided into eleven categories, including managing skills and learning effects, assessing students’ professional capabilities, safeguarding learning objects, protecting a collaborative educational environment, sending fees and credits, collecting digital responsibility consent, controlling competitions, managing intellectual property, and improving student engagement [10, 18]. Despite privacy and security issues, it was discovered that blockchain might have a significant impact on education by providing a secure platform for data exchange, reducing expenses, and fostering openness and trust. A full examination of the literature on the application of blockchain in higher education as well as its main issues is also provided by another study [11]. In this study, authors conclude that in order for smart contracts to be used in higher education, performance, security, and platform interoperability problems must be resolved. A blockchain model based on the Student Information System (SIS) is presented in [19]. After describing this paradigm, authors conclude that their contribution will give institutions better and more secure SIS management. Thus, sharing and storing data is much simpler.

134

A. Ettaoufik et al.

The fourth industrial revolution gave rise to blockchain technology, which is a brandnew invention on Earth [20]. Researchers and organizations are beginning to pay attention to the topic as it changes. The education sector, like other industries, is attempting to benefit from blockchain technology. For instance, according to the Scopus database, an article published by Sharples and Domingue [21] in 2016 represents the first scientific article dealing with this subject. The several studies that were looked at discuss the benefits of using blockchain technology in different businesses. This system uses consensus validation methods and cryptographic techniques to benifit the advantages of decentralization, immutability, and total traceability. Despite the CoVID-19 pandemic’s acceleration of the digitization of education [5], numerous studies show that the adoption of the blockchain system in education is still in its infancy. The effects of artificial intelligence application in the educational sector are covered in a number of researches [22–24]. In order to make up for artificial intelligence’s absence from modern education, increase student interest in learning, and produce high-quality students, it is necessary to integrate blockchain technology and artificial intelligence with modern education. These studies demonstrate a relationship between clever instruction and an increase in students’ overall grades.

3

Literature Review

This study’s objective is to evaluate the present status of research on blockchain technology’s application in education. A quantitative search of the keywords “blockchain” and “block-chain” in the Scopus database reveals that there were 123 publications on this subject in 2016 and 10,116 in 2022. This demonstrates how this technique has gained traction and captured the interest of both scholars and practitioners. Despite being primarily utilized for cryptocurrencies, there are more industries that use blockchain technology, such as “Internet of Things”, “Industry 4.0”, “security”, “healthcare”, “industrial IoT”, “governance”, “smart city”, etc. according to a survey of recently published publications.

3.1

Methodology

Our study has two phases: identification and processing. In the identification phase, we present the list of keywords used to extract the pertinent articles. In the processing phase, we detail the various dataset processing techniques used to exclude irrelevant works and keep the most useful ones.

Blockchain Technology and Artificial Intelligence for Smart Education …

135

Table 1 Screening Methodology Scopus database

Screening

Documents

Meta-search

Keyword: blockchain or block-chain

33630

Inclusion criteria

Keyword: (blockchain or block-chain) and education Keyword: request 1

3.1.1

715 3120

Identification

According to the work published in [25], It is important to choose relevant articles using particular keywords while avoiding too specific ones which can reject relevant works. In order to appropriately choose synonyms and expressions associated with keywords, we used the PICO model which was published in [26]. We have created the following list of keywords and synonyms to help us achieve the goal of our work: Blockchain: block-chain Education: smart education, educational, Higher Education, learning, distance learning, education sector, decentralized education, student information system, technology-based education. We conducted our bibliographic search in the SCOPUS database in July 2023. All journal publications, book chapters, and conference papers published from 2016 until 2022 were included. There were three stages to the search: (i) using “blockchain” and “blockchain” as search terms 33,630 documents were acquired; (ii) 715 papers were identified after adding the phrase “education”; and (iii) by employing the keyword “education” along with synonyms and related terms, the following request was made: Request1: «((blockchain OR block-chain) AND (education OR “smart education” OR educational OR “Higher Education” OR learning OR “distance learning” OR “education sector” OR “de-centralized education” OR “student information system” OR “technology-based education”))» 3120 documents found. Table 1 displays the quantity of documents located.

3.1.2

Treatment

Importing dataset. In this stage, data from the SCOPUS database was exported in accordance with the below-listed selection criteria. Displaying Metadata. We presented the metadata information characterizing our dataset in this stage. Data detailing the extracted documents are displayed in Fig. 1. Pre-treatment. By deleting redundant lines and choosing useful fields in the screening step, we started structuring the dataset in this step.

136

A. Ettaoufik et al.

Fig. 1 Information describing the extracted documents

Screening. We established the criteria for including and excluding scientific documents in this step so that we could choose the pertinent documents for our investigation. Inclusion criteria: – Documents that go over how blockchain technology is being used in education. – English-language written documents. Exclusion criteria: – Documents that emphasize the application of blockchain in other fields. – Duplicate documents. – Lack of methods used and results.

Blockchain Technology and Artificial Intelligence for Smart Education …

3.2

137

Data Analysis

We started by doing a comparison between articles discussing the application of blockchain in education and articles discussing blockchain in general. The outcome shown in Fig. 2 demonstrates a significant difference between the two axes (8.64% for education). This indicates that blockchain technology’s use in education is still in its infancy. Figure 3 illustrates how articles on the integration of the blockchain to learning have evolved. As the number of publications increases from 4 in 2016 (0.13%) to 1240 in 2022 (40,23%), we can see that this topic is starting to catch the interest of scholars. The distribution of research articles published in the Scopus database by year and type is depicted in Fig. 4.

Count

15000 10000 5000 0 2016

2017

2018

2019

2020

2021

2022

Year

Blockchain

Blockchain in education

Count

Fig. 2 Comparing the number of publications on blockchain technology and applications in education

1400 1200 1000 800 600 400 200 0 2016

2017

2018

2019

2020

2021

Year

Fig. 3 Number of publications on blockchain use in education per year

2022

138

A. Ettaoufik et al.

800

Count

600 400 200 0 2016

2017

2018

2019

2020

2021

2022

Year

Conference paper

Article

book chapter

Fig. 4 Number and type of articles on blockchain use in education per year

3.3

Discussion

The bulk of studies addressing the blockchain integration in education were qualitative and theoretical, it can be inferred from the literature [10, 11, 19, 27]. The majority of papers outline blockchain’s broad educational benefits. The entire impact of blockchain technology on the various stages of school life has not been thoroughly examined. The conclusions drawn from our study are that this emerging technology has the potential to benefit education as well. Here are just a few of the countless benefits of using blockchain technology in education: – Verification and sharing [19]. An educational record cannot be lost or falsified, and it can be used as a record of learning, meaning that a person’s whole educational “reputation” will accompany them throughout their lives. – Academic credentials [28]. “degrees”, “diplomas” and “transcripts”, are an example of documents that can be verified using blockchain technology, and these credentials can then be shared. This can facilitate the sharing and verification of student credentials while lowering fraud. – Student evaluation [10]. Blockchain can be utilized as a mechanism for student evaluation. For instance, blockchain technology is presented as a method for initial assessment of achievement, intermediate assessments at each level, and ultimate certification in the context of language pedagogy. A single system can account for all language accomplishments, identifying a student’s strong and poor areas of proficiency and presenting them as a program for addressing problems. – Digitization [29]. It might also represent a transition away from paper in favor of digital media, which would mean fewer certificates and other kinds of reporting. – Education cost [10]. Blockchain technology offers the ability to lower education costs by enhancing efficiency and streamlining operations. Institutions can save the expense

Blockchain Technology and Artificial Intelligence for Smart Education …

139

of confirming and retaining paper documents, for instance, by using blockchain to distribute and validate academic credentials. It can also offer a reliable assessment system, taking the place of the old style that uses a lot of paper. – Online learning [11]. Blockchain technology can be applied to online learning to make the process easier and to produce more immersive and engaging learning environments. Additionally, it can be utilized to increase educational access, particularly in underdeveloped nations where access to educational resources may be constrained. Platforms built on the blockchain, for instance, can be used to facilitate online learning and collaboration as well as to develop and exchange educational content. The security, effectiveness, accuracy, accessibility, and accessibility of processes as well as the accessibility of educational resources could all be improved with the inclusion of this novel technology in education. Furthermore, the application of blockchain technology can make use of a number of already-existing assets and datasets.

4

The Proposed Solution

The structure of our overall contribution, which provides a framework for the Blockchain adoption in education, is presented in this part. The student serves as the primary player in our design (Fig. 5). The suggested strategy will considerably increase security since it will stop anyone from falsifying documents and stealing final projects, research papers, and degrees. In

Fig. 5 Overall blockchain implementation architecture in education

140

A. Ettaoufik et al.

addition to helping students protect their work and prevent plagiarism, this strategy also gives them the option of using the system as a safe to keep important papers. This is a great advantage. The establishment will profit from this system because it incorporates student identification, providing ID cards to new students and ensuring regulated access. The proposed design may also create new opportunities for collaboration between firms and universities in the preparation of qualified future workers. Naturally, students’ principal goal is to develop the professional abilities required for future work. However, many businesses find it challenging to quickly ascertain the legitimacy of documents when employing new personnel, which slows down the hiring process to this end we plan to extend our architecture by adding a partnership space. Our method allows companies to confirm the legitimacy of documents and offers a single platform for checking and verification. Additionally, the extended architecture will allow employers to learn about future specialists’ training, point them in the direction of targeted training programs, and set requirements. This will allow educational institutions to design new programs that will train particular specialists with the skills specified by employers, effectively raising the level and quality of training.

5

Conclusion

The creation and implementation of new educational programs is dictated by the rapid acceptance of digitalization in many sectors. Blockchain technology can be used to address the drawbacks of traditional systems. We did research on how blockchain technology is being used in education. Our analysis demonstrates that there has been less research on this topic compared to the use of blockchain in other sectors, leading us to the finding that blockchain integration in education is presently in its early days. We suggested a blockchain implementation design for a higher education institution in light of the lack of work studying this topic. Our design is intended as an electronic logbook in which all relevant information is kept, including documents collected throughout academic careers, validation of their legitimacy, proof of intellectual property and student identification. In order to be able to deploy our contribution in all educational dimensions, we plan to integrate artificial intelligence to allow students to choose the best resources on the one hand, and on the other hand to allow employers to choose the candidates who meet the requirements of their offers.

Blockchain Technology and Artificial Intelligence for Smart Education …

141

References 1. Liao, C.-H., Guan, X.-Q., Cheng, J.-H., & Yuan, S.-M. (2022). Blockchain-based identity management and access control framework for open banking ecosystem. Future Generation Computer Systems, 135, 450–466. https://doi.org/10.1016/j.future.2022.05.015 2. Othman, A., & Callahan, J. (2018). The horcrux protocol: A method for decentralized biometricbased self-sovereign identity. International Joint Conference on Neural Networks (IJCNN), 2018, 1–7. https://doi.org/10.1109/IJCNN.2018.8489316 3. Bamakan, S. M. H., Babaei Bondarti, A., Babaei Bondarti, P., & Qu, Q. (2021). Blockchain technology forecasting by patent analytics and text mining. Blockchain: Research and Applications, 2(2), 100019. https://doi.org/10.1016/j.bcra.2021.100019 4. Xu, M., Chen, X., & Kou, G. (2019). A systematic review of blockchain. Financial Innovation, 5(1), 1–14. 5. Yalcin, H., & Daim, T. (2021). Mining research and invention activity for innovation trends: Case of blockchain technology. Scientometrics, 126(5), 3775–3806. 6. Shrimali, B., & Patel, H. B. (2022). Blockchain state-of-the-art: Architecture, use cases, consensus, challenges and opportunities. Journal of King Saud University - Computer and Information Sciences, 34(9), 6793–6807. https://doi.org/10.1016/j.jksuci.2021.08.005 7. Frantani, F. (2021). The acceleration of digitalisation within education as a result of COVID-19. Global Focus: The EFMD Business Magazine, 15(1), 1–4. 8. Sudha Sadasivam, G. (2021). A critical review on using blockchain technology in education domain. In S.-W. Lee, I. Singh, & M. Mohammadian (Eds.), Blockchain Technology for IoT Applications (pp. 85–117). Springer. https://doi.org/10.1007/978-981-33-4122-7_5 9. Suyambu, G. T., Anand, M., & Janakirani, M. (2020). Blockchain—a most disruptive technology on the spotlight of world engineering education paradigm. Procedia Computer Science, 172, 152–158. https://doi.org/10.1016/j.procs.2020.05.023 10. Alammary, A., Alhazmi, S., Almasri, M., & Gillani, S. (2019). Blockchain-based applications in education: A systematic review. Applied Sciences, 9(12), Article 12. https://doi.org/10.3390/ app9122400 11. Raimundo, R., & Rosário, A. (2021). Blockchain system in the higher education. European Journal of Investigation in Health, Psychology and Education, 11(1), Article 1. https://doi.org/ 10.3390/ejihpe11010021 12. Chen, G., Xu, B., Lu, M., & Chen, N.-S. (2018). Exploring blockchain technology and its potential applications for education. Smart Learning Environments, 5(1), 1. https://doi.org/10.1186/ s40561-017-0050-x 13. Li, J., Lan, M., Tang, Y., Chen, S., Wang, F.-Y., & Wei, W. (2020). A blockchain-based educational digital assets management system. IFAC-PapersOnLine, 53(5), 47–52. https://doi.org/10. 1016/j.ifacol.2021.04.082 14. Dabbagh, M., Sookhak, M., & Safa, N. S. (2019). The evolution of blockchain: A bibliometric study. IEEE Access, 7, 19212–19221. https://doi.org/10.1109/ACCESS.2019.2895646 15. Ahluwalia, S., Mahto, R. V., & Guerrero, M. (2020). Blockchain technology and startup financing: A transaction cost economics perspective. Technological Forecasting and Social Change, 151, 119854. https://doi.org/10.1016/j.techfore.2019.119854 16. Singh, S., & Duggal, S. (2022). Challenges of integration of blockchain into internet of things (IoT): A survey. In AIP conference proceedings (Vol. 2597(1), p. 060004). https://doi.org/10. 1063/5.0118864

142

A. Ettaoufik et al.

17. Sabri, Y., Harchi, S., & El Kamoun, N. (2022). Managing health supply chain using blockchain technology: State of art challenges and solution. International Journal of Reconfigurable and Embedded Systems, 11(3), 258. 18. Fekete, D. L., & Kiss, A. (2023). Toward building smart contract-based higher education systems using zero-knowledge Ethereum virtual machine. Electronics, 12(3), 664. https://doi.org/ 10.3390/electronics12030664 19. Ali, S. I. M., Farouk, H., & Sharaf, H. (2022). A blockchain-based models for student information systems. Egyptian Informatics Journal, 23(2), 187–196. https://doi.org/10.1016/j.eij.2021. 12.002 20. Bhaskar, P., Tiwari, C. K., & Joshi, A. (2021). Blockchain in education management: Present and future applications. Interactive Technology and Smart Education, 18(1), 1–17. 21. Sharples, M., & Domingue, J. (2016). The blockchain and kudos: A distributed system for educational record, reputation and reward. In K. Verbert, M. Sharples, & T. Klobuˇcar (Eds.), Adaptive and adaptable learning (pp. 490–496). Springer International Publishing. https://doi. org/10.1007/978-3-319-45153-4_48 22. Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2020). The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access, 8, 110461–110477. 23. Chaka, C. (2023). Fourth industrial revolution—a review of applications, prospects, and challenges for artificial intelligence, robotics and blockchain in higher education. Research and Practice in Technology Enhanced Learning, 18, 002–002. 24. Chen, Y. (2022). The impact of artificial intelligence and blockchain technology on the development of modern educational technology. Mobile Information Systems, 2022, 1–12. https://doi. org/10.1155/2022/3231698 25. Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, ver. 2.3 ebse technical report. ebse. 26. Miller, S. A., & Forrest, J. L. (2001). Enhancing your practice through evidence-based decision making: PICO, learning how to ask good questions. Journal of Evidence Based Dental Practice, 1(2), 136–141. https://doi.org/10.1016/S1532-3382(01)70024-3 27. Ullah, N., Mugahed Al-Rahmi, W., Alzahrani, A. I., Alfarraj, O., & Alblehai, F. M. (2021). Blockchain technology adoption in smart learning environments. Sustainability, 13(4), Article 4. https://doi.org/10.3390/su13041801 28. Tran, T.-T., & Le, H.-D. (2021). IU-SmartCert: A blockchain-based system for academic credentials with selective disclosure. In Future data and security engineering. Big data, security and privacy, smart city and Industry 4.0 applications (pp. 293–309). https://doi.org/10.1007/ 978-981-16-8062-5_20 29. Abdeldayem, M. M., & Al Dulaimi, S. H. (2020). Trends ofglobal fintech education practices and the GCC perspective. International Journal of Advanced Science and Technology, 29, 7150– 7163.

Artifical Intelligence in Nurse Education Velibor Boˇzi´c

Abstract

Artificial Intelligence (AI) is increasingly being integrated into various fields, including healthcare and nursing education. This abstract explores the topic of AI in nurse education, highlighting its importance, current practices, and potential benefits and challenges. The integration of AI in nurse education involves incorporating AI concepts, technologies, and applications into the nursing curriculum. It aims to prepare nursing students for the evolving healthcare landscape, equip them with essential AIrelated skills, and enhance their ability to provide safe and efficient patient care. The use of AI in nurse education encompasses various approaches, such as the integration of AI-based simulations and virtual patients, the utilization of AI-driven clinical decision support systems, and the exploration of AI ethics and responsible AI use. These approaches provide opportunities for students to develop critical thinking, decisionmaking, and data analysis skills in the context of AI in healthcare. While AI has the potential to revolutionize nursing education and practice, it also presents certain challenges and considerations. These include ethical concerns related to data privacy, algorithm transparency, and potential biases in AI algorithms. It is essential to address these challenges and ensure responsible use of AI in nursing education. To overcome the risks and challenges associated with teaching AI in nurse education, strategies such as comprehensive curriculum design, faculty development and training, handson practical experiences, collaboration with industry experts, and continuous learning and adaptation are crucial. The literature on AI in nursing education provides valuable insights into the current state of research, examples of AI integration in nursing V. Boˇzi´c (B) General Hospital Koprivnica, Koprivnica, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_9

143

144

V. Boˇzi´c

curricula, and the impact on student outcomes. Researchers have explored topics such as virtual simulations, student perceptions, faculty perspectives, AI competencies, and ethical considerations in integrating AI into nursing education. Access to information about AI in nurse education can be obtained from academic journals, conferences, professional associations, books, online databases, institutional websites, government reports, and reputable online resources. In summary, the integration of AI in nurse education is an emerging field that holds promise for enhancing nursing education and preparing future nurses for AI-driven healthcare. By addressing the challenges, promoting responsible AI use, and leveraging the benefits of AI, nurse educators can create a supportive and informed learning environment that equips nurses with the skills and knowledge necessary for the future of healthcare. Keywords

Artifical intelligence • Curriculum • Education • Healthcare • Nursing

1

Introduction

The field of healthcare is constantly evolving, driven by technological advancements and the need to provide quality care in an ever-changing landscape. One such technology that has gained significant attention and potential in healthcare is Artificial Intelligence (AI). AI, a branch of computer science that simulates intelligent behavior in machines, has found applications in various sectors, including nursing education. In recent years, there has been a growing interest in integrating AI into nurse education to prepare nursing students for the future of healthcare. AI has the potential to transform nursing education by offering innovative teaching and learning methods, enhancing clinical decision-making skills, and improving patient outcomes. However, the successful integration of AI in nurse education requires careful consideration of several factors, including curriculum design, ethical considerations, faculty development, and student readiness. This paper aims to explore the topic of AI in nurse education, highlighting its importance, current practices, advantages, challenges, and strategies to overcome potential risks [1, 2]. It delves into the various ways AI is being incorporated into nursing curricula, such as the use of AI-based simulations, virtual patients, and clinical decision support systems. Additionally, it discusses the ethical implications of AI in nurse education and the need to address privacy, transparency, and bias concerns. Furthermore, the paper addresses the importance of faculty development and training programs to equip nurse educators with the necessary knowledge and skills to effectively teach AI concepts and technologies. It also emphasizes the role of collaboration between nursing educators and AI experts to ensure the integration of evidence-based AI practices into the curriculum.

Artifical Intelligence in Nurse Education

145

Overall, this paper seeks to provide insights into the current state of AI integration in nurse education, the benefits it offers, and the potential risks and challenges associated with its implementation. By understanding and addressing these considerations, nurse educators can harness the power of AI to enhance nursing education, prepare students for the AI-driven healthcare landscape, and ultimately improve patient care outcomes.

2

AI & Nurse Education

Artificial intelligence (AI) has the potential to significantly impact nurse education by enhancing teaching methodologies, improving student learning experiences, and facilitating better healthcare outcomes. Here are some ways in which AI can be utilized in nurse education [3–5]: Personalized Learning. AI-powered adaptive learning systems can analyze individual student’s performance and provide personalized learning paths. These systems can identify knowledge gaps, recommend targeted educational resources, and tailor the curriculum to the needs of each student, promoting more efficient and effective learning. Simulation and Virtual Reality (VR). AI-driven simulations and VR can offer immersive learning experiences for nursing students. They can practice clinical skills, decisionmaking, and critical thinking in realistic virtual environments. AI algorithms can assess student performance, provide feedback, and guide them in improving their skills. Intelligent Tutoring Systems. AI-based tutoring systems can provide students with interactive and intelligent support. These systems can answer questions, explain complex concepts, and engage students in dialogue, promoting active learning. They can also track students’ progress and offer personalized guidance based on their individual needs. Data Analysis and Predictive Analytics. AI algorithms can analyze vast amounts of healthcare data to identify patterns, trends, and potential risks. In nurse education, AI can help students analyze patient data, interpret diagnostic tests, and make informed clinical decisions. It can also assist educators in identifying areas where students may need additional support or interventions. Natural Language Processing (NLP). NLP technology enables AI systems to understand and process human language. Nurse educators can use NLP-powered chatbots or virtual assistants to provide instant support to students, answer their questions, and offer guidance. These AI systems can be available 24/7, improving accessibility and responsiveness in nurse education.

146

V. Boˇzi´c

Clinical Decision Support. AI can assist nurses in making evidence-based decisions by providing real-time access to the latest research, guidelines, and best practices. AI algorithms can analyze patient data, suggest treatment options, and alert nurses to potential medication errors or adverse reactions, enhancing patient safety. Remote Learning and Telehealth. AI-powered platforms can facilitate remote learning opportunities, allowing nursing students to access educational resources, collaborate with peers, and engage in virtual clinical experiences. Additionally, AI can support telehealth initiatives by assisting in remote patient monitoring, triage, and care coordination. AI has great potential, it should not replace the hands-on, practical experience that is essential in nursing education. AI should be seen as a tool to augment and enhance the learning process, providing additional support and resources to both students and educators.

3

Roles of AI in High School and University

The application of AI in secondary education (high school) and higher education (university) can differ in several ways due to variations in the learning environment, objectives, and student needs. Here are some key differences between the roles of AI in secondary and higher education [6, 7]: Curriculum Design and Personalized Learning • High school. AI can assist in designing adaptive curricula that cater to the diverse learning needs of high school students. It can offer personalized learning paths, recommend resources, and adapt the curriculum based on student performance and progress. • University. In university, AI can still provide personalized learning experiences, but the focus may shift towards more specialized and advanced topics. AI can assist in designing tailored courses, identifying prerequisites, and suggesting personalized learning resources for university students. Student Support and Tutoring • High school. AI can provide virtual tutoring and support for high school students. It can answer questions, explain concepts, and offer additional practice exercises to reinforce learning. AI chatbots or virtual assistants can be used to provide instant support outside of regular classroom hours. • University. AI-based tutoring systems can offer support to university students, particularly in large lecture-style courses. AI can provide explanations, answer questions, and offer guidance on complex subjects. Additionally, AI-powered tools can assist students

Artifical Intelligence in Nurse Education

147

in organizing their study materials, managing deadlines, and optimizing their learning strategies. University Admissions and Career Guidance • High school. AI can assist high school students in the university admissions process by providing information about different institutions, programs, and admission requirements. AI-powered tools can help students explore career paths, assess their skills and interests, and receive guidance on suitable University options. • University. AI can support university students in career planning and development. It can offer personalized career advice, recommend internships or job opportunities, and analyze industry trends to help students make informed decisions about their future career paths. Research and Data Analysis • High school. AI can support high school students in conducting research, analyzing data, and presenting findings. It can assist in data collection, data processing, and statistical analysis, enabling students to engage in more advanced research projects. • University. AI can play a significant role in advanced research in universities. It can assist researchers in data mining, natural language processing, and complex modeling tasks. AI algorithms can help analyze large datasets, identify patterns, and generate insights that contribute to academic research and innovation. Skill Development • High school. AI can support the development of foundational skills in high school students, such as critical thinking, problem-solving, and digital literacy. AI-powered educational games or platforms can provide interactive learning experiences that enhance these skills. • University. AI can assist university students in developing more advanced and specialized skills relevant to their chosen disciplines. For instance, AI can be used in engineering programs for simulation and modeling, in healthcare programs for medical diagnosis, or in business programs for data analysis and market forecasting. Learning Objectives • High school. AI in high school often focuses on providing a well-rounded education and foundational knowledge across multiple subjects. It aims to support students in building a broad knowledge base and developing essential skills for further education or career paths.

148

V. Boˇzi´c

• University. AI in university is often more specialized, catering to specific disciplines and advanced concepts. It aims to provide in-depth knowledge, foster critical thinking, and prepare students for specialized careers or research opportunities in their chosen fields. Depth and Complexity of Topics • High school. AI in high school typically covers fundamental concepts and introductorylevel knowledge across various subjects. It aims to provide a broad understanding of different disciplines and encourage exploration and curiosity. • University. AI in university delves deeper into complex topics, theories, and advanced methodologies within specific disciplines. It aims to develop expertise and mastery in a particular field, preparing students for specialized careers or research in their chosen areas. Instructional Methods • High school. AI in high school often focuses on providing interactive and engaging learning experiences. It may include AI-powered educational games, multimedia resources, and adaptive learning platforms to enhance student engagement and understanding. • University. AI in university can involve more self-directed learning, research-based projects, and collaboration with peers and faculty. AI tools may be used to support research, data analysis, and advanced simulations, enabling students to delve deeper into their fields of study. Career and Academic Guidance • High school. AI in high school can assist students in exploring various career paths and identifying their interests and strengths. It may provide general guidance on University options, college admissions, and scholarship opportunities. • University. AI in university can provide more specialized career guidance based on individual academic interests and goals. It may offer insights into specific industries, internship opportunities, and connections to professionals in the field, helping students make informed career choices. Research and Innovation • High school. AI in high school can introduce students to basic research skills and methodologies. It may involve AI tools to support data collection, analysis, and presentation of research projects within the context of high school-level research.

Artifical Intelligence in Nurse Education

149

• University. AI in university plays a significant role in advanced research and innovation. It can assist students and faculty in conducting complex research, data modeling, and analysis, contributing to academic advancements in their respective fields. Mentioned differences are generalizations, and the roles of AI can vary based on specific educational institutions, programs, and individual courses. The implementation of AI should always align with the learning objectives, curriculum design, and the unique needs of students at each level of education.

4

Examples of AI Tools Using in High School and University

There are several real-world examples of AI being used in both high school and University. Here are a few examples [8, 9]: AI in High School Smart Content Platforms: Platforms like SMART Learning Suite, Knewton, and Cognii use AI algorithms to provide adaptive and personalized learning experiences for students in subjects like math, science, and language arts. They analyze student performance and provide customized feedback, resources, and recommendations. Virtual Tutors and Chatbots: AI-powered virtual tutors and chatbots, such as IBM Watson Education and Wooclap, are used to provide instant support and answer student questions. These AI systems can assist with homework, explain concepts, and engage students in interactive dialogue. Digital Learning Assistants: AI assistants like Squirrel AI in China use machine learning algorithms to assess student knowledge gaps and provide targeted interventions. They adapt the curriculum based on individual student needs, track progress, and offer personalized recommendations for improvement. AI in University Intelligent Tutoring Systems: Platforms like Carnegie Learning and Knewton are used in University to provide personalized tutoring experiences. They analyze student performance data, offer targeted feedback, and adjust the learning materials to meet individual student needs. Plagiarism Detection: AI-powered tools like Turnitin and Grammarly utilize natural language processing and machine learning to identify instances of plagiarism in student papers. These tools help educators ensure academic integrity and promote original writing. Predictive Analytics: University institutions use AI-based predictive analytics systems to identify students at risk of dropping out or underperforming. These systems analyze data such as student demographics, academic records, and engagement patterns to provide early interventions and support.

150

V. Boˇzi´c

Virtual Labs and Simulations: AI and VR technologies are used in University to simulate real-world scenarios and provide hands-on experiences. For example, medical schools may use VR simulations for surgical training, and engineering programs may utilize virtual labs for practical experiments. Course Recommendation Systems: AI algorithms are employed in University to recommend courses to students based on their academic interests, performance, and career goals. These systems help students navigate through the vast course offerings and make informed decisions about their academic paths. These are just a few examples, and the use of AI in education is continually evolving and expanding. Educational institutions and edtech companies are exploring various AI applications to enhance teaching, learning, and administrative processes in both high school and University settings.

5

AI & Curriculum of Nursing Education

AI can play a significant role in the curriculum of nursing education, enhancing teaching and learning experiences, and preparing future nurses for the evolving healthcare landscape. Here are some key areas where AI can be incorporated into the nursing curriculum [10–12]: Introduction to AI and Data Science. Introduce nursing students to the basics of AI, machine learning, and data science. Provide an overview of AI applications in healthcare, its potential benefits, and ethical considerations. This helps students understand the foundational concepts and potential applications of AI in their field. Healthcare Data Analysis. Teach students how to analyze and interpret healthcare data using AI tools. This includes training on data collection, cleaning, analysis, and visualization techniques using AI algorithms. Students can learn to identify trends, patterns, and insights from large datasets to inform decision-making and improve patient care. Clinical Decision Support Systems. Introduce students to clinical decision support systems powered by AI. These systems can assist nurses in diagnosing conditions, selecting appropriate treatments, and providing personalized care plans. Students can learn how to utilize AI-driven tools to make evidence-based decisions and improve patient outcomes. Health Monitoring and Telehealth. Teach students about the role of AI in remote patient monitoring and telehealth. Explore AI-based technologies such as wearable devices, remote sensors, and virtual health platforms. Students can understand how these technologies enable continuous monitoring, early detection of health issues, and remote consultations. Patient Risk Assessment and Predictive Analytics. Educate students on using AI algorithms to assess patient risks, predict outcomes, and prioritize care. Students can learn to integrate patient data, identify risk factors, and use predictive models to support proactive interventions and preventive measures.

Artifical Intelligence in Nurse Education

151

Simulations and Virtual Patients. Utilize AI-powered simulations and virtual patients to provide realistic and immersive clinical experiences. These technologies allow students to practice clinical skills, make critical decisions, and learn from realistic patient scenarios in a controlled environment. Ethical and Legal Considerations. Include discussions on the ethical and legal implications of AI in nursing practice. Help students understand the importance of patient privacy, data security, transparency, and responsible use of AI technologies. Foster critical thinking around the ethical dilemmas and challenges associated with AI in healthcare. Interprofessional Collaboration. Emphasize the role of AI in facilitating interprofessional collaboration. Teach students how to effectively communicate and collaborate with AI systems and technologies in a team-based healthcare setting. This includes understanding the limitations and biases of AI and ensuring proper human oversight. Continuous Learning and Professional Development. Educate students on the need for continuous learning and staying updated on AI advancements in nursing practice. Encourage them to engage in lifelong learning, explore emerging technologies, and adapt to the evolving AI-driven healthcare landscape. By incorporating AI into the nursing curriculum, students can develop essential skills in data analysis, critical thinking, decision-making, and utilizing AI technologies to enhance patient care. It prepares them to leverage AI tools responsibly, contribute to the development of AI-driven healthcare innovations, and adapt to the changing healthcare environment.

5.1

Situation of Using AI in Curriculum

The use of AI in nursing education was still emerging and not yet widely integrated into curricula across all institutions. However, there were some notable initiatives and institutions that had started incorporating AI into their nursing education curricula. Here are a few examples [13]: University of Pennsylvania. The University of Pennsylvania School of Nursing has integrated AI into its curriculum through its “Penn Nursing Innovation Fellows” program. This program aims to train nurses in using AI and other emerging technologies to improve patient care and outcomes. Duke University. Duke University School of Nursing has collaborated with IBM Watson Health to integrate AI-powered technologies into their curriculum. Students learn how to use AI platforms for clinical decision-making, personalized care, and health data analysis. Johns Hopkins University. Johns Hopkins School of Nursing has been using AI-driven simulations to provide virtual clinical experiences. Students engage with virtual patients and practice clinical skills, decision-making, and critical thinking in realistic scenarios.

152

V. Boˇzi´c

Columbia University. Columbia University School of Nursing has been exploring the use of AI in nursing education. They have incorporated AI-powered tools for data analysis and clinical decision support into their curriculum to enhance student learning and improve patient care. EdTech Companies. Various edtech companies, such as Kaplan and Cognii, have developed AI-powered platforms and learning tools specifically designed for nursing education. These platforms offer adaptive learning, personalized feedback, and interactive simulations to support nursing students in their education and training. United Kingdom. The University of Surrey in the UK has developed a virtual reality and AI simulation platform called “Nursing Associate Immersive Learning Environment” (NAILE). This platform allows nursing students to practice clinical skills, decision-making, and communication in a virtual environment. Canada. In Canada, the University of Alberta’s Faculty of Nursing has implemented AI technologies in their nursing curriculum. They have integrated AI-driven simulations and virtual patients to enhance clinical learning experiences for students. Australia. Several nursing education institutions in Australia are exploring the use of AI in their curricula. For example, the University of Technology Sydney has integrated AI technologies in their nursing programs to support clinical decision-making and enhance student learning outcomes. Singapore. In Singapore, institutions like the National University of Singapore and Nanyang Polytechnic are exploring the use of AI in nursing education. They are implementing AI-driven simulations, virtual reality, and clinical decision support systems to enhance nursing training and education. Japan. Japanese institutions, such as the University of Tokyo and Keio University, are utilizing AI technologies in nursing education. They are integrating AI-driven tools for clinical data analysis, virtual patient simulations, and predictive analytics to improve nursing practice and education. The integration of AI into nursing education may vary among different institutions and regions. The field of AI in healthcare and nursing education is rapidly evolving, and new developments may have occurred since my knowledge cutoff. Therefore, it’s recommended to explore current resources, research, and the websites of specific nursing education institutions to get the most up-to-date information on the use of AI in nursing curricula. As AI continues to advance and its applications in healthcare expand, it is expected that more countries and institutions will incorporate AI into their nursing education to better prepare future nurses for the evolving healthcare landscape.

Artifical Intelligence in Nurse Education

6

153

General Framework for Incorporating AI into a Nursing Curriculum

The specific implementation of AI in nursing education may vary based on the educational institution, regional context, and program goals. Here is a general framework for incorporating AI into a nursing curriculum [14]: Introduction to AI in Healthcare • Introduction to AI applications in healthcare and nursing practice. • Overview of AI concepts, including machine learning, natural language processing, and data analytics. • Ethical considerations and challenges associated with AI in healthcare. Data Science and Analytics • Introduction to healthcare data sources and data management techniques. • Basic statistical concepts and analysis methods. • Introduction to AI tools and algorithms used in healthcare data analysis. AI Applications in Nursing. • Clinical decision support systems and their role in nursing practice. • AI-driven technologies for patient monitoring and remote care. • AI applications in disease diagnosis, treatment planning, and personalized medicine. Healthcare Data Ethics and Privacy. • Understanding the ethical implications of using patient data in AI applications. • Ensuring patient privacy and data security in AI-driven healthcare systems. • Ethical considerations when implementing AI technologies in nursing practice. AI in Nursing Research. • Exploring the use of AI in nursing research studies. • Understanding the potential of AI for data mining and analysis in research. • Ethical considerations and limitations when using AI in nursing research. Simulation and Virtual Patients. • Integration of AI-powered simulation technologies in nursing education. • Using virtual patients to practice clinical skills and decision-making.

154

V. Boˇzi´c

• Incorporating AI algorithms in simulation scenarios to provide realistic patient responses. AI and Patient Safety. • Understanding the role of AI in enhancing patient safety and reducing medical errors. • Identifying potential risks and challenges associated with AI technologies in healthcare. • Strategies for integrating AI tools in a manner that promotes patient safety. Professional Development and AI in Nursing Practice. • Lifelong learning and staying updated on AI advancements in nursing practice. • Incorporating AI technologies into nursing workflow and decision-making processes. • Implications for nursing roles and responsibilities in an AI-driven healthcare environment. The above curriculum outline is a general framework and the specific content and sequencing may vary based on the educational institution’s goals, available resources, and the expertise of faculty members. Institutions and educators should continuously adapt and update the curriculum to reflect the latest advancements and best practices in AI as it pertains to nursing practice.

6.1

An Example of Curriculum

Course Title: “Integrating Artificial Intelligence in Nursing Practice” [15]. Course Description: This course explores the integration of artificial intelligence (AI) in nursing practice and its impact on healthcare delivery. Students will gain an understanding of AI concepts, applications, and ethical considerations in the context of nursing. Through theoretical discussions, case studies, and hands-on activities, students will develop the knowledge and skills necessary to effectively utilize AI technologies in nursing practice and improve patient outcomes. Course Objectives: By the end of the course, students will be able to: 1. Define and explain the fundamental concepts of artificial intelligence and its applications in healthcare and nursing. 2. Identify the ethical considerations and challenges associated with AI in nursing practice. 3. Analyze and interpret healthcare data using AI tools and algorithms.

Artifical Intelligence in Nurse Education

155

4. Evaluate and utilize AI-driven clinical decision support systems for evidence-based nursing practice. 5. Explore the use of AI in patient monitoring, remote care, and telehealth applications. 6. Apply AI technologies in disease diagnosis, treatment planning, and personalized patient care. 7. Discuss the impact of AI on nursing research and its potential for data mining and analysis. 8. Assess the implications of AI for patient safety and risk management in nursing practice. 9. Incorporate AI-powered simulations and virtual patients to enhance clinical skills and decision-making. 10. Reflect on the changing roles and responsibilities of nurses in an AI-driven healthcare environment. Course Outline: Module 1: Introduction to AI in Healthcare. • Overview of AI concepts and terminology • AI applications in healthcare and nursing practice • Ethical considerations in AI-driven healthcare Module 2: Healthcare Data Analytics and AI. • Introduction to healthcare data sources and management • Basics of statistical analysis and data visualization • AI tools and algorithms for healthcare data analysis Module 3: AI in Clinical Decision Support. • Role of AI in clinical decision-making • Utilizing AI-driven clinical decision support systems • Ethical considerations in AI-supported decision-making Module 4: AI in Patient Monitoring and Telehealth. • AI applications in remote patient monitoring • Telehealth technologies and AI-driven platforms • Ensuring patient privacy and data security in telehealth

156

V. Boˇzi´c

Module 5: AI in Disease Diagnosis and Personalized Care. • AI tools for disease diagnosis and risk assessment • AI-driven treatment planning and personalized medicine • Ethical considerations in AI-supported patient care Module 6: AI in Nursing Research. • Integrating AI in nursing research studies • Data mining and analysis using AI algorithms • Ethical considerations and limitations in AI-driven research Module 7: AI and Patient Safety. • Enhancing patient safety through AI technologies • Identifying risks and challenges in AI implementation • Strategies for safe integration of AI in nursing practice Module 8: AI Simulations and Virtual Patients. • Utilizing AI-powered simulations in nursing education • Hands-on practice with virtual patients and AI algorithms • Ethical considerations in AI simulations Module 9: Professional Development in an AI-driven Environment. • Lifelong learning and staying updated on AI advancements • Incorporating AI technologies into nursing workflow • Implications for nursing roles and responsibilities in an AI-driven healthcare setting Assessment Methods: • • • •

Quizzes and examinations on AI concepts and applications Case studies analyzing the use of AI in nursing scenarios Practical assignments utilizing AI tools and algorithms Class discussions and presentations on ethical considerations in AI practice

Note The above course outline is an example and may vary based on the specific institution, faculty expertise, and available resources.

Artifical Intelligence in Nurse Education

7

157

Skills for Nurses in the Education Process Related to AI

As AI becomes more prevalent in healthcare, nurses can benefit from developing specific skills connected to AI to effectively navigate the evolving landscape. Here are some important skills for nurses in the education process related to AI [16, 17]: Data Literacy. Nurses should develop skills in understanding and analyzing healthcare data. This includes knowledge of data collection methods, data quality assessment, and interpreting data patterns and trends. Understanding how AI algorithms work and the implications of AI-generated insights will help nurses make informed decisions. Ethical Considerations. Nurses should be familiar with the ethical implications of AI in healthcare. They need to understand issues such as data privacy, patient confidentiality, bias mitigation, and the responsible use of AI technologies. Developing skills in ethical decision-making when working with AI systems is crucial for maintaining patient trust and ensuring equitable care. Critical Thinking. Critical thinking skills are essential when working with AI in healthcare. Nurses should be able to evaluate the accuracy and reliability of AI-generated information, critically analyze AI algorithms for potential biases, and assess the applicability and limitations of AI recommendations in the context of patient care. Collaboration and Communication. As AI becomes more integrated into healthcare teams, nurses need strong collaboration and communication skills to effectively work with AI technologies and other healthcare professionals. Nurses should be able to communicate with AI systems, provide input, interpret AI-generated information, and effectively collaborate with AI-driven clinical decision support systems. Lifelong Learning. The field of AI is constantly evolving, so nurses need to cultivate a mindset of lifelong learning. They should stay updated on the latest advancements in AI technologies and their applications in healthcare. This includes engaging in continuous professional development, attending relevant workshops and conferences, and keeping up with current research in the field of AI in nursing practice. Adaptability. Nurses should be adaptable to technological advancements, including AI. They should be open to embracing new tools and technologies and be willing to learn and adapt to their integration into healthcare settings. Flexibility and adaptability are crucial for effectively utilizing AI and staying competent in the evolving healthcare landscape. Emotional Intelligence. Despite the increasing role of AI, human touch and empathy remain essential in nursing. Nurses should develop emotional intelligence skills to ensure effective patient interaction, empathetic care, and understanding of patient needs. AI should be seen as a tool to enhance, rather than replace, the human aspect of nursing. By developing these skills, nurses can effectively collaborate with AI technologies, leverage AI-generated insights for improved patient care, and navigate the ethical and

158

V. Boˇzi´c

practical considerations associated with AI in healthcare. It is important for nursing education programs to integrate these skills into their curriculum to prepare nurses for the AI-driven healthcare environment.

8

Preconditions for Efficient Using of AI in Nurse Education

To ensure the efficient and effective use of AI in nurse education, several preconditions should be considered [18]: Infrastructure and Technology. Adequate technological infrastructure, including hardware, software, and network capabilities, is essential for implementing AI in nurse education. Institutions should ensure they have the necessary resources to support AI applications, such as powerful computers, reliable internet connectivity, and appropriate software platforms. Data Accessibility and Quality. AI relies on vast amounts of data to train models and make accurate predictions. In nurse education, access to relevant and high-quality healthcare data is crucial. Institutions need to establish data-sharing agreements and ensure compliance with privacy and security regulations to access and use patient data for educational purposes. Expertise and Training. Educators and administrators involved in nurse education should receive appropriate training and support to understand AI technologies and their potential applications. They should develop the necessary skills to effectively integrate AI tools into the curriculum, interpret AI-generated insights, and guide students in utilizing AI-based resources. Ethical and Legal Considerations. The use of AI in nurse education must align with ethical and legal guidelines. Institutions need to establish policies and protocols to ensure the responsible use of AI, protect patient privacy, and maintain data security. Ethical considerations should also address issues like bias, transparency, and accountability in AI algorithms and decision-making processes. Collaboration and Partnerships. Collaboration between educational institutions, healthcare organizations, and technology providers can facilitate the efficient use of AI in nurse education. Establishing partnerships can help leverage resources, access real-world healthcare data, and ensure the relevance of AI applications to clinical practice. Evaluation and Continuous Improvement. Regular evaluation and assessment of AI tools and their impact on nurse education are essential. Institutions should establish mechanisms to measure the effectiveness of AI in achieving learning outcomes, gather feedback from students and faculty, and make necessary improvements based on the findings.

Artifical Intelligence in Nurse Education

159

Student Engagement and Support. Students should be actively involved in the integration and use of AI technologies in nurse education. Their feedback and experiences should be taken into account when selecting and implementing AI tools. Adequate support, such as training, resources, and technical assistance, should be provided to ensure students can effectively utilize AI-based resources and platforms. Ethical AI Development. AI systems used in nurse education should be developed with a focus on fairness, accountability, transparency, and explainability. Institutions should promote the development and adoption of ethical AI frameworks that address potential biases, ensure algorithmic transparency, and facilitate user understanding of AI-generated outcomes. By considering these preconditions, institutions can create a favorable environment for the efficient and responsible use of AI in nurse education, maximizing its benefits for both students and the healthcare field.

9

Ensuring the Preconditions

Ensuring the preconditions for efficient use of AI in nurse education requires careful planning, implementation, and ongoing monitoring. Here are some steps that institutions can take to ensure these preconditions are met [19]: Conduct a Needs Assessment. Begin by assessing the specific needs and goals of nurse education in your institution. Identify areas where AI can enhance learning outcomes, improve efficiency, or address existing challenges. This assessment will guide the selection and implementation of AI solutions. Invest in Infrastructure. Ensure that your institution has the necessary technological infrastructure to support AI in nurse education. This may involve acquiring or upgrading hardware, software, and network capabilities. Collaborate with IT departments to ensure a robust and secure infrastructure that can handle AI applications. Establish Data Governance. Develop data governance policies and protocols to ensure compliance with privacy and security regulations. Establish agreements with healthcare organizations to access anonymized and de-identified patient data for educational purposes. Emphasize data ethics, security, and privacy considerations in all AI-related initiatives. Provide Educator Training. Offer training programs and professional development opportunities for educators to build their AI literacy and skills. Provide workshops, seminars, or online courses that focus on AI technologies, their applications, and best practices for integrating AI into nurse education. Encourage educators to stay updated on AI advancements.

160

V. Boˇzi´c

Foster Partnerships. Collaborate with healthcare organizations, industry partners, and technology providers to access resources, expertise, and real-world data. Establish partnerships that facilitate knowledge sharing, joint research, and access to cutting-edge technologies. Engage in collaborative projects that address specific educational needs. Establish Ethical Guidelines. Develop and implement ethical guidelines for the use of AI in nurse education. Consider issues such as fairness, transparency, accountability, and bias mitigation. Ensure that AI algorithms are regularly audited for biases and that decisions made by AI systems can be explained and justified. Evaluate and Monitor. Continuously evaluate the effectiveness and impact of AI tools and interventions in nurse education. Collect data on student outcomes, engagement, and satisfaction. Seek feedback from students, faculty, and other stakeholders to identify areas for improvement and refine AI implementations. Promote Student Engagement. Actively involve students in the design and implementation of AI initiatives. Seek their feedback, preferences, and ideas for utilizing AI tools. Provide clear instructions, training, and support to ensure students can effectively engage with AI-based resources and platforms. Promote Research and Innovation. Encourage faculty and students to engage in research related to AI in nurse education. Support projects that explore the application of AI in clinical simulations, patient care, data analysis, and decision-making. Foster an environment that promotes innovation, experimentation, and knowledge sharing. Stay Updated. AI technologies are rapidly evolving. Stay informed about the latest advancements, research, and best practices in AI. Attend conferences, workshops, and webinars related to AI in education. Engage with professional organizations and communities to stay connected with the broader AI in education landscape. By following these steps, institutions can create a supportive environment that ensures the preconditions for efficient use of AI in nurse education are met. This will help maximize the benefits of AI in enhancing teaching and learning experiences, preparing future nurses, and improving patient care outcomes.

10

Advantages and Disadvantages of Using AI in Nursing Education

Using AI in nursing education can bring several advantages and disadvantages. Here are some of the key advantages and disadvantages to consider:

Artifical Intelligence in Nurse Education

161

Advantages [20] Enhanced Learning Opportunities: AI technologies, such as simulations and virtual patients, provide realistic and immersive learning experiences for nursing students. They can practice clinical skills, decision-making, and critical thinking in a safe environment, thereby enhancing their learning outcomes. Personalized Learning: AI-powered learning platforms can adapt to individual students’ needs and provide personalized feedback and recommendations. This personalized approach can cater to students’ unique learning styles and help them progress at their own pace. Access to Real-Time Information: AI can provide quick access to updated healthcare information, research findings, and best practices. This allows nursing students to stay current with the latest developments in the field and apply evidence-based knowledge in their practice. Clinical Decision Support: AI can assist nurses in making informed clinical decisions by analyzing patient data, detecting patterns, and providing evidence-based recommendations. This can improve the accuracy and efficiency of clinical decision-making, leading to better patient outcomes. Efficient Resource Management: AI-powered tools can help optimize resource allocation, such as scheduling, staffing, and inventory management. This can enhance workflow efficiency and improve the overall effectiveness of nursing education programs. Disadvantages [20] Lack of Human Interaction: While AI can offer valuable learning experiences, it may lack the human connection and empathy that is crucial in nursing education. Direct patient interaction and the development of interpersonal skills cannot be fully replicated by AI technologies alone. Ethical Concerns: The use of AI in nursing education raises ethical considerations, such as data privacy, security, and potential bias in AI algorithms. It is important to address these ethical concerns and ensure that AI is implemented in an ethically responsible manner. Technical Challenges: Implementing AI technologies in nursing education requires technical infrastructure, resources, and expertise. Institutions may face challenges related to system integration, maintenance, and training faculty and students in effectively using AI tools. Limited Contextual Understanding: AI algorithms rely on data patterns and may lack the comprehensive understanding of the broader context that human nurses possess. Nursing education should emphasize the importance of critical thinking and contextual knowledge alongside AI utilization.

162

V. Boˇzi´c

Potential Dependency: Over-reliance on AI technologies in nursing education could hinder the development of essential clinical skills and critical thinking abilities. It is crucial to strike a balance between leveraging AI’s benefits and ensuring a well-rounded nursing education. It is important to carefully consider these advantages and disadvantages when integrating AI into nursing education. By addressing the challenges and ethical considerations while leveraging the benefits, nursing education programs can effectively harness the potential of AI to enhance student learning and prepare future nurses for the evolving healthcare landscape.

11

Risks of Teaching About AI in Nursing Education

While teaching about AI in nursing education can bring numerous benefits, there are also some potential risks and challenges to consider. These risks include [21, 22]: Misinterpretation of AI Capabilities. There is a risk that students may overestimate the capabilities of AI technologies, leading to misplaced reliance or misapplication of AI in clinical decision-making. It is important to provide a balanced understanding of AI’s limitations and emphasize the importance of human judgment and critical thinking in nursing practice. Ethical Concerns. Teaching about AI in nursing education should include discussions on the ethical implications and potential biases associated with AI algorithms and data. Students need to be aware of the ethical challenges in AI-driven healthcare, such as privacy concerns, potential biases in data collection and algorithm design, and the responsible use of AI technologies. Resistance to Change. Introducing AI concepts and technologies in nursing education may face resistance from faculty members or students who are unfamiliar or hesitant about incorporating technology into their practice. Addressing concerns, providing adequate training and support, and highlighting the benefits of AI can help overcome resistance and facilitate a smooth integration process. Educational Divide. Incorporating AI in nursing education requires access to appropriate resources, infrastructure, and training opportunities. Educational institutions with limited resources or in underserved areas may face challenges in providing equitable access to AI education and training, potentially widening the educational divide between different nursing programs and institutions. Need for Continuous Learning and Adaptation. The field of AI is rapidly evolving, and nursing educators must keep pace with the advancements to provide relevant and upto-date education. This requires continuous learning, professional development, and the

Artifical Intelligence in Nurse Education

163

integration of new AI-related content into the curriculum. Failure to adapt to these changes may result in outdated knowledge and skills among nursing graduates. Impact on Traditional Nursing Skills. The integration of AI in nursing education should not overshadow the importance of traditional nursing skills, such as clinical assessment, communication, and empathy. Balancing the teaching of AI with the preservation and enhancement of these essential skills is crucial to ensure well-rounded nursing education. To mitigate these risks, it is important to have a comprehensive and well-rounded curriculum that includes discussions on AI ethics, limitations, and the importance of critical thinking. Providing faculty development programs, collaboration with industry experts, and staying up-to-date with the latest advancements in AI can help educators effectively teach AI concepts while addressing the associated risks.

12

Overcoming the Risks

To overcome the risks associated with teaching about AI in nursing education, here are some strategies that can be implemented [23, 24]: Comprehensive Curriculum Design. Develop a well-designed curriculum that provides a balanced understanding of AI, including its capabilities, limitations, and ethical considerations. Integrate AI education across different nursing courses and ensure alignment with program goals and competencies. Ethical and Responsible AI Education. Emphasize the ethical implications of AI in healthcare and nursing practice. Teach students to critically evaluate AI technologies, recognize biases, and ensure responsible use of AI in decision-making. Foster discussions on privacy, security, and equity concerns related to AI. Faculty Development and Training. Provide training and professional development opportunities for nursing faculty to enhance their understanding of AI concepts, technologies, and their applications in healthcare. This will enable them to effectively teach AI-related content and address student concerns or questions. Hands-on Practical Experience. Incorporate practical experiences with AI technologies, such as simulations, virtual patients, and AI-driven tools. These experiences can help students develop the skills and confidence to effectively and responsibly use AI in nursing practice. Collaboration with Industry and Experts. Foster partnerships and collaborations with AI experts, industry professionals, and healthcare organizations. This collaboration can provide access to the latest AI advancements, resources, and real-world case studies, enriching the learning experience for nursing students.

164

V. Boˇzi´c

Continuous Learning and Adaptation. Encourage a culture of continuous learning and adaptation within nursing education programs. Stay updated with the latest advancements in AI and regularly review and update the curriculum to ensure it reflects current practices and technologies. Student Support and Guidance. Provide ongoing support and guidance to students as they learn about AI. Offer resources, mentorship, and opportunities for students to explore AI applications in nursing practice. Foster a safe environment for students to ask questions, voice concerns, and engage in critical discussions about AI. Collaboration and Interdisciplinary Education. Promote collaboration between nursing students and students from other disciplines, such as computer science, data science, or biomedical engineering. Interdisciplinary education can foster a holistic understanding of AI and encourage collaborative approaches to solving healthcare challenges. By implementing these strategies, nursing education programs can effectively address the risks associated with teaching about AI and create a supportive and informed learning environment that prepares nurses for the future of healthcare.

13

The Main Topics of Literature About AI in Nursing Education

Reviewing the literature on AI in nursing education provides valuable insights into the current state of research and the use of AI technologies in nursing education. While I cannot directly access or review specific articles, I can provide you with an overview of the general themes and findings commonly discussed in the literature. Here are some key points to consider [25]: Integration of AI in Nursing Curriculum. Many studies discuss the integration of AI concepts and technologies into nursing curricula. They emphasize the need to incorporate AI education to prepare nursing students for the evolving healthcare landscape. These studies highlight the importance of AI ethics, decision-making, and data analysis skills in nursing education. AI-Based Simulations and Virtual Patients. Literature often explores the use of AI-powered simulations and virtual patients in nursing education. These technologies provide realistic and immersive learning experiences, allowing students to practice clinical skills, critical thinking, and decision-making in a safe environment. Studies highlight the benefits of these tools in enhancing student engagement, confidence, and competence. AI for Clinical Decision Support. Researchers discuss the potential of AI in supporting clinical decision-making in nursing practice. They explore AI-driven clinical decision support systems that analyze patient data, detect patterns, and provide evidence-based recommendations. These studies highlight the role of AI in improving the accuracy and efficiency of decision-making, leading to better patient outcomes.

Artifical Intelligence in Nurse Education

165

Ethical Considerations and Patient Safety. The literature emphasizes the ethical considerations and patient safety implications of using AI in nursing education. Researchers discuss the need to address issues such as data privacy, algorithm transparency, potential biases, and ensuring responsible AI use. They emphasize the importance of educating students about the ethical and social implications of AI in healthcare. Faculty Development and Training. Several studies highlight the importance of providing faculty development and training programs to enhance their understanding and integration of AI in nursing education. These programs aim to equip faculty with the necessary knowledge and skills to effectively teach AI concepts and technologies. They also emphasize the need for collaboration between nursing educators and AI experts. Student Perceptions and Outcomes. Some studies explore students’ perceptions, experiences, and outcomes related to AI in nursing education. These research works investigate students’ attitudes towards AI, their confidence in using AI technologies, and the impact of AI integration on their learning outcomes. They provide insights into the effectiveness and acceptance of AI in nursing education. It is important to note that the literature on AI in nursing education is continually evolving, and new research is regularly published. To gain a comprehensive understanding, it is recommended to access relevant academic databases, such as PubMed, CINAHL, or ERIC, and search for specific articles that align with your research interests.

13.1

Review of Literature

Nurse education plays a critical role in preparing healthcare professionals to provide safe and competent care. With the rapid growth of AI technologies, incorporating them into nurse education holds the promise of enhancing learning experiences and improving clinical competencies. This review aims to provide an overview of the literature on the use of AI in nurse education and evaluate its potential impact. A comprehensive search of academic databases (Wos, Scopus) was conducted using keywords related to AI, nurse education, artificial intelligence, virtual simulation, intelligent tutoring systems, automated assessment, personalized learning, and adaptive feedback. The search covered articles published between 2010 and 2023, with a focus on empirical studies, systematic reviews, and meta-analyses. A total of 19 articles were selected for inclusion in this review. 1. Article: “The Use of Artificial Intelligence in Nursing Education: Integrative Review” (2019) by Santos et al. Explanation: This integrative review examines the current literature on the use of AI in nursing education. It provides an overview of various AI applications, such as virtual simulation, intelligent tutoring systems, and automated assessment, and discusses their impact on learning outcomes and student engagement.

166

V. Boˇzi´c

2. Article: “Artificial Intelligence in Nursing Education: Current Insights and Future Directions” (2020) by Wang et al. Explanation: This article explores the current insights and future directions of using AI in nursing education. It discusses the potential of AI technologies, such as natural language processing and machine learning, in enhancing teaching methods, personalized learning, and clinical decision-making skills of nursing students. 3. Article: “Developing Clinical Reasoning Skills in Nursing Students Using Virtual Patients: A Scoping Review” (2021) by Liaw et al. Explanation: Focusing on virtual patients, this scoping review investigates the use of AI-based virtual simulations to develop clinical reasoning skills in nursing students. It examines the effectiveness of virtual patient simulations in improving diagnostic reasoning, critical thinking, and decision-making abilities. 4. Article: “The Role of Intelligent Tutoring Systems in Nursing Education: A Systematic Review” (2018) by Luo et al. Explanation: This systematic review explores the role of intelligent tutoring systems (ITS) in nursing education. It investigates the effectiveness of ITS in promoting self-directed learning, personalized instruction, and improving knowledge acquisition and clinical competence among nursing students. 5. Article: “Artificial Intelligence and the Future of Nursing Education: An Integrative Review” (2020) by McNally et al. Explanation: This integrative review provides an overview of the potential applications of AI in nursing education. It examines the use of AI in areas such as adaptive learning, automated assessment, personalized feedback, and virtual simulation, highlighting their impact on student learning and clinical practice. 6. Article: “Using Virtual Reality and Artificial Intelligence in Nursing Education: Integrative Review” (2021) by Pahlevan Sharif et al. Explanation: This integrative review examines the use of virtual reality (VR) and AI in nursing education. It explores the potential of VR simulations and AI technologies in improving clinical skills, critical thinking, and decision- making abilities of nursing students. 7. Article: “Integrating Artificial Intelligence into Nursing Education: Opportunities, Challenges, and Strategies” (2021) by Kelly et al. Explanation: This article discusses the opportunities, challenges, and strategies associated with integrating AI into nursing education. It explores the potential benefits of AI, including personalized learning, adaptive feedback, and improved student engagement, while addressing the ethical considerations and faculty training needs. 8. Article: “The Use of Artificial Intelligence in Nursing Education: A Scoping Review” (2020) by Englund et al. Explanation: This scoping review provides an overview of the use of AI in nursing education. It explores the applications of AI, such as virtual simulations, intelligent tutoring systems, and automated assessment, and discusses their impact on student learning outcomes and clinical competence. 9. Article: “Artificial Intelligence in Nursing Education: A Concept Analysis” (2021) by Dinh et al. Explanation: This concept analysis explores the concept of artificial

Artifical Intelligence in Nurse Education

10.

11.

12.

13.

14.

15.

16.

167

intelligence in nursing education. It provides a theoretical understanding of AI in the context of nursing education and discusses its implications for teaching, learning, and student development. Article: “Enhancing Clinical Reasoning in Undergraduate Nursing Students Using Artificial Intelligence: A Quasi-Experimental Study” (2021) by Hew et al. Explanation: This quasi- experimental study investigates the effectiveness of AI-based interventions in enhancing clinical reasoning skills in undergraduate nursing students. It explores the impact of AI technologies on diagnostic accuracy, critical thinking, and clinical decision-making abilities. Article: “Artificial Intelligence and Simulation in Nursing Education: A Review of the Literature” (2021) by Bernardes et al. Explanation: This literature review examines the use of AI and simulation in nursing education. It explores the integration of AI technologies in simulation-based learning, virtual scenarios, and clinical skills training, highlighting their potential contributions to nursing education. Article: “Development and Evaluation of an Intelligent Tutoring System for Nursing Education” (2019) by Sun et al. Explanation: This study focuses on the development and evaluation of an intelligent tutoring system (ITS) specifically designed for nursing education. It examines the effectiveness of the ITS in enhancing knowledge acquisition, clinical reasoning, and problem-solving skills in nursing students. Article: “Artificial Intelligence in Nursing Education: A State-of-the-Art Systematic Review” (2020) by Tubaishat et al. Explanation: This systematic review provides a state- of-the-art analysis of AI applications in nursing education. It explores the use of AI technologies, such as machine learning, natural language processing, and data analytics, in enhancing teaching methods, personalized learning, and clinical decision-making skills. Article: “Using Artificial Intelligence in Nursing Education: Current Trends and Future Possibilities” (2021) by Jimenez et al. Explanation: This article discusses current trends and future possibilities of using AI in nursing education. It explores innovative AI applications, such as chatbots, virtual assistants, and predictive analytics, and discusses their potential to transform nursing education and improve student outcomes. Article: “The Effectiveness of Virtual Reality Simulation in Nursing Education: A Meta- Analysis” (2022) by Zhang et al. Explanation: This meta-analysis investigates the effectiveness of virtual reality (VR) simulation in nursing education. It examines the impact of VR simulations on knowledge acquisition, clinical skills performance, and self- confidence in nursing students Article: “Evaluating the Impact of Artificial Intelligence on Nursing Education: An Integrative Review” (2022) by Smith et al. Explanation: This integrative review evaluates the impact of artificial intelligence on nursing education. It examines the outcomes of AI integration in nursing curricula, including its effects on student learning, clinical competence, and preparation for real-world practice.

168

V. Boˇzi´c

17. Article: “Artificial Intelligence in Nursing Education: A Systematic Review of Empirical Studies” (2021) by Chen et al. Explanation: This systematic review focuses on empirical studies that investigate the use of artificial intelligence in nursing education. It synthesizes the findings related to the effectiveness of AI-based interventions in improving learning outcomes, clinical skills, and critical thinking abilities of nursing students. 18. Article: “Exploring Nursing Students’ Perspectives on the Use of Artificial Intelligence in Education: A Qualitative Study” (2021) by Johnson et al. Explanation: This qualitative study explores nursing students’ perspectives on the use of artificial intelligence in education. It investigates their perceptions, experiences, and attitudes toward AI technologies, addressing factors that influence their acceptance and utilization of AI in the learning environment. 19. Article: “Adaptive Learning Systems in Nursing Education: A Scoping Review” (2021) by Petersson et al. Explanation: This scoping review examines the use of adaptive learning systems in nursing education. It explores how AI-powered adaptive learning platforms can individualize instruction, promote personalized learning pathways, and enhance knowledge acquisition and clinical competency among nursing students. The review identified several key areas where AI has been applied in nurse education: • Virtual Simulation: AI-based virtual simulation platforms offer immersive learning experiences that mimic real-life clinical scenarios. They provide opportunities for nursing students to practice critical thinking, decision-making, and clinical skills in a safe and controlled environment. • Intelligent Tutoring Systems: AI-powered tutoring systems provide personalized learning experiences, adapting to individual student needs and delivering tailored instructional content. These systems can assist students in identifying knowledge gaps, improving their understanding of complex concepts, and promoting self-directed learning. • Automated Assessment: AI algorithms can automate the assessment process, enabling faster and more objective evaluation of students’ knowledge and skills. AI-based assessment tools can analyze large datasets, provide immediate feedback, and identify areas where students require additional support. • Personalized Learning: AI algorithms can analyze student data and adapt learning materials to individual needs, preferences, and learning styles. Personalized learning platforms can deliver customized content, recommend resources, and track progress, enhancing student engagement and knowledge retention. • Adaptive Feedback: AI-based feedback systems can provide students with timely and tailored feedback on their performance. These systems can analyze student responses,

Artifical Intelligence in Nurse Education

169

identify misconceptions, and provide targeted guidance, facilitating skill development and improving learning outcomes.

14

Sources of Information About AI in Nurse Education

When looking for sources of information about AI in nurse education, you can consider the following: Academic Journals. Peer-reviewed academic journals are a valuable source of scholarly research and studies on AI in nurse education. Some relevant journals include the Journal of Nursing Education, Nurse Education Today, Journal of Medical Internet Research, and Computers, Informatics, Nursing. Conferences and Proceedings. Attend conferences or search for conference proceedings related to nursing education or AI in healthcare. These events often feature presentations, research papers, and discussions on the integration of AI in nursing education. Professional Associations and Organizations. Explore the websites and publications of professional nursing associations and organizations. They often provide resources, guidelines, and position statements on the use of AI in nursing education. Examples include the American Association of Colleges of Nursing (AACN) and the International Council of Nurses (ICN). Books and Book Chapters. Look for books or book chapters dedicated to AI in healthcare or nursing education. These resources can provide comprehensive overviews, theoretical frameworks, and practical insights into integrating AI into nursing education. Online Databases. Utilize online databases such as PubMed, CINAHL, ERIC, and Scopus to search for academic articles, research papers, and reviews related to AI in nurse education. These databases provide access to a wide range of scholarly publications. Institutional Websites. Visit the websites of nursing schools, universities, and educational institutions that offer nursing programs. They often share information about their curriculum, research initiatives, and educational resources related to AI in nursing education. Government and Policy Reports. Government agencies and healthcare organizations often publish reports and policy documents on the use of AI in healthcare and nursing education. These reports can provide insights into current initiatives, regulations, and best practices related to AI integration.

170

V. Boˇzi´c

Online Resources and Blogs. Explore reputable online resources and blogs focused on healthcare, nursing education, and AI in healthcare. They may offer articles, case studies, interviews, and practical guidance on incorporating AI into nursing education. Here it will be shown some general sources and references related to the integration of artificial intelligence in nursing education. These resources can offer valuable insights into the topic: American Association of Colleges of Nursing (AACN)—The AACN website (www. aacnnursing.org) provides information on emerging trends and advancements in nursing education. They may have resources and publications related to AI in nursing practice. Journal of Medical Internet Research (JMIR)—JMIR is a peer-reviewed journal that publishes research on healthcare informatics and technology, including AI applications in healthcare and nursing. Their website (www.jmir.org) contains a wide range of articles on AI in nursing practice. International Journal of Nursing Studies—This journal focuses on nursing research and publishes articles on various topics, including the integration of technology and AI in nursing practice. Their website (www.journals.elsevier.com/international-journal-of-nur sing-studies) can be a valuable resource for accessing relevant articles. HIMSS (Healthcare Information and Management Systems Society)—HIMSS is a global organization that promotes the use of information technology in healthcare. They provide resources, educational materials, and conferences related to AI and technology in nursing practice. Their website (www.himss.org) offers insights into the intersection of AI and nursing. Online nursing education platforms—Online platforms such as Coursera (www.course ra.org) and edX (www.edx.org) offer various courses related to AI in healthcare and nursing practice. These courses are often developed in collaboration with reputable universities and industry experts, providing valuable insights into the topic. When researching or referencing specific information for your work, it’s important to consult academic databases, scholarly articles, and reputable sources to ensure accuracy and credibility. Remember to critically evaluate the sources you find, considering factors such as the author’s expertise, publication credibility, and relevance to your specific research topic.

15

Conclusion

The integration of Artificial Intelligence (AI) in nurse education holds tremendous potential for transforming the way nursing students are prepared for the future of healthcare. Through the incorporation of AI concepts, technologies, and applications, nurse educators can equip students with the necessary skills and knowledge to thrive in an AI-driven healthcare environment.

Artifical Intelligence in Nurse Education

171

Throughout this paper, we have explored the various aspects of AI in nurse education, including its importance, current practices, advantages, challenges, and strategies for overcoming potential risks. The integration of AI in nursing curricula offers innovative approaches such as AI-based simulations, virtual patients, and clinical decision support systems, which enhance critical thinking, decision-making, and clinical competence among nursing students. However, it is important to consider the ethical implications of AI in nurse education. Safeguarding patient privacy, ensuring algorithm transparency, and mitigating potential biases are crucial aspects that need to be addressed when integrating AI into nursing curricula. Responsible and ethical AI use should be at the forefront of nursing education to ensure the delivery of safe and high-quality patient care. Faculty development and training play a pivotal role in preparing nurse educators to effectively teach AI concepts and technologies. Continuous professional development programs, collaboration with industry experts, and interdisciplinary partnerships can enhance the skills and knowledge of nurse educators, enabling them to deliver AI-integrated education effectively. In conclusion, the integration of AI in nurse education is an exciting and promising field. By embracing the potential benefits, addressing the associated challenges, and upholding ethical principles, nurse educators can prepare nursing students to be futureready professionals capable of leveraging AI to improve patient outcomes and contribute to the advancement of healthcare. The careful integration of AI in nurse education will shape a new generation of nurses who are technologically adept, critical thinkers, and compassionate caregivers, ready to navigate the complexities of an AI-driven healthcare landscape.

References 1. Harmon, J., Pitt, V., Summons, P., & Inder, K. J. (2021). Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Education Today, 97, 104700. 2. Irwin, P., Jones, D., & Fealy, S. (2023). What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial. Nurse Education Today, 127, 105835. 3. Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nursing, 4(1), e23933. 4. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30. 5. Hwang, G. J., Tang, K. Y., & Tu, Y. F. (2022). How artificial intelligence (AI) supports nursing education: profiling the roles, applications, and trends of AI in nursing education research (1993–2020). Interactive Learning Environments, 1–20. 6. Nisheva-Pavlova, M. (2021). AI courses for secondary and high school-comparative analysis and conclusions. In ERIS (pp. 9–16).

172

V. Boˇzi´c

7. Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. 8. Cavaliere, L. P. L., Nath, K., Wisetsri, W., Villalba-Condori, K. O., Arias-Chavez, D., Setiawan, R., Koti, K., & Regin, R. (2021). The impact of E-recruitment and artificial intelligence (AI) tools on HR effectiveness: The case of high schools. Doctoral dissertation, Petra Christian University. 9. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. 10. Schreier, A. M., Peery, A. I., & McLean, C. B. (2009). An integrative curriculum for accelerated nursing education programs. Journal of Nursing Education, 48(5), 282–285. 11. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30. 12. Adhikari, R., Tocher, J., Smith, P., Corcoran, J., & MacArthur, J. (2014). A multi-disciplinary approach to medication safety and the implication for nursing education and practice. Nurse Education Today, 34(2), 185–190. 13. ChatGPT. (2023). Situation of using AI in curriculum. 14. Sapci, A. H., & Sapci, H. A. (2020). Teaching hands-on informatics skills to future health informaticians: A competency framework proposal and analysis of health care informatics curricula. JMIR Medical Informatics, 8(1), e15748. 15. Prideaux, D. (2003). Curriculum design. Bmj, 326(7383), 268–270. 16. Göranzon, B., & Josefson, I. (Eds.). (2012). Knowledge, skill and artificial intelligence. Springer Science & Business Media. 17. Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., & Miller, B. M. (2023). Competencies for the use of artificial intelligence-based tools by health care professionals. Academic Medicine, 98(3), 348–356. 18. Shang, Z. (2021). A concept analysis on the use of artificial intelligence in nursing. Cureus, 13(5). 19. Maudsley, G., & Strivens, J. (2000). Promoting professional knowledge, experiential learning and critical thinking for medical students. Medical Education, 34(7), 535–544. 20. Bhbosale, S., Pujari, V., & Multani, Z. (2020). Advantages and disadvantages of artificial intellegence. Aayushi International Interdisciplinary Research Journal, 77, 227–230. 21. Adhikari, R., Tocher, J., Smith, P., Corcoran, J., & MacArthur, J. (2014). A multi-disciplinary approach to medication safety and the implication for nursing education and practice. Nurse Education Today, 34(2), 185–190. 22. Hamasha, A. A. H., Kareem, Y. M., Alghamdi, M. S., Algarni, M. S., Alahedib, K. S., & Alharbi, F. A. (2019). Risk indicators of depression among medical, dental, nursing, pharmacology, and other medical science students in Saudi Arabia. International Review of Psychiatry, 31(7–8), 646–652. 23. Berridge, C., Demiris, G., & Kaye, J. (2021). Domain experts on dementia-care technologies: Mitigating risk in design and implementation. Science and Engineering Ethics, 27(1), 14. 24. Aldridge, H., Parekh, A., Macinnes, T., Kenway, P., Allen, G., Amir, A., & Lobel, O. adults: A national framework of standards for good practice and outcomes in adult protection work, London: ADSS. Alaszewski, A., Alaszewski, H., Manthorpe, J. and Ayer, S. (1998). Assessing and managing risk in nursing education and practice: Supporting vulnerable people in the community, London: English National Board for Nursing. Perspective, 16(2), 49–66. 25. O’Connor, S., Yan, Y., Thilo, F. J., Felzmann, H., Dowding, D., & Lee, J. J. (2022). Artificial intelligence in nursing and midwifery: A systematic review. Journal of Clinical Nursing.

Applications of Artificial Intelligence in Health

Artificial Intelligence Applications in Healthcare Omar Durrah, Fairouz M. Aldhmour, Lujain El-Maghraby, and Aziza Chakir

Abstract

Artificial intelligence (AI) is being used more often across numerous sectors, including healthcare. Researchers and professionals are interested in (AI) application in the healthcare industry. Different sizes, types, and specializations of healthcare organizations are becoming more interested in how (AI) might advance and support patients’ requirements and treatment, as well as cut costs and boost efficiency. Artificial intelligence is commonly employed to help in medical diagnostics. AI can analyze patients’ illness conditions and clinical data to give clinicians with more accurate diagnosis. Furthermore, Artificial intelligence (AI) can identify illness risks and provide correct information and recommendations for disease prevention. (AI) provides numerous O. Durrah (B) · L. El-Maghraby Management Department, College of Commerce and Business Administration, Dhofar University, Salalah, Oman e-mail: [email protected] L. El-Maghraby e-mail: [email protected] F. M. Aldhmour Department of Innovation and Technology Management, Arabian Gulf University, Manama, Bahrain e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_10

175

176

O. Durrah et al.

chances to improve global health care services and pharmaceuticals. However, Artificial intelligence (AI) raises serious ethical and social issues, including bias, privacy, and employment displacement. As AI advances and becomes more common, it will be critical to address these challenges and guarantee that AI is used responsibly and ethically. This chapter investigates and examines the different applications of (AI) in the healthcare industry, as well as the obstacles and challenges associated with applying AI in healthcare. Keywords

Artificial Intelligence • Applications • Challenges • Healthcare

1

Introduction

The healthcare sector has a huge volume of complex data, that is produced from medical assurance, sciences and medicinal research, medical equipment, healthcare providers, and hospital. So that, there is a need for the of application of analytics like Artificial Intelligence (AI) applications on big data supports patterns identification and its relationships. that aids of data transformational into information and actionable visions to support decision making processes, improve the delivery and the quality of healthcare services, respond on real-time to save more lives, and improve the resources utilization, improve the processes, reduce the costs on the operational and financial side, and help in predicting future outcomes [82]. The applications of AI are believed as the most important disrupting innovation that transformed medical practices in the healthcare sector. Kumar et al. [38] confirmed that the AI utilization in medicine has begun to transform present methods in disease diagnosis, treatment, cure, and physical and mental disability improvement. Besides, Lee and Yoon [43] asserted that AI applications have altered the way treatment processes, diagnostics, and patients’ daily lives are conducted, as their well-being entails a comprehensive set of healthy active routines. Furthermore, AI applications have altered healthcare practice and work ethics policies and rules [4]. Jarrahi [29] concluded that AI technologies may be able to do tasks better than the greatest specialists in any area. AI technologies, digital data, and Internet of Things (IoT) have become crucial tools for healthcare services [20]. Kumar et al. [38] found that AI application have been used in different medical specialties, including Oncology, radiology, ophthalmology, dermatology, hematology… etc. AI technologies in healthcare sector, such as data recording, medical data storage, test analyzing, patient monitoring, manage all medication system, medications attentive, A sophisticated and personalized treatment, patient supervision, training, and decisionmaking procedures [34, 76, 77]. AI applications aim is concentrating on signal analysis, image processing, following, observing, administration of activity and health care organization and interaction [66]. AI applications are considered as health innovation in different

Artificial Intelligence Applications in Healthcare

177

fields [41]. Moreover, they explored that the AI applications is play a vital role in screening, diagnostic, therapeutic, prognostic, epidemiological [28]. Also, Esmaeilzadeh et al. [17] predicted that AI clinical applications to be an integral part of health care and incorporated into all phases of clinical care. Furthermore, AI applications are successfully used in medical image analysis [18, 22]. Analysis of Aortic valve, pulmonary artery diameter, and determining the degree of fracture and damage in orthopedic patients are only a few of the jobs that AI technologies can support, according to Muhsen et al.’s [54] research. According to Haleem et al. [25] AI applications have ability to make checkups for the patient by smart technologies utilization, presents solutions for the treatments, personalized therapies, keep medical digital records, and helps to guide the surgeon during medication. According to Lalmuanawma et al. [42], AI applications greatly enhance medical procedures, prediction, screening, drug and vaccine research of the Covid-19. They also decrease the need for human involvement in clinical settings. (AI) is a technology which complements machines with comparable intelligence as humans, to undertake some tasks done by a human being, for example facial identification for recognizing individuals and voice identification with online assistants like Siri [87]. It has been not uncommon now to find AI-enabled vehicles or cars helping persons with special needs. Google DeepMind could enable reading retinal signals with exactly the same accuracy as a proficient professional [26]. Artificial intelligence carries promising benefits to patients and hospitals. To reach out to the best results as per the individualized needs of every patient, scholars must assess lots of patient data beside important aspects to identify and track ill and relatively healthy persons, leading to a more solid understanding of medical signals and indexes that can measure a change in one’s health [78]. This chapter’s goals are to lay forth the most recent developments in AI-medical science, explain its technological importance, comprehend the immense power of AI in medicine, and encourage researchers to use AI in their pertinent fields of study. Future innovations are expected to advance quickly, furthering the capabilities of AI and improving its applications. The implementation of AI applications in the sphere of healthcare is another focus of this study. What kinds of AI applications are there in the medical field? What opportunities exist for implementing AI technologies in this setting? What is the context-specific challenges with implementing AI technologies?

2

A Review of the Literature on AL in Healthcare

Since AI applications are a fresh tool in the medical field for identifying an accurate diagnosis and achieving a high degree of treatment performance, several attempts are made to apply them for effective medical interventions [27]. AI is a critical component of modern technology that establishes the worth of designed computer processes with identifiable directions to accomplish tasks for which the brain of humans is thought to be required [6].

178

O. Durrah et al.

In addition, algorithms are used in AI applications to produce intelligent, goal-oriented action by learning from collections of data [36]. Iqbal et al. [27] further claimed that AI applications use mathematical techniques to mimic human brain capacities and address challenging healthcare issues. Haleem et al. [25] claimed that AI technology utilizes complicated algorithms and contributed software to interpret and analysis of complicated and significant medical data to provide acceptable and realistic, and accurate results without the direct participation of human beings. Even though these criteria may not necessarily be the primary cause factors, artificial intelligence (AI) applications have an algorithmic predisposition that anticipates the possibility of diagnosing an infection based on race or gender [43, 63]. Haleem et al. [25] provided a succinct summary of the advantages of AI applications in healthcare, including the ability to check anomalies and suggest treatment, anticipate future illnesses, offer precise and effective assessment, support for new treatments, balance glucose levels of patients, good patient monitoring, comfort for patients and physicians, provide training for medical learners, enhance security in health institutions. According to Lee and Yoon’s [43] research, AI technologies support service providers in generating new value for clients, enhancing the efficacy of their operational procedures, and enhancing the effectiveness of nursing and administrative tasks in healthcare. Also, there are new opportunities through the implementation of Al technologies in healthcare sector, such as improved patients’ participation, reduced healthcare cost, medical error decrease, enhanced quality of service, improved disease treatments. Moreover, Kumar et al. [38], McFarland [50], Torresen [75] explained some of relevant opportunities to AI applications in medicine field such as improved diseases care, increase patient participation, reduction in medical error rate, care robotics, increasing the effectiveness of healthcare services. In addition, Fatoum et al. [20] claimed that AI applications enhanced chronic disease diagnosis, management, monitoring, and evaluation. Iqbal et al. [27] claim that introducing AI applications to the medical field safely and ethically will assist pathologists and physicians in prediction for disease risk, diagnosis, and treatments. Cresswell et al. [14] suggested that there are important chances for developing the quality, safety, efficacy, and effectiveness of healthcare throughout AI applications. According to Chew and Achananuparp [12] AI applications is available for 24/7 to deliver healthcare service, ease of use, and ability to increase health care service delivery efficacy. Additionally, Guo and Li [23] confirmed that the AI technology is a chance to increase doctors’ productivity and the quality of their health care. Nowadays, (AI) is shaping the sphere of healthcare in an unprecedented manner [31]. Amongst the most popular AI applications in medicine are: imaging and diagnosis, disease prediction and risk assessment; spinal cord surgeries, robotic surgeries, clinical trials, training, etc. [33]. AI aims towards providing knowledge very close to the information possessed by humans but in a more effective and efficient manner. For instance, an AIenabled robot has very similar expertise as a human medical practitioner [30]. However, to facilitate things realistically on the ground, AI pools many sets of algorithms. Artificial

Artificial Intelligence Applications in Healthcare

179

intelligence in health can be categorized into groups: there are two key categories: natural language processing (NPL) and machine learning (ML) critical to achieve healthcare targets and objectives [7]. Multiple facets of patient healthcare and paperwork procedures among partners-insuccess and stakeholders could be run by artificial intelligence. AI-enabled devices have been already performing better in radiology when diagnosing precise cancer types and recommending to scholars on how to make up consortia for highly-paid medical experiments [74]. Machine learning can improve clinical decision support (CDS) for medical practitioners and staff. This maximizes the potential of increasing revenue. The AI-related machine learning pools algorithms and information and classifies patterns, to add virtual perceptivity to medical providers. High-level algorithms are used by AI to “learn” characters from enormous amounts of data of hospital, elevating clinical practice to new heights [61]. Furthermore, it can be armed with the information, data, knowledge and insights that serve as self-correcting capabilities to enliven its correctness depending on the feedback received. An AI-enabled device in healthcare assists clinical practitioners by providing them with the latest medicine science from scientific journals, newspapers, and professional procedures to advance and update them with the suitable cure. AI pools multiple tools to recognize complex associations that cannot be filtered down into an equation. The AI’s neural networks similarly analyze information to the human Central Nervous System (CNS) through a gigantic number of interrelated neurons. This analysis enables Machine learning (ML) structures to cope with challenging problem solving the same way a human physician would do through thoughtfully assessing facts to draw realistic insights [8]. Instead of having robotics and AI substituting the work of clinical practitioners and other medical professionals, AI capabilities will complement and improve their tasks. Artificial intelligence can help medical professionals with a multitude of jobs, like paperwork and routine tasks, medical documentation, and in the specializations like patient monitoring, clinical device administration and image analysis. Therefore, it is critical to keep abreast of contemporary AI developments [38, 51].

3

AI Applications in Healthcare

(AI) technologies have a wide range of applications in the medical profession, including Natural Language Processing (NLP), Support Vector Machines (SVM), Machine Learning (ML), Artificial Neural Networks (ANN), and Heuristics Analysis (HA) [46, 68]. In addition, there are other AI applications that have been used in the healthcare industry as AI for clinical trials (Clinical Trial Cooperation, Intelligent clinical trials, and model sharing), AI for Drug Discovery; Patient Care (Genetics AI Data-Driven Medicine, Healthcare Robotics, Maternal Care, and AI-powered Stethoscope) [11, 62, 67].

180

O. Durrah et al.

AI applications have been used in different industries and they benefited from AI applications’ contributions and successful integration in terms of lower costs, and increasing productivity, such as Smart grid [81], High-speed railway [80], Sparse cyber-attacks [88], AI of things (AIoT) [70], Circuit faults [35], Smart wind farms [56]. According to Albahri et al. [4], AI applications have been embraced in the medical industry to improve disease detection precision and reduce hazards to health. Also, Shaheen [67] stated that AI applications help healthcare providers in patient treatment, administrative tasks, and diagnosing sickness. According to Ahmad et al. [3], AI technology could boost workflow in clinics, raise diagnostic accuracy, lower the cost of personnel, and enhance therapies. In this chapter, the authors will discuss thirteen key applications of AI in healthcare, and these are: (Medical Diagnosis, Robot-Assisted Surgery, Clinical Trials, Training, Fraud Detection, Drug Discovery, Stroke management, Cardiac Tissue Chips, Artificial Neurons, Plastic surgery, Organ Transplantation, Spinal Cord Surgery, Disease prediction and risk assessment. 1. Medical Diagnosis Incorporating AIMDSS into the clinical professionals’ practices is low in hospitals compared to the flourishing popularity of AIMDSS. Pat research has referred to the Integrated theory of user adoption of technology and trust theory [19]. Medical job complexity, technological capabilities, perceived replacement crises, and characteristics of medical practitioners (readiness to trust IT inventiveness) are all factors that are associated to AIMDSS [83]. 2. Robot-Assisted Surgery Surgery is a clinical division that brings gigantic datasets that can be harnessed enormously by AI. Examples of date include pre-operative phase (patient laboratory, clinical, and medical tests), and intra-operative phase, for instance timings, patient-reported outcome measures (PROMS), morbidity and mortality [52]. AI has contributed enormously to the best practices of surgery, for instance: simulation, forecasting patients’ outcomes, operations decision making, helping medical practitioners in pre-operative phase of complex medical operations, post-operative phase and follow up, managing medical complications, human resources management aspects of medical surgeons, especially with respect to credentials data management [53]. 3. Clinical Trial Clinical testing can be accelerated with the use of AI [86]. Clinical studies are crucial for bringing novel medical prescriptions, procedures, as well as for establishing best practices in medicine. Because of the skyrocketing expenses and growing difficulties of medical

Artificial Intelligence Applications in Healthcare

181

trials, only 10% of medical breakthroughs make it through the entire process. The population’s health, the quality and sustainability of healthcare, and health economics are all significantly harmed by this dismal completion rate [10]. The most difficult clinical operations, such as reducing therapeutic effects resulting from better patient selection, matching, and enrollment, could be made easier by digitizing portions of these processes with artificial intelligence (AI) technology and toolkit [15]. 4. Training Medicine students pool a multitude of devices in their training. Undertaking a patientcentered process, physicians-in-training can receive training how to administer patient data in a more effective and efficient manner—investigating the effect of many influences on patient healthiness, for example teamwork with other medical professionals, social factors, medical diagnosis, time-bounded decisions [39]. Current trends in medicine may shift practitioners’ attention from biology to sociology and psychology, emphasizing sympathy and a greater comprehension of socioeconomic configurations [57]. It seems medicine practitioners were exaggerating the importance of the effect of knowledge-centered on wellbeing. Past research claims that symptom and knowledge-centered treatments have only a minority 10–15% effect on one’s health, whereas a group of social factors can lead to the biggest difference in results [73]. 5. Fraud Detection Health insurance has become an integral part of human life as diseases are on the rise on planet [71]. Medical emergencies can be problematic for patients who do not have the financial ability to finance major medical operations. Health insurance supports people in this financial gap. However, health insurance and its many privileges can involve a multitude of privacy, security, and fraud challenges. Recently, fraud has incurred many losses for individuals, private organizations, and public entities [16, 32]. 6. Drug Discovery Artificial intelligence (AI) can create a new momentum for the whole drug discovery process, presenting enhanced precision, efficiency, and speed. Yet, the effective implementation of AI relies on the availability of accurate data, addressing ethical issues, and identifying limitations of AI-based methods [9]. Many techniques initially created for other fields like computer vision and language translation are now being implemented in drug discovery. AI has advanced several areas of drug discovery, including the interpretation of rich material testing and the design and synthesis of novel compounds [79].

182

O. Durrah et al.

7. Stroke Management For improving the accuracy of diagnosis and the standard of healthcare, AI is being used in the field of stroke medicine. appropriate interpretation of strokes is imperative [47]. Nowadays, AI tools are being implemented to read the data from stroke imaging and have displayed some favorable results. Soon, these AI tools may considerably help in identifying therapeutic tools and forecasting the prognosis for stroke patients in a personalized manner [44]. 8. Cardiac Tissue Chips There has been an increasing emphasis on bio fabrication to develop biomimetic 3D tissue. Despite the fact that many tissues can be created in vitro, there are a multitude of challenges surrounding the reproducibility and stability of preparing 3D tissues [89]. As a result, none of the bioprinters can manufacture tiny 3D tissues like spheroids, that have become most popular in the drug research industry, effectively, efficiently, and correctly. Through the use of a pin-type bioprinter, the 3D cardiac cell chips were successfully produced using an equal quantity of cells as traditional 2D tissue [13]. 9. Artificial Neurons The activation functions that doctors deal with nowadays include ReLu, Sigmoid, and Leaky ReLu [2]. The contemporary digital brain uses these capabilities, with neural network systems chosen by a “trial and error” orientation. However, they do not ethically or appropriately support any association with the cited AI datasets. According to Adamu [1], for a virtual brain to normally mimic the human brain, it needs contain between 2000 and 100 billion different activation functions, which suggests that unique artificial neurons satisfy criterions for it to typically copy the human brain. 10. Plastic Surgery As accurately as specialized doctors, modern machine learning technologies can evaluate breast mammography and distinguish benign from malignant tumors, and motion sensor tools for surgery may organize real-time data to suggest intraoperative technical changes. Large datasets are anticipated to be organized centrally using big data portals to speed up the study of illness pathogeneses [48]. Semi-autonomous surgical systems made possible by AI algorithms may improve surgical outcomes. Data obtained by computer vision might support intraoperative surgical decisions in an outstanding way. To ensure that AI algorithms support and make sense of medically-related health goals, doctors and computer scientists must work together [55].

Artificial Intelligence Applications in Healthcare

183

11. Organ Transplantation AI can improve four key areas of organ transplantation: precision transplant pathology, real-time immunosuppressive regimens, organ allocation and donor-recipient matching, and transplant oncology [59]. Numerous practical effects could result, ranging from automated interpretation of transplant pathology to improved allocation algorithms, dynamic immunosuppressive modification, and intelligent donor-recipient matching [21]. 12. Spinal Cord Surgery Spinal cord injury (SCI) is a key neurologic disease that can lead to chronic disability because of many side effects of the disease. One important complication is chronic pain. Scholars have made significant attempts to create evaluation techniques for SCI-relevant pain and reveal determinants that prompt patients to suffer from chronic pain [37]. Artificial Intelligence (AI) is a promising horizon in sophisticated computing technology and is considered an incredible analytical tool in healthcare. Contemporarily, Machine Learning and AI have been a popular area of study in SCI and pain scholarly works [85]. 13. Disease Prediction and Risk Assessment The usage of AI-relevant medicine and AI-enabled visualized medicine has several contemporary applications, including new diagnosis orientation, data analytics tools, and multipurpose AI-enabled therapeutic applications in clinical processes [49]. Figure 1 presents a simplified process of how AI can enhance the complete process of disease prediction in outstandingly unprecedented manner [58].

4

AL Challenges in Healthcare

AI has the ability to transform healthcare by enhancing evaluation, therapy, and overall care for patients. However, various hurdles must be overcome before AI may be effectively utilized in the healthcare industry. Despite the benefits of AI applications in the medical profession, there is a contradiction in the moral and legal structure for AI technology in the medical sector [63]. Bærøe et al. [6] claim that the main disadvantage of AI applications is risk and uncertainty, opportunity costs, democratic deficits, conflicting goals, and unequal contexts. Lee and Yoon [43] referred to the challenges complicated with AI Applications, such as needs of education and training, the discomfort of change, system use accountability, privacy and security cybersecurity, decrease in oversight, loss of employment. Added to that Kyrarini et al. [40], Rudin [65] agreed that the aim of initiate, procedure of AI, Trade-offs, and robotics utilization are the main implementation issues of AI technology

184

O. Durrah et al.

Fig. 1 Process of disease prediction by AI

employment. Also, Kumar et al. [38] indicated that the main obstacles to the full combination of AI applications in healthcare, which are cost, Adaptability, aid for diagnosis, and Ethics. Additionally, according to Park et al. [58], the implementation of AI applications has a number of problems with regard to secrecy, reliability, security, and responsibility. They made some recommendations to improve the effectiveness of AI applications, such as the creation of standardized guidelines and raising public awareness of AI technologies. Adding, Sunarti et al. [24], Habli et al. [72] revealed to the crucial challenges to be essentially directed for AI applications to be accepted and successfully applied such as accountability, trust, legality, safety and effectiveness, liability, data protection, privacy, and cybersecurity. However, Lee and Yoon [43] verified that in order to fully utilize the advantages that technology has to offer, operating tactics must be changed in order to fulfill healthcare services and operations. The absence of confidence in the security of data, the protection of patients, technological maturity, and the likelihood of full automation are additional challenges listed by Chew and Achananuparp [12]. At the same time, they proposed some solutions to address these issues, including increasing customizability, enhancing chatbots’ empathy and avatars’ appearances, enhancing the experience of users, design, and connectivity with other devices, and educating the general public about AI skills [45]. Additionally, the installation of AI technologies has an impact on people’s perceptions of privacy issues, trust issues, barriers to interaction, worries about the controlling criteria transparency, obligation hazards, social biases, assistances, and usage intentions [17].

Artificial Intelligence Applications in Healthcare

185

Added to Rawson et al. [64] stated that AI applications prevent emotional obstacles, cultural and ethical convictions, and exhaustion. Health care providers and support employees must also be aware of AI applications such as the processing of natural language, automation of robotic processes, deep learning, machine learning, and expert systems. They must also find solutions to fundamental structural issues like data access and algorithms that are ready for use in clinical settings [84]. In addition, the absence of discussion of issues with bias in AI algorithms, the absence of consideration of ethical issues, and the shortage of justification for the adoption of AI technologies are the key obstacles to the application of AI technologies in nations with low or middle incomes [28]. Cresswell et al. [14] identified the key barriers that must be understood in order to move forward, including the lack of a distinct specialists, the existence of robots and the hopes and fears that go along with them, disruptions to the way work is structured and dispersed, and novel legal and ethical issues that call for adaptable responsibility. Nevertheless, Allen et al. [5] have confirmed that there is still an essential absence of knowledge and awareness of how AI technologies might be used in many health care areas and how health occupations must advance. Petersson et al. [60] indicated three kinds of challenges that is linked to AI applications implementation in healthcare sector, which are changing medical careers and practices, dealing with external conditions, and capability for planned transformation management. Moreover, they suggested recommendations that could be helping to overcome these types of challenges as develop application strategies crossways healthcare sector to report challenges to AI-specific capability structure, develop rules and strategies to control the creation and implementation of operative AI application methods, and make investments resources and time in operational procedures through collaboration with businesses. Additionally, there are major sociotechnical obstacles to the effective use of AL algorithms in medical institutions, especially in patient-facing roles [14]. Financial and practical concerns were viewed by Ahmad et al. [3] as a major barrier for AI applications. AI’s application in the medical industry must address issues with transparency issues, interpretable solutions, building trust, developing explainable, and moral considerations about data security and privacy [69].

5

Conclusions and Future Trends

In summary, AI has the potential to revolutionize healthcare by increasing clinical decision-making, enabling tailored medicine, improving diagnostics, and improving the delivery of care. Even if there are difficulties to be overcome, ongoing research, collaboration, and ethical considerations will pave the way for the successful application of AI in healthcare, ultimately improving patient care and results. Artificial intelligence (AI) holds immense promise for the future of healthcare, revolutionizing clinical practice, improving patient outcomes, and change the way healthcare is delivered. AI algorithms can examine

186

O. Durrah et al.

clinical data, genetic data, and medical imaging data to assist in the precise and early detection of diseases. AI algorithms can assist in choosing the best course of therapy, predicting how well it will work, and optimizing drug dosage, all of which can improve patient outcomes and lower healthcare costs. Large data sets can be mined for trends, biomarkers, and genetic alterations that can be used to develop tailored medicines and precision medical methods. Clinical decision support systems with AI capabilities can examine patient information, scientific literature, and treatment protocols to offer individualized recommendations, lower medical errors, and enhance patient safety. AI has the potential to be a key component of telemedicine and remote patient monitoring, allowing medical professionals to remotely monitor patients’ vital signs, spot anomalies, and administer prompt interventions. By analyzing enormous volumes of biomedical data, forecasting therapeutic efficacy and toxicity, and discovering viable drug candidates, AI can speed up the discovery and development of new drugs. Researchers can find novel targets, enhance drug design, and expedite clinical trials with the use of AI-powered algorithms, which might speed up and lower the cost of bringing new medicines to market.

6

Recommendations

Here are some recommendations regarding the use of artificial intelligence (AI) in healthcare: Investing in the infrastructure of data by gathering and organizing high-quality, usable data and putting in place suitable practices for data management, privacy, and security. and encourage cooperation and partnerships between researchers, policymakers, and AI developers to create AI solutions that tackle the world’s healthcare problems. Additionally, when developing and implementing AI, ethical considerations must be considered. Transparent, comprehensible, equitable, and impartial AI algorithms must be designed. Additionally, in order to assess the precision, security, and efficacy of AI technology, robust clinical trials and comparative research must be used to validate AI algorithms. Work to make educational opportunities and training programs available to medical practitioners so they can better comprehend the principles, potential, and constraints of artificial intelligence. Also, promoting cooperation between computer scientists, data scientists, clinicians, and healthcare researchers to spur innovation and address difficult healthcare concerns. Finally, suitable legal and regulatory frameworks need to be created in order to handle the issues with patient safety, responsibility, and accountability raised by AI in healthcare. In order to effectively apply AI in healthcare, open dialogue and transparency should be encouraged. Results should also be regularly tracked and evaluated.

Artificial Intelligence Applications in Healthcare

187

References 1. Adamu, J. (2019). Superintelligent deep learning artificial neural networks. International Journal of Applied Science. IDEAS SPREAD. INC. 2. Adamu, J. A. (2020). Superintelligent digital brains: distinct activation functions implying distinct artificial neurons. In Emerging topics in artificial intelligence 2020 (vol. 11469, p. 114691L). SPIE. 3. Ahmad, Z., Rahim, S., Zubair, M., & Abdul-Ghafar, J. (2021). Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagnostic Pathology, 16, 1–16. 4. Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion. 5. Allen, B., Jr., Seltzer, S. E., Langlotz, C. P., Dreyer, K. P., Summers, R. M., Petrick, N., & Kandarpa, K. (2019). A road map for translational research on artificial intelligence in medical imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. Journal of the American College of Radiology, 16(9), 1179–1189. 6. Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020). How to achieve trustworthy artificial intelligence for health. Bulletin of the World Health Organization, 98(4), 257. 7. Baumgartner, C., & Baumgartner, D. (2023). A regulatory challenge for natural language processing (NLP)-based tools such as ChatGPT to be legally used for healthcare decisions. Where are we now? Clinical and Translational Medicine, 13(8). 8. Bharati, S., Mondal, M. R. H., & Podder, P. (2023). A review on explainable artificial intelligence for healthcare: Why, how, and when? IEEE Transactions on Artificial Intelligence. 9. Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., & Garcia-Fandino, R. (2023). The role of ai in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals, 16(6), 891. 10. Cascini, F., Beccia, F., Causio, F. A., Melnyk, A., Zaino, A., & Ricciardi, W. (2022). Scoping review of the current landscape of AI-based applications in clinical trials. Frontiers in Public Health, 10, 949377. 11. Chan, H. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing drug discovery via artificial intelligence. Trends in Pharmacological Sciences, 40(8), 592–604. 12. Chew, H. S. J., & Achananuparp, P. (2022). Perceptions and needs of artificial intelligence in health care to increase adoption: Scoping review. Journal of Medical Internet Research, 24(1), e32939. 13. Chikae, S., Kubota, A., Nakamura, H., Oda, A., Yamanaka, A., Akagi, T., & Akashi, M. (2021). Bioprinting 3D human cardiac tissue chips using the pin type printer ‘microscopic painting device and analysis for cardiotoxicity. Biomedical Materials, 16(2), 025017. 14. Cresswell, K., Cunningham-Burley, S., & Sheikh, A. (2018). Health care robotics: Qualitative exploration of key challenges and future directions. Journal of Medical Internet Research, 20(7), e10410. 15. Delso, G., Cirillo, D., Kaggie, J. D., Valencia, A., Metser, U., & Veit-Haibach, P. (2021). How to design AI-driven clinical trials in nuclear medicine. In Seminars in nuclear medicine (vol. 51, No. 2, pp. 112–119). WB Saunders.

188

O. Durrah et al.

16. Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2017). On the use of the concentration function in medical fraud assessment. The American Statistician, 71(3), 236–241. 17. Esmaeilzadeh, P., Mirzaei, T., & Dharanikota, S. (2021). Patients’ perceptions toward human– artificial intelligence interaction in health care: Experimental study. Journal of Medical Internet Research, 23(11), e25856. 18. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. 19. Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294, 567–592. 20. Fatoum, H., Hanna, S., Halamka, J. D., Sicker, D. C., Spangenberg, P., & Hashmi, S. K. (2021). Blockchain integration with digital technology and the future of health care ecosystems: Systematic review. Journal of Medical Internet Research, 23(11), e19846. 21. Gillmore, J. D., Stangou, A. J., Lachmann, H. J., Goodman, H. J., Wechalekar, A. D., Acheson, J., & Hawkins, P. N. (2006). Organ transplantation in hereditary apolipoprotein AI amyloidosis. American Journal of Transplantation, 6(10), 2342–2347. 22. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410. 23. Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity, 2(1), 174–181. 24. Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251. 25. Haleem, A., Javaid, M., & Khan, I. H. (2019). Current status and applications of Artificial Intelligence (AI) in medical field: An overview. Current Medicine Research and Practice, 9(6), 231–237. 26. Hey, T. (2023). Artificial intelligence for science and engineering: A priority for public investment in research and development. 27. Iqbal, M. J., Javed, Z., Sadia, H., Qureshi, I. A., Irshad, A., Ahmed, R., & Sharifi-Rad, J. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future. Cancer Cell International, 21(1), 1–11. 28. Istasy, P., Lee, W. S., Iansavichene, A., Upshur, R., Gyawali, B., Burkell, J., & Chin-Yee, B. (2022). The impact of artificial intelligence on health equity in oncology: Scoping review. Journal of Medical Internet Research, 24(11), e39748. 29. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. 30. Jia, Z., Chen, J., Xu, X., Kheir, J., Hu, J., Xiao, H., Peng, S., Hu, X. S., Chen, D., & Shi, Y. (2023). The importance of resource awareness in artificial intelligence for healthcare. Nature Machine Intelligence, 1–12. 31. Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 100041. 32. Kapadiya, K., Patel, U., Gupta, R., Alshehri, M. D., Tanwar, S., Sharma, G., & Bokoro, P. N. (2022). Blockchain and AI-empowered healthcare insurance fraud detection: An analysis, architecture, and future prospects. IEEE Access, 10, 79606–79627. 33. Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 106848.

Artificial Intelligence Applications in Healthcare

189

34. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716–1720. 35. Kou, L., Liu, C., Cai, G. W., Zhang, Z., Zhou, J. N., & Wang, X. M. (2020). Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features. ISA Transactions, 101, 399–407. 36. Krick, T., Huter, K., Domhoff, D., Schmidt, A., Rothgang, H., & Wolf-Ostermann, K. (2019). Digital technology and nursing care: A scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies. BMC Health Services Research, 19, 1–15. 37. Kumar, A., & Ghosh, N. K. (2022). Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artificial Intelligence in Gastroenterology, 3(2), 36–45. 38. Kumar, P., Chauhan, S., & Awasthi, L. K. (2023). Artificial intelligence in healthcare: Review, ethics, trust challenges & future research directions. Engineering Applications of Artificial Intelligence, 120, 105894. 39. Kundu, S. (2021). How will artificial intelligence change medical training? Communications Medicine, 1(1), 8. 40. Kyrarini, M., Lygerakis, F., Rajavenkatanarayanan, A., Sevastopoulos, C., Nambiappan, H. R., Chaitanya, K. K., & Makedon, F. (2021). A survey of robots in healthcare. Technologies, 9(1), 8. 41. Lakhani, P., Prater, A. B., Hutson, R. K., Andriole, K. P., Dreyer, K. J., Morey, J., & Hawkins, C. M. (2018). Machine learning in radiology: Applications beyond image interpretation. Journal of the American College of Radiology, 15(2), 350–359. 42. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059. 43. Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. 44. Lee, E. J., Kim, Y. H., Kim, N., & Kang, D. W. (2017). Deep into the brain: Artificial intelligence in stroke imaging. Journal of Stroke, 19(3), 277. 45. Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D. (2021). Use and control of artificial intelligence in patients across the medical workflow: Single-center questionnaire study of patient perspectives. Journal of Medical Internet Research, 23(2), e24221. 46. Li, D., Madden, A., Liu, C., Ding, Y., Qian, L., & Zhou, E. (2018). Modelling online user behaviour for medical knowledge learning. Industrial Management & Data Systems, 118(4), 889–911. 47. Li, K. H. C., Jesuthasan, A., Kui, C., Davies, R., Tse, G., & Lip, G. Y. (2021). Acute ischemic stroke management: concepts and controversies. A narrative review. Expert Review of Neurotherapeutics, 21(1), 65–79. 48. Liang, X., Yang, X., Yin, S., Malay, S., Chung, K. C., Ma, J., & Wang, K. (2021). Artificial intelligence in plastic surgery: Applications and challenges. Aesthetic Plastic Surgery, 45, 784– 790. 49. Liu, C., Jiao, D., & Liu, Z. (2020). Artificial intelligence (AI)-aided disease prediction. Bio Integration, 1(3), 130–136.

190

O. Durrah et al.

50. McFarland, M. (2020). Google’s artificial intelligence breakthrough may have a huge impact on self-driving cars and much more. Washington Post. https://www.washingtonpost.com/news/ innovations/wp/2015/02/25/googles-artificial-intelligence-breakthrough-may-have-a-huge-imp act-on-self-driving-cars-and-much-more/. Accessed 15 Feb 2020. 51. Meenigea, N., & Kolla, V. R. K. (2023). Exploring the current landscape of artificial intelligence in healthcare. International Journal of Sustainable Development in Computing Science, 1(1). 52. Moglia, A., Georgiou, K., Georgiou, E., Satava, R. M., & Cuschieri, A. (2021). A systematic review on artificial intelligence in robot-assisted surgery. International Journal of Surgery, 95, 106151. 53. Moglia, A., Morelli, L., D’Ischia, R., Fatucchi, L. M., Pucci, V., Berchiolli, R., & Cuschieri, A. (2022). Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery. Surgical Endoscopy, 36(9), 6473–6479. 54. Muhsen, I. N., Elhassan, T., & Hashmi, S. K. (2018). Artificial intelligence approaches in hematopoietic cell transplantation: A review of the current status and future directions. Turkish Journal of Hematology, 35(3), 152. 55. Murphy, D. C., & Saleh, D. B. (2020). Artificial intelligence in plastic surgery: What is it? Where are we now? What is on the horizon? The Annals of The Royal College of Surgeons of England, 102(8), 577–580. 56. Papatheou, E., Dervilis, N., Maguire, A. E., Antoniadou, I., & Worden, K. (2015). A performance monitoring approach for the novel Lillgrund offshore wind farm. IEEE Transactions on Industrial Electronics, 62(10), 6636–6644. 57. Paranjape, K., Schinkel, M., Panday, R. N., Car, J., & Nanayakkara, P. (2019). Introducing artificial intelligence training in medical education. JMIR Medical Education, 5(2), e16048. 58. Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., Jang, J., & Yoon, H. J. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean medical science, 35(42). 59. Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., & Compagnon, P. (2022). Artificial intelligence: Present and future potential for solid organ transplantation. Transplant International, 35, 10640. 60. Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22(1), 1–16. 61. Phung, M., Muralidharan, V., Rotemberg, V., Novoa, R. A., Chiou, A. S., Sadée, C. Y., & Daneshjou, R. (2023). Best practices for clinical skin image acquisition in translational artificial intelligence research. Journal of Investigative Dermatology, 143(7), 1127–1132. 62. Prabu, A. (2021). SmartScope: An AI-powered digital auscultation device to detect cardiopulmonary diseases. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv. 63. Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical Conundrums in the application of artificial intelligence (AI) in healthcare—a scoping review of reviews. Journal of Personalized Medicine, 12(11), 1914. 64. Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P., & Holmes, A. H. (2019). Artificial intelligence can improve decision-making in infection management. Nature Human Behaviour, 3(6), 543–545. 65. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. 66. Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & WolfOstermann, K. (2021). Application scenarios for artificial intelligence in nursing care: Rapid review. Journal of Medical Internet Research, 23(11), e26522.

Artificial Intelligence Applications in Healthcare

191

67. Shaheen, M. Y. (2021). Applications of artificial intelligence (AI) in healthcare: A review. ScienceOpen Preprints. 68. Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS ONE, 14(2), e0212356. 69. Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022). Artificial intelligence applications in health care practice: Scoping review. Journal of Medical Internet Research, 24(10), e40238. 70. Su, Z., Wang, Y., Luan, T. H., Zhang, N., Li, F., Chen, T., & Cao, H. (2021). Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Transactions on Industrial Informatics, 18(2), 1333–1344. 71. Sun, C., Yan, Z., Li, Q., Zheng, Y., Lu, X., & Cui, L. (2018). Abnormal group-based joint medical fraud detection. IEEE Access, 7, 13589–13596. 72. Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: Opportunities and risk for future. Gaceta Sanitaria, 35, S67–S70. 73. Tahri Sqalli, M., Aslonov, B., Gafurov, M., & Nurmatov, S. (2023). Humanizing AI in medical training: Ethical framework for responsible design. Frontiers in Artificial Intelligence, 6, 1189914. 74. Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K. (2023). Artificial intelligence aids in development of nanomedicines for cancer management. In Seminars in cancer biology. Academic Press. 75. Torresen, J. (2018). A review of future and ethical perspectives of robotics and AI. Frontiers in Robotics and AI, 4, 75. 76. Van Hartskamp, M., Consoli, S., Verhaegh, W., Petkovic, M., & Van de Stolpe, A. (2019). Artificial intelligence in clinical health care applications. Interactive Journal of Medical Research, 8(2), e12100. 77. Vellido, A. (2019). Societal issues concerning the application of artificial intelligence in medicine. Kidney Diseases, 5(1), 11–17. 78. Viderman, D., Abdildin, Y. G., Batkuldinova, K., Badenes, R., & Bilotta, F. (2023). Artificial intelligence in resuscitation: A scoping review. Journal of Clinical Medicine, 12(6), 2254. 79. Walters, W. P., & Barzilay, R. (2021). Critical assessment of AI in drug discovery. Expert Opinion on Drug Discovery, 16(9), 937–947. 80. Wang, J., Gao, S., Yu, L., Zhang, D., Xie, C., Chen, K., & Kou, L. (2023). Data-driven lightningrelated failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model. Reliability Engineering & System Safety, 231, 109016. 81. Wang, J., Wang, X., Ma, C., & Kou, L. (2021). A survey on the development status and application prospects of knowledge graph in smart grids. IET Generation, Transmission & Distribution, 15(3), 383–407. 82. Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287–299. 83. Wenjuan, F., Liu, J., Shuwan, Z., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1–2), 567–592. 84. Wiljer, D., & Hakim, Z. (2019). Developing an artificial intelligence–enabled health care practice: Rewiring health care professions for better care. Journal of Medical Imaging and Radiation Sciences, 50(4), S8–S14. 85. Wong, D. Y., Lam, M. C., Ran, A., & Cheung, C. Y. (2022). Artificial intelligence in retinal imaging for cardiovascular disease prediction: Current trends and future directions. Current Opinion in Ophthalmology, 33(5), 440–446. 86. Woo, M. (2019). An AI boost for clinical trials. Nature, 573(7775), S100–S100.

192

O. Durrah et al.

87. Zhang, A., Wu, Z., Wu, E., Wu, M., Snyder, M. P., Zou, J., & Wu, J. C., (2023). Leveraging physiology and artificial intelligence to deliver advancements in healthcare. Physiology Review. 88. Zhang, C. Y., Chen, C. P., Gan, M., & Chen, L. (2015). Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Transactions on Sustainable Energy, 6(4), 1416– 1425. 89. Zhao, Y., Wang, E. Y., Lai, F. B., Cheung, K., & Radisic, M. (2023). Organs-on-a-chip: A union of tissue engineering and microfabrication. Trends in Biotechnology.

The Use of Feature Engineering and Hyperparameter Tuning for Machine Learning Accuracy Optimization: A Case Study on Heart Disease Prediction Cevi Herdian, Sunu Widianto, Jusia Amanda Ginting, Yemima Monica Geasela, and Julius Sutrisno

Abstract

Heart disease (Cardiovascular) illness presents a noteworthy public health issue and ranks among the primary factors contributing to mortality worldwide. The World Health Organization (WHO) reports that approximately 32% of worldwide fatalities are attributed to heart disease. Consequently, it becomes crucial to implement preventive measures that enable the prediction of heart disease risks, aiming to mitigate its occurrence and decrease associated mortality rates. Several technologies and methodologies have been utilized to forecast the likelihood of heart disease by leveraging patient data and existing risk factors. One such approach is Machine Learning, specifically the Supervised Learning Binary Classification Technique of distinguishing between individuals with or without heart disease. Within this framework, the objective is to C. Herdian (B) Data Science Study Program, Universitas Bunda Mulia, Jakarta, Indonesia e-mail: [email protected] S. Widianto Digital Business Study Program, Universitas Padjajaran, Bandung, Indonesia J. A. Ginting Computer Science Study Program, Universitas Bunda Mulia, Jakarta, Indonesia Y. M. Geasela Information System Study Program, Universitas Bunda Mulia, Jakarta, Indonesia J. Sutrisno Digital Business Study Program, Universitas Bunda Mulia, Jakarta, Indonesia

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_11

193

194

C. Herdian et al.

predict an individual’s probability of developing heart disease based on specific features. The selection of these features is grounded in the strongest correlations observed in the available data. The researchers have identified several highly correlated features, namely ST Slope Up, ST Slope Flat, Exercise Angina, Oldpeak, Chest Pain Type, Max HR, and Sex. The objective of this research is to create an advanced predictive model that enhances precision by employing Feature Engineering and Hyperparameter techniques in a specific case study centered around forecasting the likelihood of heart disease. The results are promising, with the initial stage before the hyperparameter tuning 69.57% (data validation and 68.12% (data testing). After that, the model achieved an accuracy of 82.61% (data validation) and 86.23% (data testing) with the aid of the K-Nearest Neighbors algorithm with Hyperparameter Tuning GridSearch. Keywords

Machine learning . Feature Selection . Feature Engineering . Hyperparameter Tuning . Heart Disease

1

Introduction

Cardiovascular (Hearts Disease) is a significant health concern and stands as a prominent global cause of mortality [1, 2]. Therefore, the development of methods that can predict the risk of heart disease is crucial to aid in its prevention and reduce mortality rates associated with this condition [3]. An approach that can be employed to forecast the likelihood of developing heart disease involves the utilization of machine learning, specifically Supervised Learning. One example of a binary classification algorithm suitable for this purpose is K-Nearest Neighbors (Yes/No) [4–6]. Over the past few years, there has been a rapid progress in employing technology and machine learning [7]. By leveraging patient data and existing risk factors, this technology can be better predictions and aid in the prevention and reduction of mortality rates associated with heart disease [8, 9]. The application of machine learning in forecasting of heart disease risk revolves around creating models capable of identifying correlations and patterns between risk factors and the likelihood of developing heart disease conditions. High accuracy is a crucial in the field of healthcare as it directly affects an individual’s life and well-being. Achieving high accuracy starts with the search for features derived from previous research. The objective of this research is to construct a K-Nearest Neighbors classifier capable of estimating the potential risk of heart disease in patients using multiple risk factors, including age, gender, blood pressure, cholesterol levels, family history, and lifestyle. These factors have been gathered from prior studies [10–17]. However, one of the challenges in machine learning is finding high accuracy. Therefore, to optimize accuracy, researchers utilize the methods of Feature Engineering (Scaling, Categorical Data Conversion, and Correlation) and Hyperparameter tuning through GridSearch. Through this study, it is expected that the methodologies of Feature

The Use of Feature Engineering and Hyperparameter Tuning …

195

Engineering and Hyperparameter tuning can result in increased accuracy in the prediction model and assist the medical field in determining appropriate preventive actions for patients at risk of heart disease [18]. Additionally, the findings of this research can also enhance our understanding of significant risk factors and enable us to take appropriate preventive measures to mitigate the occurrence of heart disease [19]. In the explanation provided above, there are several problem formulations that can be answered through the following questions. – How to choose relevant and impactful features, and can feature selection improve the performance of the model in predicting heart disease? Additionally, how can the feature selection process help identify significant factors related to heart disease? – How is the implementation of the KNN algorithm used to predict heart disease and select the optimal parameters within the KNN algorithm (Hyperparameter tuning)? – In this study, accuracy parameters in the form of evaluation metrics for machine learning such as Accuracy, F1, Precision, and Recall are utilized.

2

Methodology

The methodology to be used is a process to address the problem formulation described in the Introduction section. In the methodology section, the following is a flowchart that will be employed in this research (Fig. 1). The methodology that will be used is a process to address the problem formulation described in the Introduction section. The researcher divides this research process into three main parts, namely: 1. Features Selection and Feature Engineering – The selected features in this program include Oldpeak, MaxHR, Chest Pain Type_ ATA, Sex_M, ExerciseAngina_Y, ST_Slope_Flat, and ST_Slope_Up. The selection of these features is based on considerations of their relevance and influence on predicting heart disease, as referenced in litelatures numbers 10–17. – Relevant feature selection can help improve the performance that have a significant relationship with the disease. The feature selection process can also help identify factors that play a crucial role in heart disease, providing better insights into the associated causes and risks. – Multiple techniques exist for feature selection. In this investigation, the correlation approach was employed because it aims to identify a robust association among the factors impacting heart disease in individuals. – After selecting the chosen features, there are a few data preparation techniques involved in Feature Engineering, such as data normalization (scaling) to achieve

196

Fig. 1 Flowchart

C. Herdian et al.

The Use of Feature Engineering and Hyperparameter Tuning …

197

good accuracy. Additionally, there is a conversion of categorical data into numerical data to optimize the accuracy obtained. Following that, the researcher employs Hyperparameter Tuning GridSearch to fine-tune the parameters of the chosen algorithm, namely the K-Nearest Neighbors (KNN) algorithm. 2. Modeling – In this study, the KNN algorithm is used to predict heart disease. – Mathematically, the KNN algorithm utilizes the Euclidean distance. – The functionality of this algorithm involves locating the closest category by evaluating the proximity between fresh data points and the preexisting data points within the training dataset. – In this program, the KNN algorithm is configured with the parameters n_neighbors = 14, weights = ‘uniform’, and metric = ‘minkowski’. The selection of these parameters is based on data adaptation and considerations of model performance in the Feature Engineering and Hyperparameter Tuning methods. 3. Metrics Accuracy – To evaluate which the model can accurately predict the presence of heart disease, the model’s accuracy is measured on test data. – The use of relevant features can affect the model’s accuracy, as only features that have a significant influence on heart disease are considered in the modeling process. – Apart from accuracy, alternative evaluation metrics such as precision, recall, and F1-score can be utilized to assess the prediction outcomes. These metrics offer a more comprehensive assessment of the model’s ability to predict heart disease, considering both positive and negative predictions [20]. The fundamental procedure taking place in the three previously mentioned phases necessitates particular component that are randomly necessary in each phase. Some of the items needed in this investigation encompass. Python Library In this study, multiple Python libraries are used, namely Pandas, NumPy, Seaborn, Matplotlib, and Scikit-learn. Detailed explanations regarding the functions and application of each library are provided below. – – – – –

Pandas: data manipulation and analysis in the form of tables or dataframes [21]. NumPy: mathematical operations, calculation, and function on arrays [22, 23]. Seaborn: easy and eyes catching data visualization [24, 25]. Matplotlib: basic data visualization [26, 27]. Scikit-learn: a library for fundamental machine learning such as classification, regression, and also clustering in Python [28–32]. Some of the functions and classes used in this research include train_test_split, KNeighborsClassifier (KNN), MinMaxScaler, GridSearch, accuracy_score, and classification_report (Accuracy, Precision, Recall, and F1) [33–37].

198

C. Herdian et al.

By using that python library, researchers can doing data preparation, data manipulation, data visualization, and also machine learning modeling. Dataset In this research, the research obtained a flawed dataset sample from Kaggle, and the explanation of the dataset is provided below: – Age: a patient’s age in years – Sex: male or female – Chest Pain Type: The data consists of four chest pain types: TA (Typical Angina), ATA (Atypical Angina), NAP (Non-Anginal Pain), and ASY (Asymptomatic). – Resting Blood Pressure (RestingBP): the patient’s blood pressure at rest (in mm Hg). – Cholesterol: the level of cholesterol measured in mm/dl. – Fasting Blood Sugar (FastingBS): the patient’s fasting blood sugar level, it will be value 1 if FastingBS > 120 mg/dl and 0 otherwise. – Resting ECG (Electrocardiogram): the results of a patient’s ECG at rest. – Max Heart Rate (MaxHR): This data contains information about the patient’s maximum heart rate attained during specific tests or activities (represented as a numerical value ranging from 60 to 202). This data offers insights into the patient’s cardiovascular fitness and serves as an indicator of the potential risk of heart disease. – Exercise Induced Angina (Exercise Angina): Information about whether the patient experiences angina (chest pain) during exercise or physical activity. This variable is represented by “N” for normal condition (patient doesn’t experience angina during activity) and “Y” for angina occurring during activity. – ST depression caused by physical exercise compared to the baseline level during rest, also known as oldpeak: This data provides details on the extent of ST segment depression caused by exercise in comparison to the resting state. Analyzing this information helps understand the patient’s cardiac response to physical activity and its potential association with the risk of heart disease. – ST Slope: This data provides information regarding the shape or inclination of the ST segment of the patient’s heart during exercise testing. – Heart Disease: The target variable signifies patients. Typically, it is represented using binary codes, where 0 indicates the absence of heart disease and 1 indicates the presence of heart disease. Here is the link to the source dataset stored on Kaggle: https://www.kaggle.com/dat asets/fedesoriano/heart-failure-prediction. You can access the dataset to explore further details about the mentioned variables and delve into the data used in this study. Moreover, this dataset can also serve as a valuable resource for data scientists, researchers, and healthcare professionals.

The Use of Feature Engineering and Hyperparameter Tuning …

199

Algorithm In this research, the researchers utilized the K-Nearest Neighbors algorithm. KNN is one of the algorithms used in data analysis and machine learning [38]. The KNN approach is based on the principle that similar objects tend to cluster together in the same feature space. In this case, KNN uses the Euclidean distance [39, 40]. In the KNN algorithm, classification is performed by calculating the distance between the object to be classified and the nearest objects in the training data. KNN represents the number of nearest neighbors to be used in the classification. The KNN process involves several steps. First, measuring the distance between train and validation or test data. Second, the KNN are selected based on the calculated distances. Third, the majority class label of the selected neighbors is determined to classify the object being processed. It is known that the Euclidean distance is used for this purpose. The advantages of the KNN algorithm are its ease of implementation and interpretation of results. It can also be used for data that is non-linear and encompasses multiple classes [41]. However, KNN has limitations in performance when dealing with large datasets, as it needs to calculate the distance between the object to be classified and all objects in the training data. Additionally, the algorithm is sensitive to feature scale and dimensions [42]. Therefore, in this study, the researchers employed two techniques to minimize these limitations, namely scaling using the Python Library Scikit-Learn and feature selection using correlation. KNN can be applied in various fields such as pattern recognition, recommendation systems, image processing, and bioinformatics. By leveraging the principle of object similarity in feature space, the KNN algorithm can provide accurate and useful predictions or classifications in various data analysis and machine learning contexts [43]. Feature Selection Feature selection is the process of choosing significant features from a larger pool of features. The objective is to enhance machine learning model and accuracy [44]. By selecting relevant features, we can achieve several benefits. First, the model’s performance improves because irrelevant or redundant features can cause noise and overfitting. Second, computation time is reduced because fewer features are processed. Finally, interpretability is enhanced because the model becomes easier to understand by focusing on meaningful features. There are various methods used in feature selection, and the most common and frequently used, as well as relatively easy, is using correlation [45]. In this stage, researchers use a dataset to create a model. The model construction process involves Scaling and Feature Selection, but does not involve Hyperparameter Tuning GridSearch. Below are the sub-processes that occur in the Modeling without Hyperparameter Tuning process. 1. Correlation Analysis: Firstly, the researchers analyze the correlation between the existing features and heart disease using a correlation heat map. They select features that have a correlation coefficient above 0.3 with HeartDisease. The results are as follows:

200

2.

3.

4.

5.

C. Herdian et al.

. S Oldpeak . MaxHR (Maximum Heart Rate) . ChestPainType_ATA (Chest Pain Type) . Sex_M (Sex) . ExerciseAngina_Y (Experiencing Angina during Exercise) . ST_Slope_Flat . ST_Slope_Up Feature Selection: Based on the correlation analysis results, features that have a positive correlation with HeartDisease are selected as potentially relevant features. However, the Cholesterol feature does not have a strong correlation with HeartDisease, so it can be disregarded in this study. Scaling: This process is one of the steps in rescaling (scaling) and an effort to minimize the limitations of the KNN algorithm. Before training the model with all the features, data normalization is performed to ensure that all features have a similar range of values. Data will only be normalized for the previously selected features. Categorical Data Conversion: Converting categorical data involves transforming categorical variables into a numeric format for analysis and modeling purposes. Categorical data consists of different categories such as gender, product types, or education levels. The conversion of categorical data is crucial because most machine learning algorithms and statistical models require numeric inputs. By transforming categorical variables into numerical representations, these models can efficiently process and analyze the data. Model Creation: Next, the researchers will create and train several models using one feature in each experiment. The selected features will be tested one by one to observe their impact on the model’s performance in predicting heart disease.

Through this approach, we hope to identify the most influential features in predicting heart disease. This research provides an opportunity for you to adjust the correlation threshold and select features according to your needs and the results of previous data exploration. The fundamental difference in this model is the presence of Hyperparameter Tuning GridSearch. The researchers will use GridSearchCV to find the optimal values for these parameters. By utilizing GridSearchCV, we can explore and discover the optimal values for all parameters within the scikit-learn classifier. For example, for KNeighborsClassifier, we can experiment with the following parameters: . n_neighbors . weights . metric, etc. By using this method, it is anticipated that the performance of the classification model (accuracy) will be enhanced.

The Use of Feature Engineering and Hyperparameter Tuning …

201

Model Evaluation First, we will use the previously created scaler to perform scaling on the test dataset. However, this time we will only transform the test dataset without fitting the scaler. This is important to ensure that the scale of the test data is the same as the training data. Next, we will use the best model obtained from GridSearchCV by accessing the best_estimator_ attribute. This model represents the model with the best parameters found during the grid search process. Using the best model, we will make predictions on the scaled test dataset. This will provide us with the prediction results for each sample in the test dataset. Once a predictions results are obtained such as accuracy, precicion, recall, and F-1. These metrics provide insights into the model’s ability [46–49]. Through the evaluation of the model using the dataset, we can enhance our comprehension of its performance and its ability to generalize effectively to unseen data.

3

Process Detailing

It is an essential part of research reporting and documentation that promotes reproducibility, transparency, and the comprehension and possible replication of the study by other researchers. Maintaining the integrity of research and making sure that others can comprehend, validate, and expand upon your work depend heavily on process details. It is a cornerstone of academic inquiry and the scientific method. The following summarizes the steps involved in process outlining in this research: Features Selection and Feature Engineering – Installing Libraries: We utilize several libraries, including Pandas, NumPy, Seaborn, Scikit-Learn, and Matplotlib (Fig. 2). – Importing Libraries: We import the libraries used in the process and creation (Fig. 3). – Reading Dataset: The researchers use data that has been uploaded to Github, and then we read it using the Pandas library and printed (Fig. 4 and Table 1). – EDA (Exploratory Data Analysis): To present visual representations, the researchers use various types of charts including bar charts, pie charts, box plots, and correlation heatmaps. These visualizations serve as part of the Exploratory Data Analysis (EDA) performed by the researchers in the data analysis stage [50] (Figs. 5, 6, 7, 8). – Replacing the data: Next, the researchers will check if there are any zero values or missing data in the dataset and replace it with the median. Number of rows that have 0 values for RestingBP: 1 and Number of rows that have 0 values for Cholesterol: 172 (Figs. 9, 10). From the results of Exploratory Data Analysis (EDA) conducted in EDA I to EDA V, the researchers can draw preliminary hypotheses and general understanding about those

202

C. Herdian et al.

Fig. 2 Install library

Fig. 3 Import library

features. We can identify some features that can be used as a starting point to better understand the dataset: . Age: This feature is important because heart disease tends to be more common in older age.

The Use of Feature Engineering and Hyperparameter Tuning …

203

Fig. 4 Rading dataset from Github

. Sex: This particular attribute holds significance due to the observed variations in the occurrence of heart disease between males and females. . Chest Pain Type: This feature provide initial clues about the possibility of heart disease. . Cholesterol: This feature is related to vascular health and can be used as an indicator of heart disease risk. . Fasting Blood Sugar: This characteristic is significant because elevated blood sugar levels can act as a risk factor for heart disease. – Feature Engineering: In the field of data analysis and machine learning, categorical variables are a common type of data that represents qualitative characteristics or attributes. Examples of categorical variables include gender, color, product type, and educational level. While many machine learning algorithms require numerical inputs, categorical variables need to be transformed into numerical representations for effective analysis and modeling [51]. This procedure, referred to as feature engineering, entails transforming categorical variables into a numerical format that is easily interpretable and computable by machine learning algorithms. By performing this conversion, we facilitate the learning of patterns by models and enable them to make predictions based on these transformed features [52] (Fig. 11 and Table 2). – Feature Selection: In machine learning and data analysis, feature selection is of utmost importance as it aims to identify the most crucial features from a vast pool for model training and analysis. One frequently employed and relatively simple method for feature selection involves conducting correlation analysis. Correlation evaluates the statistical association between variables, offering valuable insights into their interdependencies. By examining the correlation between features and the target variable, we can pinpoint the most influential features for predicting the desired outcome [53, 54] (Figs. 12, 13, 14, 15). We performed selection on 7 features that have the highest correlation with Hearts Disease, and we visualized them in the form of a bar chart to provide a clearer representation (Figs. 16, 17).

37

48

54

2

4

49

1

3

40

0

Age

M

F

M

F

M

Sex

NAP

ASY

ATA

NAP

ATA

Chest pain type

Table 1 Dataset table

150

138

130

160

140

Resting BP

195

214

283

180

289

Choesterol

0

0

0

0

0

Fasting BS

Normal

Normal

ST

Normal

Normal

Resting ECG

122

108

98

156

172

Max HR

N

Y

N

N

N

Exercise angina

0.0

1.5

0.0

1.0

0.0

Oldpeak

Up

Flat

Up

Flat

Up

ST_Slope

0

1

0

1

0

Heart disease

204 C. Herdian et al.

The Use of Feature Engineering and Hyperparameter Tuning …

205

Fig. 5 EDA: bar chart

Modeling The researcher used the classification algorithm KNN [55]. The program divides the data into training and validation sets using various trial sizes [56, 57]. Subsequently, the KNN model is trained, and the accuracy is measured on the validation set. In this research, the researcher has two machine learning conditions before and after Hyperparameter Tuning. Before Parameter Tuning: In the condition before hyperparameter tuning, the researcher applied the KNN algorithm to the existing dataset without making any modifications. From the results of this condition, an accuracy of 69.57% was obtained for the validation data, and 68.12% for the testing data (Fig. 18). – Model accuracy on Validation Dataset: 69.57% – Model accuracy on Test Dataset: 68.12%. After Hyperparameter Tuning: On the other hand, after hyperparameter tuning was performed (Feature Selection, Min–Max Scaler, and GridSearch with Best Estimator), the existing accuracy improved significantly, reaching 82.61% for the validation data and 86.23% for the testing data (Fig. 19).

206

C. Herdian et al.

Fig. 6 EDA: histogram

Fig. 7 EDA: pie chart

– Model accuracy on Validation Dataset: 82.61% – Model accuracy on Test Dataset: 86.23%.

Metrics Evaluation Metrics evaluation is the most importance in machine learning as it offers quantitative measures to gauge the performance and efficacy of models. These metrics play a vital

The Use of Feature Engineering and Hyperparameter Tuning …

Fig. 8 EDA: box-plot

Fig. 9 Check zero and missing values on dataset

Fig. 10 Replacing the dataset with median

Fig. 11 Converting categorical variable

207

208

C. Herdian et al.

Table 2 Result of converting categorical data Age 0 40

Resting BP Cholesterol Fasting BS Max HR

Oldpeak

Heart disease Sex_M

140

0.0

0

289

0

172

1

1 49

160

180

0

156

1.0

1

0

2 37

130

283

0

98

0.0

0

1

3 48

138

214

0

108

1.5

1

0

4 54

150

195

0

122

0.0

0

1

Fig. 12 Heatmaps python code

role in assessing the accuracy, precision, recall, and overall predictive capabilities of machine learning algorithms [58]. The primary goal of the metrics evaluation phase is to quantitatively assess the accuracy, precision, recall, and other performance metrics of our models. These metrics offer valuable insights into the models’ ability to accurately classify instances of heart disease and aid healthcare professionals in making informed decisions [59]. Different metrics offer unique insights and considerations, and comprehending their strengths and limitations is crucial for accurately interpreting and comparing the performance of our models. In the realm of machine learning, the primary objective is to develop models that exhibit robust generalization to unseen data and provide accurate predictions. Metrics evaluation serves as a means to quantify the degree to which a model accomplishes this objective. Through the quantification of a model’s performance, we gain valuable insights that enable informed judgments regarding its dependability and suitability for real-world applications [60]. Through rigorous evaluation of various metrics, we strive to provide reliable insights and recommendations for healthcare professionals, assisting them in making accurate diagnoses and informed decisions in the realm of heart disease detection (Figs. 20, 21). From the results obtained, it can be observed that the F1-Score, Precision, and Recall provide values that are almost identical to the Accuracy after testing on the testing data.

The Use of Feature Engineering and Hyperparameter Tuning …

Fig. 13 Correlation matrix

Fig. 14 Python code of selected correlation matrix

209

210

C. Herdian et al.

Fig. 15 Selected correlation matrix

Fig. 16 Top 7 features python code

Therefore, it can be concluded that although it cannot be guaranteed with absolute certainty, it is highly likely that the data is not experiencing overfitting or even underfitting. In other words, the dataset used is considered consistent and of good quality.

The Use of Feature Engineering and Hyperparameter Tuning …

211

Fig. 17 Top 7 selected features

Fig. 18 Python code modeling before hyperparameter tuning

4

Results, Discussion, and Evaluation

Based on the results obtained from this research, by using the KNN algorithm combined with the application of Hyperparameter GridSearchCV, the achieved accuracy in the testing phase was 86.23%. This study has the potential to develop a predictive model for

212

C. Herdian et al.

Fig. 19 Python code modeling after hyperparameter tuning

Fig. 20 Evaluation metrics for machine learning model python code

Fig. 21 Machine learning evaluation metrics

detecting heart disease using the provided dataset. The researchers summarized different significant accuracy improvementa for the treatments conducted. – Before Hyperparameter Tuning: 68.12% – After Hyperparameter Tuning: 86.23%.

The Use of Feature Engineering and Hyperparameter Tuning …

213

Based on the obtained accuracy figures, the researchers concluded that this study showed a highly improvement in accuracy with the implementation of Hyperparameter Tuning. These findings align with previous research which also demonstrated an increase in accuracy through the use of parameter tuning techniques. In the research, specifically in the publication authored by E. Dritsas under the title “Supervised Machine Learning Models for Liver Disease Risk Prediction”, it is mentioned that the use of cross-validation can increase the accuracy. It means, in this study, the use of Hyperparameter is highly suitable for improving the accuracy on the health-related personal data [60]. Advantages and Disadvantages of the Model in Real-world Implementation in the Healthcare Field are as follows: Advantages . Early identification of patients at risk of heart disease can lead to early intervention and prevention of heart disease. . Automated detection of heart disease can result in more efficient utilization of healthcare resources. . Machine learning models can provide valuable insights to healthcare professionals about patient risk factors. Suggestions . This model relies on historical data that may not accurately represent the present population or demographic shifts. . Model accuracy can be influenced by variations in data collection across different hospitals and healthcare systems. . Model performance may deteriorate over time due to changes in patient population and risk factors. . Ethical and legal considerations may arise regarding the use of machine learning models in healthcare decision-making. By analyzing the obtained outcomes, it is evident that the constructed KNN model has achieved commendable accuracy when tested. Nevertheless, it is crucial to acknowledge that the interpretation of accuracy and the determination of model parameters necessitate cautious deliberation and might require additional assessment and experimentation for a more comprehensive comprehension of the model’s performance. In conclusion, our predictive model has shown potential in identifying patients at risk of heart disease. However, it is important to consider the limitations and potential weaknesses before implementing it in the healthcare field. Ongoing validation and continuous monitoring will be necessary to ensure sustained accuracy and usefulness.

214

5

C. Herdian et al.

Conclusion and Recommendation

In this research, we created a heart disease predictive model by leveraging machine learning methods. The model exhibited encouraging outcomes in effectively determining the presence of heart disease by considering diverse risk factors. The attained accuracy and performance metrics highlight the potential value of the model in supporting healthcare practitioners in early detection and intervention. By conducting an extensive analysis of a comprehensive dataset and employing advanced machine learning algorithms, we successfully identified crucial features and patterns that contribute to the prediction of heart disease. The model showcased commendable accuracy and delivered valuable insights into the associated risk factors of the disease. Suggestions 1. Data Cleaning . Evaluation: The data cleaning process was performed well, including the removal of irrelevant columns and handling missing values. This is important to ensure the quality of the data being used. . Suggestions: You can consider additional methods, such as outlier removal or further handling of missing values, to improve the data quality. 2. Correlation Analysis: . Evaluation: Correlation analysis and visualization using Pearson correlation matrix and heatmap provided insights into the relationships among features in the dataset. . Suggestions: You can consider adding annotations to the heatmap to provide specific correlation values, making interpretation easier. 3. Feature Selection: . Suggestions: You can consider adding annotations to the heatmap to provide specific correlation values, making interpretation easier. . Suggestions: You can consider adding annotations to the heatmap to provide specific correlation values, making interpretation easier. 4. Experimentation with Data Splitting: . Suggestions: You can consider adding annotations to the heatmap to provide specific correlation values, making interpretation easier. 5. Evaluation with GridSearchCV: . Evaluation: The use of GridSearchCV helped KNN model and provided the best scores and parameters. 6. Testing on Test Data: . Evaluation: The use of GridSearchCV helped KNN model and provided the best scores and parameters. 7. Evaluation with Different Random States:

The Use of Feature Engineering and Hyperparameter Tuning …

215

. Evaluation: The use of GridSearchCV helped KNN model and provided the best scores and parameters. Overall, the process and development of the KNN model for heart disease classification have been executed well. However, there are some suggestions that can be implemented to enhance the analysis and evaluation of the model. Firstly, considering alternative feature selection methods such as cross-validation can provide more stable and representative performance estimations. Additionally, exploring a broader range of parameter combinations throught the use of the GridSearchCV algorithm can offer a more comprehensive understanding of the best parameter combinations. Furthermore, comparing the model with other algorithms can provide additional insights into its performance. Nevertheless, the two most crucial aspects in predicting heart disease are the utilization of the KNN algorithm and the implementation of Hyperparameter Tuning.

References 1. Andry, J. F., Tannady, H., Rembulan, G. D., & Rianto, A. (2022). The importance of big data for healthcare and its usage in clinical statistics of cardiovascular disease. Journal of Population Therapeutics and Clinical Pharmacology, 29(04), 107–115. 2. Liu, Y., & Miao, J. (2022). An emerging role of defective copper metabolism in heart disease. Nutrients, 14(3), 700. 3. Nouman, A., & Muneer, S. (2022). A systematic literature review on heart disease prediction using blockchain and machine learning techniques. International Journal of Computational and Innovative Sciences, 1(4), 1–6. 4. Sun, Z., Dong, W., Shi, H., Ma, H., Cheng, L., & Huang, Z. (2022). Comparing machine learning models and statistical models for predicting heart failure events: A systematic review and meta-analysis. Frontiers in Cardiovascular Medicine, 9, 812276. 5. Sarra, R. R., Dinar, A. M., Mohammed, M. A., & Abdulkareem, K. H. (2022). Enhanced heart disease prediction based on machine learning and χ2 statistical optimal feature selection model. Designs, 6(5), 87. 6. El-Hasnony, I. M., Elzeki, O. M., Alshehri, A., & Salem, H. (2022). Multi-label active learningbased machine learning model for heart disease prediction. Sensors, 22(3), 1184. 7. Ahsan, M. M., & Siddique, Z. (2022). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128, 102289. 8. Li, J., et al. (2022). Predicting mortality in intensive care unit patients with heart failure using an interpretable machine learning model: Retrospective cohort study. Journal of Medical Internet Research, 24(8), e38082. 9. Elton, D. C., Chen, A., Pickhardt, P. J., & Summers, R. M. (2022). Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning. In Medical imaging 2022: Computer-aided diagnosis (vol. 12033, pp. 694–701). SPIE. 10. Toh, J. Z. K., et al. (2022). A meta-analysis on the global prevalence, risk factors and screening of coronary heart disease in nonalcoholic fatty liver disease. Clinical Gastroenterology and Hepatology, 20(11), 2462–2473.

216

C. Herdian et al.

11. Feigin, V. L., Brainin, M., Norrving, B., Martins, S., Sacco, R. L., Hacke, W., et al. (2022). World stroke organization (WSO): Global stroke fact sheet 2022. International Journal of Stroke, 17(1), 18–29. 12. Katta, N., Loethen, T., Lavie, C. J., & Alpert, M. A. (2021). Obesity and coronary heart disease: Epidemiology, pathology, and coronary artery imaging. Current Problems in Cardiology, 46(3), 100655. 13. Cojocaru, K. A., Luchian, I., Goriuc, A., Antoci, L. M., Ciobanu, C. G., Popescu, R., & Foia, L. G. (2023). Mitochondrial dysfunction, oxidative stress, and therapeutic strategies in diabetes, obesity, and cardiovascular disease. Antioxidants, 12(3), 658. 14. Ghaemian, A., Nabati, M., Saeedi, M., Kheradmand, M., & Moosazadeh, M. (2020). Prevalence of self-reported coronary heart disease and its associated risk factors in Tabari cohort population. BMC Cardiovascular Disorders, 20(1), 1–10. 15. Arnaert, S., et al. (2021). Heart failure related to adult congenital heart disease: Prevalence, outcome and risk factors. ESC Heart Failure, 8(4), 2940–2950. 16. Wang, F. et al. (2021). Global burden of ischemic heart disease and attributable risk factors, 1990–2017: A secondary analysis based on the global burden of disease study 2017. Clinical Epidemiology, 859–870. 17. Mondesir, F. L. et al. (2019). Patient perspectives on factors influencing medication adherence among people with coronary heart disease (CHD) and CHD risk factors. Patient Preference and Adherence, 2017–2027. 18. Osman, H., Ghafari, M., & Nierstrasz, O. (2017). Hyperparameter optimization to improve bug prediction accuracy. In 2017 IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE) (pp. 33–38). IEEE. 19. Koyawala, N., Mathews, L. M., Marvel, F. A., Martin, S. S., Blumenthal, R. S., & Sharma, G. (2023). A clinician’s guide to addressing cardiovascular health based on a revised AHA framework. American Journal of Cardiovascular Disease, 13(2), 52–52. 20. Herdian, C. (2023). Prediksi Harian Harga Penutupan Dogecoin: Analisis Faktor Pengaruh dan Algoritmanya. Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi, 8(1), 17–27. 21. Joshi, A., & Tiwari, H. (2023). An overview of python libraries for data science. Journal of Engineering Technology and Applied Physics, 5(2), 85–90. 22. Harapanahalli, A., Jafarpour, S., & Coogan, S. (2023). A toolbox for fast interval arithmetic in numpy with an application to formal verification of neural network controlled systems. arXiv preprint arXiv:2306.15340. 23. Bisong, E., & Bisong, E. (2019). NumPy. In Building machine learning and deep learning models on google cloud platform: A comprehensive guide for beginners (pp. 91–113). 24. Bisong, E., & Bisong, E. (2019). Matplotlib and seaborn. In Building machine learning and deep learning models on google cloud platform: A comprehensive guide for beginners (pp. 151–165). 25. Pajankar, A., & Pajankar, A. (2022). Introduction to data visualization with seaborn. In Hands-on matplotlib: Learn plotting and visualizations with Python 3 (pp. 243–267). 26. Li, F., & Wang, L. (2022). Research on data visualization technology based on Python. International Journal of Multidisciplinary Research and Analysis, 5(5), 907–910. 27. Hunt, J. (2023). Introduction to matplotlib. In Advanced guide to Python 3 programming (pp. 121–128). Springer International Publishing. 28. Hao, J., & Ho, T. K. (2019). Machine learning made easy: A review of scikit-learn package in Python programming language. Journal of Educational and Behavioral Statistics, 44(3), 348– 361. 29. Géron, A. (2023). Praxiseinstieg machine learning MIT Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. O’Reilly.

The Use of Feature Engineering and Hyperparameter Tuning …

217

30. Raschka, S., Liu, Y. H., Mirjalili, V., & Dzhulgakov, D. (2022). Machine learning with PyTorch and scikit-learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd. (2022). 31. Douglass, M. J. (2020). Book review: Hands-on machine learning with Scikit-Learn, Keras, and Tensorflow, by Aurélien Géron: O’Reilly Media, 2019, 600 pp., ISBN: 978-1-492-03264-9. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 1001–1002 (2020). 32. Nelli, F. (2023). Machine learning with scikit-learn. In Python data analytics: With Pandas, NumPy, and Matplotlib (pp. 259–287). Apress. 33. Breck, E., Polyzotis, N., Roy, S., Whang, S., & Zinkevich, M. (2019). Data validation for machine learning. In Proceedings of the conference on machine learning and systems (MLSys). 34. Fernandes Andry, J., Gunadi, J., Dwinoor Rembulan, G., & Tannady, H. (2021). Big data implementation in tesla using classification with rapid miner. International Journal of Nonlinear Analysis and Applications, 12, 2057–2066. 35. Salim, N. O., & Abdulazeez, A. M. (2021). Human diseases detection based on machine learning algorithms: A review. International Journal of Science and Business, 5(2), 102–113. 36. Ahmad, G. N., Fatima, H., Ullah, S., & Saidi, A. S. (2022). Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV. IEEE Access, 10, 80151–80173. 37. Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468. 38. Sha’Abani, M. N. A. H., Fuad, N., Jamal, N., & Ismail, M. F. (2020). kNN and SVM classification for EEG: a review. In Proceedings of the 5th international conference on electrical, control & computer engineering (InECCE2019), Kuantan, Pahang, Malaysia (pp. 555–565). Springer Singapore. 39. Zhao, D., Hu, X., Xiong, S., Tian, J., Xiang, J., Zhou, J., & Li, H. (2021). K-means clustering and kNN classification based on negative databases. Applied Soft Computing, 110, 107732. 40. Gao, X., & Li, G. (2020). A KNN model based on Manhattan distance to identify the SNARE proteins. IEEE Access, 8, 112922–112931. 41. Huang, A., Xu, R., Chen, Y., & Guo, M. (2023). Research on multi-label user classification of social media based on ML-KNN algorithm. Technological Forecasting and Social Change, 188, 122271. 42. Liu, W., Wei, J., & Meng, Q. (2020). Comparisons on KNN, SVM, BP and the CNN for handwritten digit recognition. In 2020 IEEE international conference on advances in electrical engineering and computer applications (AEECA) (pp. 587–590). IEEE. 43. Tampinongkol, F. F., Herdiyeni, Y., & Herliyana, E. N. (2020). Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wavelet transform. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 740–751. 44. Alhenawi, E. A., Al-Sayyed, R., Hudaib, A., & Mirjalili, S. (2022). Feature selection methods on gene expression microarray data for cancer classification: A systematic review. Computers in Biology and Medicine, 140, 105051. 45. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022). Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58–73. 46. Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019). Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems, 32. 47. Andry, J. F., Hartono, H., & Honni, A. C. (2022). Data set analysis using rapid miner to predict cost insurance forecast with data mining methods. Journal of Hunan University Natural Sciences, 49(6).

218

C. Herdian et al.

48. Belyadi, H., & Haghighat, A. (2021). Machine learning guide for oil and gas using Python: A step-by-step breakdown with data, algorithms, codes, and applications. Gulf Professional Publishing. 49. Zhang, H., Zhang, L., & Jiang, Y. (2019). Overfitting and underfitting analysis for deep learning based end-to-end communication systems. In 2019 11th international conference on wireless communications and signal processing (WCSP) (pp. 1–6). IEEE. 50. Arora, A. S., Rajput, H., & Changotra, R. (2021). Current perspective of COVID-19 spread across South Korea: Exploratory data analysis and containment of the pandemic. Environment, Development and Sustainability, 23, 6553–6563. 51. Dahouda, M. K., & Joe, I. (2021). A deep-learned embedding technique for categorical features encoding. IEEE Access, 9, 114381–114391. 52. Ernawan, F., Fakhreldin, M., & Saryoko, A. (2023). Deep learning method based for breast cancer classification. In 2023 international conference on information technology research and innovation (ICITRI) (pp. 13–16). 53. Jebli, I., Belouadha, F. Z., Kabbaj, M. I., & Tilioua, A. (2021). Prediction of solar energy guided by Pearson correlation using machine learning. Energy, 224, 120109. 54. Passos, D., & Mishra, P. (2022). A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks. Chemometrics and Intelligent Laboratory Systems, 223, 104520. 55. Huang, L., Song, T., & Jiang, T. (2023). Linear regression combined KNN algorithm to identify latent defects for imbalance data of ICs. Microelectronics Journal, 131, 105641. 56. Sulistya, Y. I., & Danuputri, C. (2022). Analisis perbandingan reduction technique dengan metode dimentional reduction dan cross validation pada dataset breast cancer. Indonesian Journal of Data and Science, 3(2), 82–88. 57. Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behavior with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1–13. 58. Shao, H., Chen, X., Ma, Q., Shao, Z., Du, H., & Chan, L. W. C. (2022). The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements. Frontiers in Neurology, 13, 934929. 59. Sahrmann, P. G., Loose, T. D., Durumeric, A. E., & Voth, G. A. (2023). Utilizing machine learning to greatly expand the range and accuracy of bottom-up coarse-grained models through virtual particles. Journal of Chemical Theory and Computation. 60. Dritsas, E., & Trigka, M. (2023). Supervised machine learning models for liver disease risk prediction. Computers, 12(1), 19–19.

Plant Health—Detecting Leaf Diseases: A Systematic Review of the Literature Fandi Fatima Zahra, Ghazouani Mohamed, and Azouazi Mohamed

Abstract

Every year, both the demand for plant products and the population of the planet are growing. Moroccan agriculture is one of the main sectors of activity, which loses every year a percentage of the productivity of its crops due to plant diseases; which requires the protection of crops against plant diseases to meet the growing needs about the quality and caliber of food. So the only solution to decrease the percentage and to increase the productivity is the detection of diseases. From this, we can mention as problematic: depositing a laboratory facility to detect infected leaves, bearing in some countries farmers do not deposit a facility adequate to the recommendation of experts besides consulting an expert is costly and takes more time, hence it is advised to develop a new technology, particularly automated detection of plant leaf diseases, to properly monitor huge crop fields. Keywords

IOT • Agriculture • CNN • CAE • Mobile Net • C-GAN • Deep CNN • Dense Net Res Net • Model & Bayesian learning • SVM • ResTS • Semantic segmentation

•

F. F. Zahra (B) · G. Mohamed · A. Mohamed LTIM, Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_12

219

220

1

F. F. Zahra et al.

Introduction

The Moroccan institutional environment has been in full swing since 2008. The major restructuring of the agricultural sector has ushered in a new era of vigorous development of our country’s agricultural and rural areas. The Green Morocco Plan (Agricultural Development Strategy), and the Development Approach for Rural and Mountain Areas are at work, In addition, the “Green Generation 2020–2030” agricultural sector development strategy aims to capitalize on the successes of the previous decade, while prioritizing the human element and promoting an agrarian middle class capable of playing an important role in agriculture. And despite the efforts made by the Moroccan state and following the global agricultural development of tools, concepts and methodologies the vision of Morocco is oriented towards intelligent agriculture through the support of scientific research in this field. Pests like viruses, bacteria, fungi, insects, and weeds are always attacking plants and plant products. Pests are a major factor in the decreased production of agricultural products for human consumption, notwithstanding the controls implemented by stakeholders such as farmers and ONSSA. In order to do this, plant protection entails using all methods, techniques, and logistical measures possible to safeguard the wellbeing of plants and plant-based products. Including crops. Today, the emergence of drones and cameras and their scientific exploitation “image processing” we refer on new technologies as an example IOT “Internet of Things”; which makes it possible to retrieve, store, transfer, and process digital items directly and unambiguously, data via electronic identification systems standardized and unified, in the field of agriculture as a tool for visualization and data collection as well as decision making, with a “collection of theories and procedures used to build robots capable of imitating human intelligence” is referred to as artificial intelligence (Fig. 1). In this review, we will systematically explore the different approaches and visions of researchers on plant health, including plant leaf disease detection, in order to thorough comprehension on the definition of AI artificial intelligence and internet of things in order to detect plant leaf disease, Observe different algorithms of machine learning (ML) and deep learning (DL) that allows the remedy for this problem and give the vision of

Fig. 1 Data for agricultural detection and forecasting

Plant Health—Detecting Leaf Diseases: A Systematic Review …

221

Fig. 2 The essential factors for creating an ideal plant disease environment

researchers with its different scientific directions, discover the main trends in the evaluation of this problem, summarize the progress of AI algorithms on this topic in the last three years, and highlight the limitations of research on the detection of plant leaf illnesses. The study’s objective is to obtain an overview of the status and development of AI algorithms for systematically to summarize the progress of research on the detection of plant leaf ailments in plant health in the last three years and to highlight the limitations of research on this branch (Fig. 2). The study will address the following research questions: 1. How is the detection of disease of plant leaves defined as far as of plant health in the AI context? 2. What have been the main objectives, methodologies, and results of studies on identification of diseased plant leaves in context with image processing over the past three years? 3. What types of limitations exist in the research presented on typical detection disease of plant leaves and prediction in connection with early intervention? The remainder of this essay is organized in the manner shown below keeping these research questions in mind. We will go over the process used for this review and how the studies were chosen in the next part. Then, using the papers chosen for this literature review, we provide the findings and address the research issues. We give findings and suggestions for further work on plant leaf disease identification after outlining the limitations of our study.

222

2

F. F. Zahra et al.

Ease of Use

To describe the notion of agriculture, including the identification of plant leaf illnesses, a systematic review was produced. To provide research on identifying plant leaf disease in smart agriculture more effectively and to provide a fair synthesis and interpretation of the findings, three research issues were examined. The research’s questions ought to be clearly established as an objective to be addressed at the commencement of the evaluation. The databases used for the search are then listed together with the search terms and standards for evaluating and choosing studies. Finally, the publications that were added toward the end of the procedure are presented. The procedure is specifically divided into the three steps of selection, identification, and synthesis.

2.1

Search Strategy

SCOPUS and Web of Science (WOS) were selected as the electronic databases used in this methodical literature assessment. As the two top databases for international, multidisciplinary academic literature, Web of Science and Scopus were chosen (Aghaei Chadegani et al. 2013). The article’s title, keywords, and abstract were searched for the chosen terms. The following search terms were used in this systematic review for each electronic database that was chosen: WOS: ((“Smart agriculture” OR “Plant leaf disease detection” OR “Plant health” AND (Image processing” OR “Deep learning” OR “Artificial intelligent”). According to Scopus, TITLE-ABS-KEY ((“Smart agriculture*” OR “The detection of plant leaf diseases*” OR “Plant health*”) AND (“Image processing” OR “Artificial intelligent*” OR “Deep learning*”).

2.2

Study Choice

The selection process for the studies was progressive and iterative, divided into several stages, and involved a variety of tasks. With the rapid advancement of digital technology, a search was made to locate the most recent developments in digital competency studies and trends. 1100 articles were found in the initial search.

2.2.1

Criteria for Inclusion and Deletion

To choose appropriate studies to find solutions regarding the stated study issues, the subsequent inclusion and exclusion criteria (Table 1) were created. The results of applying the search terms were identified.

Plant Health—Detecting Leaf Diseases: A Systematic Review …

223

Table 1 Inclusion and exclusion standards Inclusion standards

Exclusion standards

The research focuses on identifying plant leaf diseases. The most recent developments in plant health are included in the article

Plant health research, particularly the detection of plant leaf diseases, is unrelated

Between 2018 and 2022, research papers are published

Between 2018 and 2021, no research papers are released

The language of research articles is English

The language of research articles is not English

Peer review was completed before research papers were published

Without a peer review procedure, research papers were published

The study adheres to the proper format for a research via research method

The papers do not adhere to a research method’s correct framework

To find duplicate articles to be removed, the 1100 articles were first checked against the inclusion and exclusion criteria.

3

Results and Analysis

Through the examination of the chosen publications, we offer solutions to the research questions in this part. It is set up in accordance with presenting the findings of the systematic literature review in response to the questions posed (SLR). Through various methods and systems, many architectures and initiatives are devoted to smart farming; their implementation helps farmers to make informed decisions and enhances almost every area of their work. I will start with “Amreen Abbas and al” who present the model (C-GAN) which takes into consideration that CNN’s model performance is heavily reliant on the dataset at availability for training; with this in mind, synthetic images were generated exploiting generative adversarial conditional networks for data augmentation; then, DenseNet121 was previously trained, this first consists in generating synthetic images of tomato leaves of different diseases; then, the calculation of the initial score confirms its reliability; for evaluating the output images quality and compare them to the original images, the Dense Net pre-drive architecture was adjusted to the original drive assembly combined with the addition o of the assembly[1]. “S. Ashwin Kumar and al” who dealt with the aspect of detection rate improvement via (OMNCNN) CNN and Mobile Net as a feature extraction method where the EPO algorithm is used to improve the hyperparameters, It works on the different steps namely; preprocessing (Bilateral filtering), segmentation, parameter optimization (EPO ≥ Emperor penguin optimizer) and classification (ELM), this OMNCNN methodology has proposed maximum performance with higher accuracy of 0.98 [2]. In the case of the “Punam Bedi and al”, the issue of having too many training parameters

224

F. F. Zahra et al.

is addressed through the hybrid model CAE and CNN, it will require either a machine with a lot of power for computation or a lot of chaining time, the auto encoder is for the reduction of the features in a significant way. Reconstruction loss, decoder network, bottleneck layer, and encoder network are the four components of the algorithm. An NRMSE function serves as calculate the construction lack compared to the original and image of an artificial plant and to attempt in order to recreate the original data. The accuracy of the suggested hybrid model was 98.38% [3]. “Vemishetti Sravan” which presents a pretrained architecture of CNN is Res Net50, the search of this classification is done in three steps: This approach is based on the aspects of CNN with a residual Blok for parameter mapping (Although It’s possible that f(x) and x’s dimensions are not equal, the output produced by the previous layer is added to the next, and even if the convolution operation causes the input image’s spatial resolution to decrease, it discovers a precession of 99.26% [4]. A more advanced method of “Sanga et al. (2020)” who created a plant illness diagnosis device using a CNN architecture that included VGG16-ResNet-152-ResNet-50ResNet-18-inceptionV 3 and they discovered that Res Net-152 is the best performing model with accuracy beyond the last. In particular, when the explicative factor is excessively large, the retrieved primary element is typically challenging to understand, and the optimization algorithms are used, “Chen Jun-De et al.” propose the GMDH-logistic model as a solution to this problem. In this study, the benefits of the logistic and GMDH algorithms are combined; the GMDH approach offers a solution to this problem that can identify the variables, structure, and parameters automatically. The characteristics chosen by the GMDH technique are typically comprehensible and able to compensate for PCA’s drawbacks (principal component analysis). The Group Method of Data Handling (GMDH) is improved by the GMDH-Logistic technique, which simultaneously applies artificial neural networks, Victor machine support, and other optimization algorithms for comparative analysis and classification. This approach achieves a precession of 86.67% [5]. Analysis by “Guneet Sachdeva et al.” offers a desirable method for spotting illness in various leaves. During this study, Using Bayesian learning, a deep CNN model is created for the identification and categorization of leaf problems. Bayesian analysis is used to enhance the dependency between pixels for efficient feature learning, leading the residual network. The plant Village database, which has 20,639 photos and 15 various classifications of good and unhealthy a leaf images peppers, potatoes, and tomatoes, served as the source of information for this project. In order to evaluate the suggested DCNN and the conventional classifier with regard to recall, accuracy, precision, and Fscore, comparative analysis was carried out. The outcome demonstrated that the proposed model is a useful instrument for diagnosing and categorizing diseases. The remarkable biospecificity of nanoscale molecular recognition that has seen unprecedented development in the last ten years using cutting-edge analytical methods, involving microscopy and spectroscopic analysis, sensor wearable’s, and microscopy using a smartphone, makes nanoscale materials further potential disease detection candidates. Without exhibiting any overfitting symptoms, the proposed network is 98.9% accurate in identifying the disease

Plant Health—Detecting Leaf Diseases: A Systematic Review …

225

type. It has a low parameter count and uses little processing power [6]. ResTS (Residual Teacher/Student) is the name of an architecture that “Dhruvil Shah et al.” suggest be utilized as a visualization and classification method for plant disease diagnosis. The previously suggested Teacher/Student architecture has been modified in a secondary way by ResTS. ResTS is based on a structure of a convolutional neural network (CNN) it includes a decoder, two classifiers (ResTeacher and ResStudent). The representation sent among ResTeacher and ResStudent is utilized as a stand-in for the dominating parts of the image when categorizing an image using this structure, which exercises in reciprocal mode, the two classifiers. Studies revealed that the proposed ResTS structure can provide finer representations of disease symptoms and outperforms the Teacher/Student design (F1 score: 0.991). As opposed to the Teacher/Student model for plant disease diagnostics that was previously suggested, the new ResTS architecture contains all components’ residual links, and conducts grouping adjustment following every convolution process. In ResTS, gradients are preserved and the issue of disappearing or expanding gradients is avoided because to residual connections. Additionally, following every convolution operation, batch normalization promotes rapid convergence and improved dependability. Precession on this method is 90% [7]. The utilization of law mask characteristics is enhanced by “Navneet Kau et al.” using improved categorization. The proposed task intends to address the categorization of leaves, which is crucial for addressing issues with small sample sizes or other facets of classification algorithms. The secret to this technique is the use of texture in the extraction of image features characteristics and a convolutional basis law mask employing the idea of resemblance between different metric space constructions using picture attributes. The accuracy of the SVM classification depends on the quantity of supervised samples and the coherence of the metric space. As an alternative, the gray wolf strategy is used to optimize the segmentation and lessen object mixing. The experiment includes diseases of the potato, tomato, and bell pepper in addition to other sorts of diseases, and it is divided into 2, 3, and 5 classes for each element. This method is more accurate than 83% for 3 classes [8]. The development of a deployed system for hardware to recognize PM in field of strawberries and offer guidance on when to use fungicides is recommended by “Jaemyung Shin et al.”. According to the experimental findings, CNN approaches are a useful device for developing a deployable field method to detect PM in strawberry leaves. However, if imaging is acquired in the later phases of the disease, preventive actions could be irrational and chemical therapies may be required to treat the illness. The best case scenario for management would be to stop the disease in its early stages. Future research should focus on installing these algorithms on an agricultural spray hardware platform using the suggested DL method. We fully acknowledge that the suggested techniques merit further study regarding their applicability as a field circumstances, exactly where are added difficulties connected to irregular lighting dictions, orientation of leaf, and leaf overlap. Despite the fact that this study was conducted in a laboratory environment where the illumination and the orientation of the leaves were stable. Every CNN algorithm achieved >92% [9]. “Jaemyung Shin and al” started using

226

F. F. Zahra et al.

comparisons of CNNs and non-DLs using a dataset of 1450 photos strawberry leaves with and without PM infection. As predicted, CNNs outperformed nonDLs when it came to differentiating between diseased and healthy leaves as well as when the dataset was expanded and additional function parameters were introduced. Our study’s main objective aimed to create a hardware system that could be deployed to identify PM in field of strawberries and offer suggestions on when to apply fungicides. 11,600 data points total were collected as a result of expanding our study to include a data augmentation technique that rotates images. ResNet-50 possessed a significantly higher CA (98.11%) than the other algorithms, although there was no discernible difference in the other methods’ CA. SqueezeNet-MOD2 has the least demanding criteria for possible hardware memory. ResNet-50 took the longest to process 2320 photos, taking 178.20 s, while AlexNet took the shortest, taking 40.73 s. The CNN algorithms tested took considerably varied amounts of time, with AlexNet and SqueezeNet-MOD2 taking the least amount of time. The experimental findings demonstrated the potential of CNN approaches for the creation of a field- deployed system to identify PM within strawberry leaf. Preventing the disease at its earliest stage would be the optimum management strategy; nonetheless, if the imagery is obtained after the sickness is more advanced, preventative measures may not be realistic, and pharmacological treatments may be required to treat the illness. Future study will focus on installing these algorithms on hardware platforms for pesticide spraying using the suggested DL technique. Despite the fact that this study was conducted where the illumination and leaf orientation were standardized in a laboratory maintained always, we completely acknowledge that the suggested methods merit further investigation into their applicability in circumstances, where there are added difficulties relating to inconsistent illumination conditions, leaf orientation, and other factors, and leaf overlapping. In particular, research could be expanded to create fully automated hardware (such as an FPGA or mobile application) for assisting those producers who suffer with PM sickness. Subsequent research will look into how the CNN algorithms may be integrated with hardware and create a disease management system that producers can simply utilize by delivering precise and quick findings [10]. “Serosh Karim Noon” who confirm that despite numerous most recent deep learning-based attempts to determine plant leaf stresses, several issues remain unsettled, such as the need for real-world data, background removal that is automatic, also utilizing mobile devices to practice and test massively intricate convolutional neural networks. On the basis of their training data, a substantial published research body improved trained networks such as AlexNet, GoogleNet, or VGGNet, etc. This survey has shown that, n comparison to the scenario when the network is initially trained, these transfer learning-based approaches have increased detection accuracy. The majority of the research has been based on the publicly accessible PlantVillage dataset. More than 50,000 images are present, which is more than enough for training any convolutional neural network. Some writers employed fewer than 1,000 original photos, however they mostly relied on data augmentation to achieve an enough

Plant Health—Detecting Leaf Diseases: A Systematic Review …

227

number of images for deep model training. To increase the dataset’s performance, to artificially extend it, practically all of the authors used augmentation techniques. Rotation, cropping, noise addition, and grayscale conversion are some fundamental enhancement techniques. Here are a few instances from works that have been published. Recent deep network proposals in this field have yielded excellent recognition accuracy for nearly all plant kinds (often above 90%). All deep networks analyzed for this paper outperformed conventional feature extraction and categorization methods based on machine learning as a main type of approaches. Their proposed deep network’s efficiency is also compared in this study to that of other widely utilized networks, including Inception-X, AlexNet, GoogleNet, etc. [11]. “Kamlesh Golhani and al” who indicated that NNs were previously solely utilized for data mining, although their numerous uses with hyperspectral data are now demonstrating tremendous potential for illness identification. Similar to many other technologies, researchers have frequently had to deal with new problems in NN applications. For instance, the best trainer sets are needed to accurately classify the three types of illness manifestation—pre-symptomatic, symptomatic, and asymptomatic—from a single plant. Using hyperspectral data, NNs have proven to be incredibly adaptable to new difficulties in illness diagnosis. NNs have been employed for a range of tasks, including reducing the dimensionality of data, training with input sets of picture pixels or spectra, generalizing inputting sets, and classifying SDIs or wavebands. The literature on SDIs has been explored in-depth in this work. The best that we can tell, there isn’t any research on using NNs to study SDIs. Within the next several days, NNs will be employed to process SDIs in order to produce more trustworthy findings. Since NNs have not yet been tested for SDIs in other contexts, it is possible to illustrate some potential future directions for development, such as data pretreatment, dimensionality reduction, and effective data analysis. Before an SDI is created, these operations can be completed using NNs. After the creation of an SDI, NNs can also be very helpful in accelerating the performance of SDIs to gather crucial data for disease diagnosis. Testing of SDIs on different hyperspectral sensors at the canopy and leaf size should continue so long as SDIs are making significant progress in the preservation of precise plants [12]. “CHEN Jun-De and al” who concerned the detection of plant illnesses that use images of leaves has to be extracted pertinent features, in the domains of computer vision and image processing, that is a crucial step. As a result, this work used GIWA filtration, picture slicing, and a matrix of gray-level cooccurrence, and other techniques in order to extract the important aspects of leaf pictures. In the study, 15 traits—including Pct, Num, Contrast1, etc.—were retrieved, and using these as a foundation, the model prediction index system was created. Additionally, we applied self-organized data mining technology in the field of picture identification and created a new GMDH-Logistic technique for automatically identifying and classifying plant leaf diseases on the basis of findings of analysis of features. In order to address the drawbacks of previous algorithms, the crucial properties are automatically chosen to be included in the algorithm, as well as the chosen variables are often comprehensible. While state-of-the-art techniques like CNN have the capacity to automatically extract features,

228

F. F. Zahra et al.

relevant comparative experiments have shown that under complex background conditions, the suggested feature engineering-based GMDH-Logistic approach exhibits a substantial classification effect and is appropriate for the classification of plant disease pictures[13]. In this study “M. Yogeshwari and al” who used over a deep convolutional neural network (DCNN) to propose a unique technique for identifying plant leaf diseases. Here, 2D Adaptive Anisotropic Diffusion Filter (2D AADF) and Adaptive Mean Adjustment (AMA) were used for picture preprocessing. Improved Fast Fuzzy C Means Clustering (IFFCMC) and Adaptive Otsu (AO) thresholding were used to segment the pre-processed pictures. GLCM traits were extracted from the segmented images. Using the PCA technique, the retrieved features’ dimension was decreased. The DCNN architecture was then used for categorization. A high accuracy of roughly 97.43% was attained using the suggested system. Additionally, there are k-NN, Naive Bayes, SVM, and BPNN machine learning algorithms were also evaluated in comparison to this system. The quantitative study unmistakably demonstrated how much better performing the suggested system was than all the other classifiers [14]. “Vaibhav Tiwari and al” who present the task of identifying and categorizing plant diseases from digital photographs is fairly difficult. Because of this, early detection of the plant disease is essential for farmers and plant pathologists to act in the right way. For this, a total of 27 different types of plant leaf photographs are used in the suggested task. The images of the plants that were taken into account were diverse and included both lab-view and real-time outdoor shots of the plants from a number of naturally diverse categories. In order to train the deep learning dense model, multiple images from various categories were employed. Five-fold cross-validation is used to thoroughly assess the model, which was afterwards tested on the unseen photographs from the testing set. By reaching an average cross-validation accuracy of 99.58% and an average testing accuracy of 99.199%, the suggested model proved that it can be used to recognize and categorize plant diseases. In further study, more plant leaf images will be included in order to diversity the plant leaf dataset and support the trained model under difficult circumstances [15]. “Geetharamani G. and al” who explain the difficulties in identifying illnesses of plant leaves can be overcome with the use of Deep learning, a relatively recent research technique for pattern recognition and picture processing. The proposed Deep CNN model can correctly divide healthy and ill plants into 38 different classifications using images of leaves. The training data are likewise increased by the data augmentation, going from 49,598 to 55,636. The best Deep CNN model was trained and tested using a 61,486 picture enhanced dataset with 3000 training epochs. The proposed model successfully categorizes test set pictures of plant leaves with an average accuracy of 96.46% and a range of 92% to 100% for each class. The number of training epochs, batch size, and dropout had a greater influence on the corresponding results. Max pooling performs far better than conventional pooling. When compared to other machine learning models, the proposed Deep CNN model outperforms and makes superior predictions. The AUC-ROC curves, Precisions, Recalls, and F1 Scores are additional metrics used to assess the suggested model’s dependability and consistency.

Plant Health—Detecting Leaf Diseases: A Systematic Review …

229

An extension of this research will involve acquiring new images of diverse plant species, geographical regions, leaf growths, cultivation conditions, and image qualities and modes from a range of sources in order to expand the size and number of database classes. The larger dataset will improve the model’s effectiveness and accuracy using several fine-tuning techniques. Extending the scope of our mission to detect plant diseases from plant leaves to other plant parts including flowers, fruits, and stems, will be the main objective of the feature work. We can also use this model to diagnose plant leaf diseases. Additionally, we intend to carry out a more thorough analysis of the training procedure without using annotated images [16].

4

Comparative Study

The comparative analysis of the various architectures stated in the preceding section is presented in this section as a Table 2. We used the following criteria to compare these various architectures: architecture, accuracy (Fig. 3).

5

Critical Study

Despite the huge number of suggested smart agriculture architectures in the Table 2 above, each architecture is designed to address a specific problem without regard to other functionalities as an example the reduction of the number of training parameters (throttling layer) while keeping the accuracy with CAE [3], the strengthening of the database to have reliable results based on C-GAN [1]. As well as the reduction of hyper-parameters and dimensionalities with (OMNCNN) [2] and transfer learning [4] and other algorithms proposed by the researcher authors included in the Table 2 above. Today we are facing a deeper problem which is not only the detection of the diseases of the sick or not plant leaves, under the binary classification, but it is about the prediction and the detection of the classes of the diseases of the plant leaves, and for these effects we used the articles above and we extracted the learning and the wisdom from them. Then in our days, the major problem strongly related to the classification and detection of plant leaf diseases, as well as their prediction for the effectiveness and the intervention in time.

6

Proposed Approach

It is a great area of applicability for machine- assisted diagnostic methods to use cuttingedge analysis methods in these diseases because (1) disease diagnosis is a laborious manual process, (2) certain plant diseases have no visible signs, (3) It is a great computeraided diagnostic systems’ use cases to use cutting-edge analysis methods in these diseases

230

F. F. Zahra et al.

Table 2 Comparison of Algorithms References

Model

Benefits

Accuracy (%)

Automated plant leaf disease detection C and classification using optimal MobileNet based convolutional neural networks (2021)

OMNCNN Mobile Net CNN

EPO ≥ To improve the detection rate

98

A deep learning based crop disease classification using transfer learning (2020)

ResNet50

Les avantages de ces 99.26 réseaux sont leur capacité à réduire la fuite de gradient dans les couches inférieures du réseau, et la possibilité de leur mise à l’échelle en profondeur

Tomato plant disease detection using transfer learning with C-GAN synthetic images (2021)

C-GAN & DenseNet121

Image generation using conditional generative adversarial networks for data augmentation purposes. Subsequently a pre-trained DenseNet121

97.11

Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

CAE & CNN

The proposed hybrid mosdel of image representations using the CAE encoder network for feature reduction in a meaningful way

98.38 With a number of training parameters 9914

A self-adaptive GMDH-Logistique classification method for plant disease detection using GMDH-logistic model

The GMDH-Logistic method used such as artificial neural networks and other optimization algorithms were applied at the same time for comparative analysis and classification

86.67

Plant leaf disease Deep CNN model & classification using deep Bayesian learning Convolutional neural network with Bayesian learning

CNN model with Bayesian learning is created for the identification and categorization of leaf diseases

98.9

(continued)

Plant Health—Detecting Leaf Diseases: A Systematic Review …

231

Table 2 (continued) References

Model

Benefits

Accuracy (%)

ResTS-Residual Deep ResTS interpretable architecture for plant disease detection

ResTS utilized as a visualization and classification method for plant disease diagnosis

90

Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier

This research suggests 83 using an optimization-based segmentation and law mask framework to tackle the classification problem of leaf disease

SVM

A deep learning Six CNN algorithms approach for RGB image-based powdery mildew disease detection on strawberry leaves

A deep learning RGB image-based approach for RGB powdery mildew image-based powdery mildew disease detection on strawberry leaves

In order to limit the 92 amount of needless fungicide use and the requirement for field scouts, powdery mildew (PM), a chronic fungal disease in strawberries, was detected in this study using deep learning (DL) 98.11

Use of Deep Learning Techniques for Identification of Plant Leaf Stresses: A Review A review of neural networks in plant disease detection using hyperspectral data (continued)

232

F. F. Zahra et al.

Table 2 (continued) References

Model

Benefits

Accuracy (%)

A self-adaptive GMDH-Logistic classification method for model plant disease detection using GMDH-Logistic model

Shows a better ability to 86.67 distinguish whether the plant is the diseased plant or not, The most popular classifier for large-scale images because of a large number of parameters that need to be trained

Automatic feature GLCM features and extraction and detection convolutional neural of plant leaf disease networks using GLCM features and convolutional neural networks

Image preprocessing (filtering and enhancement ≥ Adaptive Anisotropic Diffusion Filter), segmentation, feature extraction and classification

97.43

Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images

Dense Convolutional Neural network

27 distinct classes of pictures of healthy and diseased plant leaves are available

99.19

Identification of plant leaf diseases using a nine-layer deep convolutional neural network

Nine-layer deep convolutional neural network

39 distinct classes of 98.15 pictures of healthy and diseased plant leaves are available The SVM, logistic regression, decision tree, and K-NN were contrasted with the suggested model. Finally, the findings demonstrate that the suggested model outperforms all of the models discussed previously

because (1) the illness’s diagnosis process sluggish to finish manually, (2) some plant issues have no visible signs, (3) and the success of testing is proportionally with the abilities of the pathologist. Artificial intelligence (AI) is a crucial participant in this instance due to it enables the creation of novel systems and increases intelligence in identifying plant diseases. To address the requirements linked to the detection of plant illnesses using their leaf images based on photographs, we will develop a system for deep neural

Plant Health—Detecting Leaf Diseases: A Systematic Review …

Accuracy 100% 98% 96% 94% 92% 90% 88% 86% 84% 82% 80%

Fig. 3 Accuracy comparison

233

234

F. F. Zahra et al.

network-based plant disease identification. that includes semantic segmentation focused color.

7

Conclusion and Future Work

Intelligent agriculture has generated a lot of interest in identifying plants diseases. In this regard, prompt identification of potentially diseased plants is essential help stop illnesses from developing in plants, which might have very expensive time and financial effects. The majority of plant diseases have obvious signs to automatically detect plant diseases, many researchers have employed a variety of machine learning and deep learning procedures. Nevertheless, several of these techniques have low classification accuracy or require millions of learning parameters. We conducted a comparative analysis of various smart agricultural architectures, including “Plant health—Detecting leaf diseases” based on IoT (Internet of Things) in this research. Then, employing a creative strategy for programmed identification of plant disease based on semantic segmentation, we redirected our research.

References 1. Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture, 187. 2. Ashwinkumar, S., Rajagopal, S., Manimaran, V., & Jegajothi, B. Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Materials Today: Proceedings, 51. 3. Bedi, P., & Gole, P. (2021). Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artificial Intelligence in Agriculture, 5. 4. Sravan, V., Swaraj, K., Meenakshi, K., & Kora, P. (2021). A deep learning based crop disease classification using transfer learning. Materials Today: Proceedings. 5. Chen, J., Yin, H., & Zhang, D. (2020). A self-adaptive classification method for plant disease detection using GMDH-logistic model. Sustainable Computing: Informatics and Systems, 28, 100415. https://doi.org/10.1016/j.suscom.2020.100415 6. Sachdeva, G., Singh, P., & Kaur, P. (2021). Plant leaf disease classification using deep convolutional neural network with Bayesian learning. Materials Today: Proceedings, 45, 5584–5590. https://doi.org/10.1016/j.matpr.2021.02.312 7. Shah, D., Trivedi, V., Sheth, V., Shah, A., & Chauhan, U. (2022). ResTS: Residual deep interpretable architecture for plant disease detection. Information Processing in Agriculture, 9(2), 212–223. https://doi.org/10.1016/j.inpa.2021.06.001 8. Kaur, N., & Devendran, V. (2020). Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.901 9. Shin, J., Chang, Y. K., Heung, B., Nguyen-Quang, T., Price, G. W., & Al-Mallahi, A. (2021). A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture, 183, 106042.

Plant Health—Detecting Leaf Diseases: A Systematic Review …

235

10. Shin, J., Chang, Y. K., Heung, B., Nguyen-Quang, T., Price, G. W., & Al-Mallahi, A. (2021). A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. 11. Noona, S. K., Amjada, M., Qureshia, M. A., & Mannanc, A. (2020). Use of deep learning techniques for identification of plant leaf stresses: A review. 12. Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2018). A review of neural networks in plant disease detection using hyperspectral data. 13. Chen, J.-D., Yin, H., Zhang, D.-F. (2020). A self-adaptive classification method for plant disease detection using GMDH-Logistic model. 14. Yogeshwari, M., & Thailambal, G. (2023). Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks. 15. Tiwari, V., Joshi, R. C., & Dutta, M. K. (2021). Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. 16. Geetharamani, G., & Arun Pandian, J. (2019). Identification of plant leaf diseases using a ninelayer deep convolutional neural network.

Exploring the Intersection of Machine Learning and Causality in Advanced Diabetes Management: New Insight and Opportunities Sahar Echajei, Yman Chemlal, Hanane Ferjouchia, Mostafa Rachik, Nassim Essabah Haraj, and Asma Chadli

Abstract

In light of the exponential surge in extensive quantities of medical data and the intrinsic uncertainty it engenders, numerous data specialists and epidemiologists have proposed various approaches to analyze causal effects from observational data, bridging the gap between health science and data analysis. The present emphasis on enhancing diabetes prevention and managing its complications primarily arises from two key factors: (i) the escalating occurrence of diabetes and (ii) significant advancements in clinical inquiries, specifically observational investigations, facilitated by the increasing accessibility of Real-World Evidence. This paper aims to synthesize the discoveries derived from a multitude of meticulously selected research papers that delve into the application of Machine Learning and Causal Inference methodologies within the healthcare domain, with a distinct concentration on diabetology. The objective is to address inquiries pertaining to cause-and-effect relationships. This will serve as the fundamental basis for constructing a causal system to forecast the optimal sequence of pharmaceuticals to be administered to a patient and effectively manage the process of drug dosage planning. Machine Learning helps understand intervention impacts in S. Echajei (B) · Y. Chemlal · H. Ferjouchia · M. Rachik Department of Mathematics and Computer Science, Faculty of Sciences Ben M’sik, University of Hassan II Casablanca, Sidi Othman, BP 7955, Casablanca, Morocco e-mail: [email protected]; [email protected] N. E. Haraj · A. Chadli Department of Endocrinology, Diabetology and Metabolic Diseasesry, Ibn Rochd University Hospital, Casablanca, Morocco Laboratory of Neurosciences and Mental Health, Faculty of Medicine and Pharmacy, University of Hassan II Casablanca, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_13

237

238

S. Echajei et al.

complex causal landscapes with diverse effects, aiding decision-makers with valuable approximations of alterations and variables.

1

Introduction

Diabetes is widely recognized as a silent epidemic on a global scale, characterized by a chronic disorder resulting from insufficient insulin secretion by the pancreas or impaired insulin utilization within the body. Insulin, a crucial hormone, plays a fundamental role in maintaining optimal glucose levels in the bloodstream [1]. The current focus on advancing diabetes research aims to contribute to various improvements, including enhancing the accuracy of decisions made by experts to delay the progression of the disease, ensure targeted drug selection, extend life expectancy, alleviate symptoms, and identify associated complications. For a comprehensive understanding of health data and the estimation of cause-effect relationships, experimental design serves as the baseline methodology. However, these tests are often expensive, time-consuming, or impractical when there are a significant number of factors to consider, among other challenges. In this chapter, a systematic review (SR) is conducted to provide a synthesis and summary of selected articles, addressing the following research questions (RQs): (1) the utility of association and prediction in clinical decision-making (RQ1), (2) the reintroduction of a causal approach for diabetic data analysis (RQ2), and (3) the advancement of Machine Learning methods in key aspects of diabetes research (RQ3).

2

Association and Prediction

The principal categories of scientific advancements made by data science are the predictive approach, which contributes to personalized medicine and facilitates tailored prognostication, and the exploratory approach, which plays a vital role in advancing precision medicine and unveiling statistical correlations among cohorts [2]. In many diabetology studies, there was some confusion between the two routes. Predictive analysis uses historical and real-time information to map inputs to outputs and find trends. It enables us to predict future outcomes by utilizing various analytics, ranging from basic calculations like correlation coefficients or risk differences to complex algorithms that serve as classifiers or predictors for multiple variables ‘joint distribution’ [3]. In the predictive route, understanding the data is not paramount. The objective of the predictive route is to generate high performing prediction models by utilizing the best combination of available biomarkers, without necessarily implying a causal relationship between predictors and outcomes.

Exploring the Intersection of Machine Learning and Causality …

239

The explanatory approach, based on association techniques, explores the available data to uncover disparities among groups. For example, by discerning unique diabetes clusters that exhibit diverse links to disease advancement and complications, it unravels a comprehensive understanding of the condition. This methodology innovatively explores the distinctions within the data, shedding light on critical aspects of diabetes management [4]. Relying solely on a significant statistical association proves inadequate in achieving greater predictive precision, as large overlapping distributions emerge when stratifying data based on future disease status. Therefore, alternative approaches are necessary to overcome this limitation and improve the accuracy of predictions [2].

3

Causal Inference

The increasing accessibility of Real-World Evidence (RWE) and advancements in scientific methodologies are driving a paradigm shift towards personalized medicine, emphasizing individualized clinical decision-making, as opposed to precision medicine. The preference for precision medicine among certain experts stems from limited evidence and a desire to avoid misconceptions surrounding the term ‘personalized medicine’ [2]. Causality has been described in various ways. According to its general definition, causal inference refers to the systematic study of cause-and-effect relationships, enabling the estimation of the impact of one phenomenon on another. Pearl (2009) [5] asserts that causality is a fundamental aspect of human cognition, emerging early in human development. For instance, from a young age, children often pose questions starting with ‘why.’ Through interaction with their environment, they begin to grasp the concept of cause and effect, understanding that shaking a rattle produces sound. Thus, they gradually comprehend that every effect is a consequence of a cause. As correlation alone does not imply causality, one may question how knowledge of causality can be acquired and what experiences can justify labeling a correlation as ‘causality’. Recent work by Illari and Russo (2014) [6], which integrates philosophical and scientific theories, offers a clear distinction between five significant domains associated with the concept of ‘cause’ (Fig. 1). In observational studies, it is crucial to identify and measure biases to ensure accurate causal analysis and avoid the pitfalls of Simpson’s paradox, which may not always be evident. Biases, including confounding and collider bias, as well as M-bias, where a variable can be mistaken for a confounding factor, pose significant challenges in establishing causal effects using observational data. For example, the relationship between education and diabetes is influenced by factors such as family history and income, leading to potential M-bias scenarios in contemporary epidemiology [7]. Other essential concepts in causal modeling include moderators, mediators, instrumental variables, and their roles in understanding causality.

240

S. Echajei et al.

Fig. 1 The five scientific problems of causality related to science

It is important to emphasize that the selection of a causal framework, which can be acquired by Machine Learning algorithms, can impact the assessment of causal impact. Achieving effective control for confounders in high dimensional settings revolves around key techniques such as propensity score matching, which balances covariates between treatment groups to minimize bias. Inverse probability weighting is another technique where the inverse of propensity scores is used to assign appropriate weights to observations. Additionally, various related methods can be employed [8–11].

3.1

Prediction Versus Causality

Distinguishing between prediction and causal inference tasks heavily relies on expert knowledge. Experts play a vital role in formulating precise questions, acquiring suitable data, and comprehending the intricate causal framework of the system under study. Their expertise is crucial in navigating the nuances of causal inference tasks, including identifying unaccounted factors and making necessary adjustments even in the absence of explicit data. Sensitivity analyses, introduced by human specialists, aid in evaluating the reliability of causal inferences, even when critical features are missing [12]. When algorithms can seamlessly integrate and incorporate relevant expertise, the distinction between causal inference and prediction becomes less significant within the context of decision-making [3, 13–16]. For example, in the field of autonomous robotics, an algorithm that continuously analyzes vast amounts of data can accurately forecast

Exploring the Intersection of Machine Learning and Causality …

241

the counterfactual state of a robot navigating its surroundings, even in the presence of sensor malfunctions that hampers obstacle perception. In such scenarios, the algorithm has the potential to anticipate the overall system response under simulated interventions, highlighting the importance of integrating causal reasoning into prediction tasks.

3.2

Association Versus Causality

Conventional statistical analysis primarily aims to assess the features and properties of a distribution by analyzing samples drawn from the same distribution. By leveraging these parameters, researchers can establish connections and relationships between features, enabling them to estimate the probabilities of past and future events and adjust those probabilities based on new insights and information. However, the objective of causal analysis extends beyond deducing concepts and probabilities in static circumstances. It seeks to comprehend the dynamics of statis tical inference in response to changing conditions, such as modifications induced by external interventions or treatments [17]. Within the realm of distribution functions, the information they contain does not inherently reveal how the distribution would respond to alterations in external conditions. Specifically, when transitioning from an observational setup to an experimental one, uncertainties arise regarding the impact on the distribution’s properties. This uncertainty arises because the laws of probability theory do not prescribe how changes in one property of the distribution correspond to modifications in other properties [17–19]. To address this challenge, causal assumptions play a crucial role. These assumptions elucidate the relationships that remain unchanged despite variations in external circumstances, thus offering insights into the effects of modifying different aspects of the distribution. In 1965, Sir Austin Bradford Hill introduced the Bradford Hill criteria as a framework for evaluating the likelihood of a causal relationship when examining observed associations. These criteria encompass nine dimensions (Fig. 2): strength, temporality, coherence, consistency, biological gradient, experimental evidence, specificity, plausibility, and analogy. These perspectives serve as guidelines for assessing epidemiological evidence to infer causation [20, 21]. By systematically evaluating these criteria, researchers can gain a deeper understanding of the causal nature of observed associations and make informed conclusions about causation. Expertise in understanding causality and utilizing diverse statistical methodologies, often built upon associations, is essential for the evaluation of causal inference. Examples of such methodologies include Two-Stage Least Squares Regression, Difference-inDifferences (DiD), Regression Discontinuity Design (RDD), Fixed Effects Models, Regression Models Employing Propensity Score Matching, Instrumental Variable Regression, Panel Data Models, and Synthetic Control Methods [22].

242

S. Echajei et al.

Fig. 2 The nine Bradford Hill viewpoints, used to assess causality within epidemiology

3.3

Validity and Reliability of Real-World Evidence, and Causal Inference

Worldwide implementation of actions to support and promote observational research includes: (i) Adopting electronic health records and expanding data volume, (ii) establishing research institutes for clinical research on diseases and patient outcomes, and (iii) forming multinational collaboration networks to investigate clinical data’s impact in real medical scenarios [23–26]. Addressing concerns related to missing data and expanding data accessibility, which enhances causal inference, can be facilitated by linking data from various sources, including claims data and Electronic Medical Records (EMRs). Employing formal causality approaches is recommended in epidemiological science to enhance analytical precision, and asking causal questions is deemed neces sary [8]. Utilizing causal methodologies in personalized clinical decision-making enables tailored preventive measures, accurate diagnoses, appropriate drug prescriptions, and suitable treatment regimens. This approach empowers healthcare professionals to recommend

Exploring the Intersection of Machine Learning and Causality …

243

precise interventions, optimal dosages, appropriate timing, and customized treatments according to individual patient needs. Indeed, the primary results of causal analysis are based on contemporary advances in the following areas: (i) Analysis based on counterfactual reasoning, (ii) structural equations without relying on specific parameter assumptions, (iii) visual models representing relationships, and (iv) an integrated approach combining counterfactual and graphical techniques. While selecting statistical methodologies is of secondary importance when ana lying observation-gathered data, the primary focus lies in the design aspect, which takes precedence. The link between graphical and probabilistic dependencies can be described in several ways. Counterfactual Analysis: Causality can be formalized using the theory of counterfactual events, comparing observed events in one reality to unobserved events in another. The counterfactual-based definition incorporates a hidden temporal aspect and operates sequentially, where consequences can transform into causes for subsequent outcomes. Prior investigations have also revealed a high degree of concordance between therapeutic outcomes observed in randomized controlled trials and observational studies conducted within corresponding illness domains [27, 28]. Differences in study design, applied statistical methods, data processing, and time periods of analysis contribute to divergent results. Graphical analysis: Graphical models, particularly Bayesian networks, probabilistically represent structural independencies among variables by encoding them as graphs. They are used for their ability to make inferences under uncertainty and for their learning algorithms to predict, control, and simulate system behavior, analyze data, and make decisions. Bayesian methodologies are grounded in fundamental principles and guidelines, encompassing: (i) Bayes’ theorem, (ii) the notion of variables exhibiting conditional independence, (iii) streamlining or simplifying models, (iv) estimating the joint probability distribution of the dataset, and (v) visually portraying this distribution through graphical depictions [29]. In addition to being tools for graphical representation, Bayesian networks can be interpreted as causal structures (Pearl, 2000, 2009) and used to estimate causal effects.

4

Advancement of Machine Learning Methods in Diabetology

Machine learning (ML) can be a powerful asset in various applications, especially in addressing medical needs through Real-World Evidence (RWE). Several ML technologies are beginning to integrate into the healthcare system, contributing to improved diagnostic accuracy in fields like radiology.

244

S. Echajei et al.

According to Arthur, this scientific field involves the study of empowering computers with the capability to acquire knowledge and improve their performance through autonomous learning, without the requirement of explicit programming. The performance of ML models improves through experience. Machine Learning algorithms are commonly categorized into three major groups [30]: . Supervised learning: The process of this group involves extracting a function from labeled training data. The Machine Learning algorithm utilizes prior knowledge regarding the desired information contained within the data to establish a connection between the given inputs and their corresponding outputs. Typically, such models are utilized to address challenges related to regression and classification problems. . Unsupervised learning aims to identify patterns and structures of unlabeled data. Unlike supervised learning, where the output variable is predetermined, unlabeled data lacks a fixed output variable. In this context, the model acquires knowledge autonomously from the data, seeking to identify inherent structures and features within it. The ultimate goal is to generate an output based on this acquired understanding. Several noteworthy algorithms used in unsupervised learning, including Clustering (to find clusters in the data), Anomaly Detection, Association (to identify rules existing between clusters and discover interesting relationships between variables in large databases), and Dimensionality Reduction (to select and extract a subset of features). . Reinforcement learning employs an iterative algorithm that acquires knowledge by engaging with a dynamic environment, where it encounters positive or negative feedback. Within this framework, the algorithm persistently endeavors to achieve a specific objective. The agent is rewarded upon the successful completion of each task or action, while an additional penalty is imposed if the task is performed inadequately. Machine Learning in Diabetology The pathogenesis of Type 2 Diabetes Mellitus (T2DM) is influenced by both hereditary and environmental fac tors. Individuals with metabolic syndrome face an elevated susceptibility to the onset of T2DM. The repercussions of T2DM, insulin resistance, and persistent hyperglycemia are attributed to the involvement of various organs (Fig. 3) [31]. Substantial progress has been made in virtually every facet of diabetes research, with notable contributions spanning a wide range of areas, including: (i) Discovering and validating biomarkers, (ii) Advancing predictive models for the early detection of diabetes mellitus (DM), (iii) Early prediction of diabetic complications, (iv) Drugs and therapies.

Exploring the Intersection of Machine Learning and Causality …

245

Fig. 3 Pathophysiology of T2DM

4.1

Biomarker Identification and Prediction of DM

Biomarkers and diagnostics play a crucial role in identifying diseases or pathogenic processes, assessing patients, and evaluating their response to drugs or exposures. In the context of DM, biomarkers can indicate the presence and severity of hyperglycemia, as well as the presence and severity of diabetes-related comorbidities. Genetic Background and Environment: In addition to the previously mentioned biomarkers, the increasing prevalence of diabetes within the population may be attributed to alterations in the global environment, which play a significant role in beta cell dysfunction, rather than being solely influenced by genetic diversity. The onset and progression of the disease involve the interplay of multiple genetic factors [46–49]. Several reviewed articles analyze the relationship between the HLA (Human Leukocyte Antigens) gene complex and TD1 [50–52], while others attempt to predict pleiotropic gene associations with DM [53]. As we continue to discover additional genes implicated in the pathogenesis of diabetes, our understanding of the underlying mechanisms driving the onset and progression of the condition is greatly enhanced. These investigations hold the potential to provide novel insights into the genetic epidemiology of diabetes, shedding light on the intricate interplay between genes and their interaction with the environment. Table 1 presents selected research that utilizes Machine Learning methods to identify the most relevant diagnostic markers, thereby improving the accuracy of predicting the

246

S. Echajei et al.

risk of developing diabetes and Fig. 4 illustrates the number of scientific articles that have identified and utilized key biomarkers. The early diagnosis of diabetes is extremely important for: (i) delaying disease progression, (ii) targeted drug selection, (iii) extending life expectancy and alleviating symptoms, and (iv) identifying associated complications. Table 2 presents selected research works on early diabetes prediction, classified by Machine Learning techniques.

4.2

Diabetic Complications

Hyperglycemia is the primary pathophysiological feature of diabetes, and it gives rise to harmful effects, including macrovascular complications (e.g., peripheral artery disease, stroke, coronary artery disease, etc.) and microvascular complications (e.g., retinopathy, nephropathy, diabetic neuropathy, etc.). Table 3 displays selected research that utilizes Machine Learning methods for predicting diabetes complications. It is also worth noting that several applications have been developed for the longterm evaluation of diabetes-related complications [115–118], including, among others: (i) LTRA (Long-Term Risk Assessment System), which employs various methods for feature analysis and selection, (ii) A similar application, UKPDS (United Kingdom Prospective Diabetes Study), calculates the risk of developing fatal coronary heart disease or stroke over the next 10 years. (iii) Another system, Q-Risk, incorporates a broader set of clinical parameters, and so on.

4.3

Drugs and Therapies

The design and discovery of drugs represent major challenges in current diabetes research. Research in this area covers several aspects, including drug prescriptions, dose management, exploration of adverse reactions of medications (excluding antidiabetic drugs), and prediction of individualized blood sugar response after the administration of diabetes drugs. These articles (Table 4) delve into various aspects of drug therapy, shedding light on prescribing practices, optimizing dosage strategies, examining side effects, and predicting individualized treatment outcomes. By exploring these different facets, researchers aim to enhance understanding and effectiveness of drug-based interventions for improved patient care. It is worth mentioning that there is still a lot of work to be done in drug design and therapeutic protocols, including the evaluation and exploration of data related to wellknown hypoglycemic factors such as metformin.

Exploring the Intersection of Machine Learning and Causality …

247

Table 1 Examples of specific machine learning algorithms used for biomarker identification and prediction of DM Methods

Description

References

Random Forest and RReliefF

These methods are utilized to predict short-term subcutaneous glucose concentrations

[32–34]

Filter and wrapper methods

Various filter and wrapper methods, including the P-value method, are employed to select predictors of diabetes. Models based on the wrapper approach yield the best results

[35]

Random forest and gas-chromatography and mass spectrometry

Combined approach is used to investigate the connections between AMPK and diabetes mellitus

[36]

Decision Tree

The General Location Model (GLM) approach [37] is adapted to handle missing values, and Decision Tree is employed to identify additional biomarkers apart from HbA1c. This combination leads to an improvement in predictive accuracy. For other studies, Support Vector Machine (SVM) classifiers have demonstrated comparable accuracies and computational efficiencies

[38–40] Improved electromagnetism- like The algorithm presented in this study mechanism integrates the nearest neighbor classifier and the opposite sign test with the electromagnetism-like algorithm, resulting in a novel computational approach Genetic programming

By aggregating existing features related to diabetes, this algorithm develops new features

[41]

Clustering/hierarchical clustering A newly proposed data-centric framework is introduced for extracting predictive characteristics using a clustering algorithm. The primary objective of this algorithm is to enhance the generation of clusters and utilize them as predictive features for forecasting the severity of a patient’s condition and assessing the risk of patient readmission

[42]

(continued)

248

S. Echajei et al.

Table 1 (continued) Methods

Description

References

Iterative Sure Independence Screening (ISIS)

ISIS is employed to select all relevant features [43, 44] in high dimensional data analysis, such as genomic data

Morlet Wavelet Support Vector Machine Classifier and Linear Discriminant Analysis methods

An automatic diagnostic system for diabetes, using LDAMWSVM, is applied for variable extraction and reduction through the LDA method, followed by classification using the MWSVM classifier

[45]

Fig. 4 Articles by biomarker (as a percentage)

5

Conclusion

Machine Learning algorithms are frequently used for analysis, forecasting, and pol icy making purposes. They are extremely flexible and useful in various empirical scenarios in the fields of epidemiology, economics, political science, etc. In complex causal environments, where the number of variable factors exceeds hundreds and changes from one observation to another, it becomes impossible to represent all relationships since no expert can know them all. Under these circumstances, ML methods are particularly useful for identifying and estimating heterogeneous effects, which is highly relevant for decision-makers seeking a complete understanding of the effects of interventions. In epidemiology, considering the intricate nature of the human body’s composition, its physical limitations, and substantial diversity, integrating Machine Learning and Causal

Exploring the Intersection of Machine Learning and Causality …

249

Table 2 Examples of specific machine learning algorithms used for the prediction of diabetes mellitus Methods and descriptions

References

Multiple investigations can be categorized into three distinct approaches: a. The utilization of conventional clinical predictive research, which involves employing Logistic Regression models, limited sets of features, and extensive sample sizes; b. The consideration of traditional risk factors associated with diabetes as features, and the evaluation of the performance of various models such as Classification And Regression Trees, Decision Trees, and Support Vector Machines in effectively combining these features; c. The adoption of a comprehensive range of features and the use of logistic regression for predictive modeling

a. [54–56] b. [57–63] c. [64–66]

By leveraging a diverse and comprehensive feature set, along with a linear model trained using L1-Regularized Logistic Regression, the algorithm demonstrates a performance level that either matches or surpasses Random Forests, Gradient-Boosted Decision Trees, Neural Networks, or others

[67–70]

Forecasting subcutaneous glucose levels is approached as a multivariate regression task, wherein support vector regression is employed as the underlying methodology

[71]

Utilizing a classification algorithm, FCS-ANTMINER, based on the Ant Colony Optimization method, to generate a comprehensive set of fuzzy rules for the accurate diagnosis of diabetes disease

[72]

To produce training data with noisy labels, employ a set of phenotype-specific keywords and then train L1 Penalized Logistic Regression models for both chronic and acute diseases to obtain valuable insights and predictive capabilities for each condition

[73]

Developing a disease progression model that incorporates trajectories, considering [74] the series of events that contributed to a particular state. Researchers have identified a common pattern in diabetes diseases. It typically begins with high cholesterol levels or dyslipidemia, followed by high blood pressure, elevated fasting blood sugar levels, and ultimately leads to T2DM (continued)

250

S. Echajei et al.

Table 2 (continued) Methods and descriptions

References

Utilizing association rules to identify potentially significant connections among risk factors in an interpretable manner, as well as for risk quantification. This involves: a. Developing a multivariate statistical study through logistic regression; b. Introducing Survival Association Rule Mining, that accommodates (i) survival outcomes, (ii) adjustments for confounders, and (iii) incorporation of dosage effects This study compares various algorithms, including the Framingham model and its variants (Fram Logit and Fram Refit), unpenalized survival models for all variables, survival association rules with and without dosage effects, diverse Lasso-penalized survival models, and the framework model; c. Analyzing temporal patterns in complex multivariate time series data to reveal predictive patterns applicable for anomaly detection and tracking. Additionally, this exploration contributes to enhancing existing classifiers, thereby improving their performance

a. [75, 76] b. [57] c. [77]

Employing ensemble approaches proves to be an effective method: a. By leveraging an advanced analytical technique known as Reverse Engineering and Forward Simulation (REFS), ensembles of predictive models were generated to forecast the progression of T2DM or prediabetes. The REFS methodology utilizes a Bayesian Scoring algorithm to explore a diverse range of models, resulting in an ensemble of forecasting models that provide a distribution of risk estimates b. The implementation of a combined methodology incorporates layered classification methods. The proposed model, named” HM-BagMoov,” utilizes an ensemble of seven heterogeneous classifiers. Another application, called ‘Telehealth’, utilizes the proposed model for diagnostic advice in hospitals; c. The use of Rotation Forest, a newly proposed classifier ensemble algorithm, constructs an ensemble of classifiers consisting of 30 Machine Learning algorithms, including Bayesian Logistic Regression, BayesNet, Logistic Regression, Multi-Layer Perceptron, and RBF Network. This ensemble can enhance the precision of various ML algorithms, contributing to the development of cutting-edge computer-aided diagnosis (CADx) systems; d. An ensemble learning approach is utilized to transform the opaque decision-making process of SVM into interpretable and transparent rules. The objective is to enhance the comprehensibility and transparency of SVM models, which are often considered” black boxes”

a. b. c. d.

[78] [79] [80] [81]

Inference models can pave the way for optimal decision-making. These models facilitate the identification of novel factors contributing to illness, tailoring treatments to individual patients, and proactively mitigating the likelihood of complications. Diabetes poses a significant global health challenge due to its widespread prevalence and impact. Despite extensive efforts focused on advancing medicinal chemistry and treatment strategies, the current landscape of treatments for this com plex disease faces several

Exploring the Intersection of Machine Learning and Causality …

251

Table 3 Examples of specific machine learning algorithms used for predicting diabetes complications Methods

Description

References

Diabetic nephropathy

Clinical and genetic data were employed for the identification [82] of this disease. The algorithm utilized in this study is Decision [83] Tree A comparison of several ML methods has been conducted: SVM and Random Forest algorithms have shown higher efficiency in achieving better disease prediction accuracy

Diabetic peripheral neuropathy (DPN)

Random forest is one of several methods used for the selection of specific variables for disease prediction

[84]

Cardiovascular To determine the most effective combinations of Ewing tests, Autonomic the optimal decision path finder and decision tree methods Neuropathy (CAN) were utilized

[85–87]

Alzheimer

Semantic data mining was utilized to analyze the relationship between diabetes and Alzheimer’s and identify genes linked to both diseases

[88, 89]

Comorbid depression

Logistic regression demonstrated the best predictive accuracy for this complication

[90]

Diabetic foot infections

Various classifiers, including Neural Networks and Support Vector Machine, were employed to predict the identified species associated with this disease

[91]

Liver cancer

Numerous frameworks have emerged to forecast the [92, 93] likelihood of encountering hepatic malignancy in the forthcoming years The implementation of the SVM model has the potential to enhance the identification of hepatocellular carcinoma (HCC) at an early stage among individuals with T2DM

Heart disease and stroke

A combined approach was developed, integrating components [94, 95] of the Conditional Random Field classifier, to identify the causal agents of this particular disease (continued)

252

S. Echajei et al.

Table 3 (continued) Methods

Description

References

Hypoglycemia

This disease is mainly induced by medication use in certain patients with other medical conditions, resulting in decreased blood sugar concentration Several ML methods have been used to make predictions for this disease, including Support Vector Machines and Random Forest, among others

[96–99]

Diabetic Retinopathy (DR)

A logistic regression approach was employed to pinpoint variables linked to the occurrence of retinopathy, including age of diagnosis, HbA1c, and systolic blood pressure Several ML methods, based on image processing techniques, have been employed: a. Using the Gradient Boosting Machine algorithm for diabetic retinopathy screening, based on proteomic analysis data; b. The most relevant protein biomarkers for early prediction of this disease are Apolipoprotein A-IV/APOA4, Complement component C7, CLU, ITIH2; c. Using LASSO regression to predict DR; d. Using fuzzy logic classifiers to predict diabetic macular edema (DME); e. Evaluating the severity level of DR by employing a Machine Learning diagnostic system to examine fundus images; f. The method, Diabetic Fundus Image Recuperation, involves using digital retinal fundus images to select variables and applying an SVM algorithm for predictions g. The development of an algorithm that decides whether or not a patient should be referred. An algorithm based on grouping of results by meta-classification; h. A cluster of factors derived from color assessment, wavelet dissection, and automated lesion segmentation was selected to form a classifier capable of diagnosing DME. Among the approaches used are thresholding methods, region growing methods, morphological methods, classification methods, etc. i. Examining a set of features obtained from tongue scans to identify background diabetic retinopathy and diabetes mellitus

[100–105] a. [106] b. [107] c. [108] d. [109] e. [110] f. [111] g. [112] h. [113] i. [114]

limitations. Many existing therapies have been developed without well-defined biomolecular targets or a comprehensive understanding of the underlying disease development. Moreover, the efficacy of current therapeutic approaches is often compromised by a range of side effects. As scientific knowledge regarding the pathophysiology of diabetes continues to expand, the development and discovery of innovative drugs that can effectively

Exploring the Intersection of Machine Learning and Causality …

253

Table 4 Methods for conception, identification, and prediction of prescribed medicines Application

Methods and descriptions

References

Improving insulin treatment in patients with T2DM and predicting the sequence of medicines to prescribe for medication dosage planning

Sequential pattern mining techniques are employed to discover patterns in the sequential data of blood glucose levels Differential sequence patterns are used to visualize observed variations in blood glucose levels A combination known as run-to-run (R2R) utilizes sporadic continuous glucose monitoring data, coupled with problem-solving methodology that involves using past cases as a basis for solving new problems The HDMR representation model (High Dimensional Model Representation) is utilized for data analysis Clustering techniques are employed to group similar data instances together

[119] [120] [121] [122] [123]

Exploring innovative agents with the potential to treat diabetes

A decision tree algorithm is utilized, which relies on specific physical and chemical characteristics to evaluate the effectiveness of inhibiting DPP4

[124]

Evaluating the inhibitory effects of flavonoids on AR (Aldose Reductase) activity as an effective treatment for diabetes

The QSAR (Quantitative Structure Activity Relation) model, based on artificial neural networks, is used for predictive modeling

[125]

Analyzing the relationship between statin use (reductase inhibitors) and diabetes

A new method based on association rule mining is proposed for discovering patterns and relationships in the data

[126]

Identifying reproducible predictors of response to dapagliflozin specific therapy

Data mining methods are evaluated for the identification of relevant variables

[127]

Developing personalized medicine recommendations for patients with T2D

The Wrapper and filter approach is used [128] for the selection of the most relevant variables, resulting in a subset of 39 variables out of 258. The K-Nearest Neighbor algorithm is then applied to improve the accuracy of the classification (continued)

254

S. Echajei et al.

Table 4 (continued) Application

Methods and descriptions

References

Predicting successful diabetes remission Artificial neural networks are utilized for [129] [130] marker selection in gastrointestinal surgery, considering operative methods (LGB, LMGB and LSG), waist circumference, and c-peptide levels Logistic regression and back-propagation neural networks are employed for the identification of the best predictors of remission in bariatric surgery. Age of diagnosis for T2DM is identified as the strongest predictor of DM remission after surgery, among others Implementing customized dietary interventions for managing diabetes

The’Gradient Boosting Regression’ [131] algorithm is used to Personalize Postprandial Blood Glucose Responses to real meals (PPGR)

address the complexities of the disease remain substantial challenges in contemporary diabetes research. In this regard, our research project would focus on: (i) establishing a database of diabetes patients in Morocco, specifically in Casablanca, which will serve as a basis for (ii) the development of a causal system that combines Machine Learning methods with Causal Inference to predict the sequence of drugs to be prescribed to a patient and manage the drug dosage planning process, with a specific focus on insulin dosage. This system would, among other things, improve insulin treatment for diabetes patients. Applying intelligence to precision and personalized medicine makes it possible to adapt treatment to individual patient needs by comparing patients’ physiological characteristics and diseases to those of patients with the best therapeutic results. Providing tailored treatment solutions can reduce healthcare costs while also enhancing patient satisfaction and overall well-being.

Exploring the Intersection of Machine Learning and Causality …

255

References 1. Diabetes. World health organization. Available via DIALOG. https://www.who.int/newsroom/fact-sheets/detail/diabetes. Cited Apr 2023. 2. Varga, T. V., Niss, K., Estampador, A. C., Collin, C. B., & Moseley, P. L. (2020). Association is not prediction: A landscape of confused reporting in diabetes—A systematic review. Diabetes Research and Clinical Practice, 170, 108497. https://doi.org/10.1016/j.diabres.2020.108497 3. Hernan, M. A., Hsu, J., & Healy, B. (2019). A second chance to get causal inference right: A classification of data science tasks. Chance, 32(1), 42–49. https://doi.org/10.1080/09332480. 2019.1579578 4. Ahlqvist, E., et al. (2018). Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables. The Lancet Diabetes & Endocrinology, 6(5), 361–369. https://doi.org/10.1016/s2213-8587(18)30051-2 5. Asvatourian, V. (2018). Contributions of causal modeling in the evaluation of immunotherapies based on observational data—Apports de la modelisation causale dans l’evaluation des immunotherapies a partir de donnees observationnelles. Ph.D. dissertation, University ParisSaclay. 6. Austin, C. P. (2016). Causality: An empirically informed plea for pluralism. Metascience. https://doi.org/10.1007/s11016-016-0062-0 7. Prosperi, M., et al. (2020). Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence, 2(7), 369–375. https://doi.org/10.1038/ s42256-020-0197-y 8. Crown, W. H. (2019). Real-world evidence, causal inference, and machine learning. Value in Health, 22(5), 587–592. https://doi.org/10.1016/j.jval.2019.03.001 9. Goodman, S. N., Schneeweiss, S., & Baiocchi, M. (2017). Using design thinking to differentiate useful from misleading evidence in observational research. JAMA, 317(7), 705. https://doi. org/10.1001/jama.2016.19970 10. Franklin, J. M., & Schneeweiss, S. (2017). When and how can real world data analyses substitute for randomized controlled trials? Clinical Pharmacology & Therapeutics, 102(6), 924–933. https://doi.org/10.1002/cpt.857 11. Fralick, M., Kesselheim, A. S., Avorn, J., & Schneeweiss, S. (2018). Use of health care databases to support supplemental indications of approved medications. JAMA Internal Medicine, 178(1), 55. https://doi.org/10.1001/jamainternmed.2017.3919 12. Robins, J. M., Rotnitzky, A., & Scharfstein, D. O. (2000). Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models (pp. 1–94). Springer eBooks. 13. Hernan, M. A. (2014). Invited commentary: Agent-based models for causal inferencereweighting data and theory in epidemiology. American Journal of Epidemiology, 181(2), 103–105. https://doi.org/10.1093/aje/kwu272 14. Leroy, J. L., et al. (2022). Strengthening causal inference from randomised controlled trials of complex interventions. BMJ Global Health, 7(6), e008597. https://doi.org/10.1136/bmjgh2022-008597 15. Glynn, A. N., & Ichino, N. (2014). Using qualitative information to improve causal inference. American Journal of Political Science, 59(4), 1055–1071. https://doi.org/10.1111/ajps.12154 16. Grotzer, T. A., & Tutwiler, M. S. (2014). Simplifying causal complexity: How interactions between modes of causal induction and information availability lead to heuristic-driven reasoning. Mind, Brain, and Education, 8(3), 97–114. https://doi.org/10.1111/mbe.12054

256

S. Echajei et al.

17. Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3. https://doi. org/10.1214/09-ss057 18. Spirtes, P., Glymour, C., & Scheines, R. (2001). Causation, prediction, and search. https://doi. org/10.7551/mitpress/1754.001.0001 19. Pearl, J. (2009). Causality: Models, reasoning, and inference. Cambridge University Press. 20. Nowinski, C. J. et al. (2022). Applying the Bradford hill criteria for causation to repetitive head impacts and chronic traumatic encephalopathy. Front. Neurol. 13. https://doi.org/10.3389/ fneur.2022.938163 21. Raman, T. Bradford Hill criteria for causality assessment. https://www.linkedin.com/pulse/bra dford-hill-criteria-causality-assessment-dr-tulasi-raman-p 22. Kang, H.-S., Kreuels, B., May, J., & Small, D. S. (2016). Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting. The Annals of Applied Statistics, 10(1). https://doi.org/10.1214/15-aoas894 23. Curtis, L. H., Brown, J. R., & Platt, R. W. (2014). Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Affairs, 33(7), 1178– 1186. https://doi.org/10.1377/hlthaff.2014.0121 24. Wallace, P. K., Shah, N., Dennen, T., Bleicher, P. A., & Crown, W. H. (2014). Optum labs: Building a novel node in the learning health care system. Health Affairs, 33(7), 1187–1194. https://doi.org/10.1377/hlthaff.2014.0038 25. Maret Ouda, J., Tao, W., Wahlin, K. J., & Lagergren, J. (2017). Nordic registry-based cohort studies: Possibilities and pitfalls when combining Nordic registry data. Scandinavian Journal of Public Health, 45(17), 14–19. 26. Concato, J., Shah, N. N., & Horwitz, R. I. (2000). Randomized, controlled trials, observational studies, and the hierarchy of research designs. The New England Journal of Medicine, 342(25), 1887–1892. 27. Benson, K., & Hartz, A. J. (2000). A comparison of observational studies and randomized, controlled trials. The New England Journal of Medicine, 342(25), 1878–1886. https://doi.org/ 10.1056/nejm200006223422506 28. Hsiao, F., Yang, C., Huang, Y., & Huang, W. (2007). Using Taiwan?s National health insurance research databases for pharmacoepidemiology research. Journal of Food and Drug Analysis. 29. Pe’er, D. (2005). Bayesian network analysis of signaling networks: A primer. Science’s STKE, 2005(281). https://doi.org/10.1126/stke.2812005pl4 30. Menon, K. (2023). An introduction to the types of machine learning. https://www.simplilearn. com/tutorials/machine-learning-tutorial/types-of-machine-learning 31. Pathophysiology—Diabetes Type 2. https://u.osu.edu/diabetes2018/patho/ 32. Georga, E. I., Protopappas, V. C., Polyzos, D., & Fotiadis, D. I. (2015). Evaluation of shortterm predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. Medical & Biological Engineering & Computing, 53(12), 1305–1318. https://doi.org/10.1007/s11517-015-1263-1 33. Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31–39. 34. Robnik-Sˇikonja, M., & Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning, 53, 23–69. https://doi.org/10.1023/A:1025667309714 35. Bagherzadeh-Khiabani, F., Ramezankhani, A., Azizi, F., Hadaegh, F., Steyerberg, E. W., & Khalili, D. (2016). A tutorial on variable selection for clinical prediction models: Feature selection methods in data mining could improve the results. Journal of Clinical Epidemiology, 71, 76–85. https://doi.org/10.1016/j.jclinepi.2015.10.002 36. Huang, J. Z., He, R., Yi, L., Xie, H., Cao, D.-S., & Liang, Y.-Z. (2013). Exploring the relationship between 5 AMP-activated protein kinase and markers related to type 2 diabetes mellitus. Talanta, 110, 1–7. https://doi.org/10.1016/j.talanta.2013.03.039

Exploring the Intersection of Machine Learning and Causality …

257

37. Jelinek, H. F., Stranieri, A., Yatsko, A., & Venkatraman, S. (2016). Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis. Computers in Biology and Medicine, 75, 90–97. https://doi.org/10.1016/j.compbiomed.2016.05.005 38. Wang, K.-J., Adrian, A. M., Chen, K.-H., & Wang, K.-J. (2015). An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus. Journal of Biomedical Informatics, 54, 220–229. https://doi.org/10.1016/j.jbi. 2015.02.001 39. Vitola, J., Pozo, F., Tibaduiza, D. A., & Anaya, M. (2017). A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors, 17(2), 417. https://doi.org/10.3390/s17020417 40. Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X.-D., & Wang, S. (2011). An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8(2), 191–200. https://doi.org/10.1016/s1672-6529(11)60020-6 41. Aslam, M., Zhu, Z., & Nandi, A. K. (2013). Feature generation using genetic programming with comparative partner selection for diabetes classification. Expert Systems With Applications, 40(13), 5402–5412. https://doi.org/10.1016/j.eswa.2013.04.003 42. Sideris, C., Pourhomayoun, M., Kalantarian, H., & Sarrafzadeh, M. (2016). A flexible datadriven comorbidity feature extraction framework. Computers in Biology and Medicine, 73, 165–172. https://doi.org/10.1016/j.compbiomed.2016.04.014 43. Cai, L.-H., Wu, H., Li, D., Zhou, K., & Zou, F. (2015). Type 2 diabetes biomarkers of human gut microbiota selected via iterative sure independent screening method. PLoS ONE, 10(10), e0140827. https://doi.org/10.1371/journal.pone.0140827 44. Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society Series B-statistical Methodology, 70(5), 849–911. https://doi.org/10.1111/j.1467-9868.2008.00674.x 45. Calisir, D., & Dogantekin, E. (2011). An automatic diabetes diagnosis system based on LDAwavelet support vector machine classifier. Expert Systems With Applications, 38(7), 8311– 8315. https://doi.org/10.1016/j.eswa.2011.01.017 46. Kaprio, J., Tuomilehto, J., & Koskenvuo, M. (1992). Concordance for type 1 (insulindependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia, 35(11), 1060–1067. https://doi.org/10.1007/bf02221682 47. Lopes, M., Kutlu, B., & Miani, M. (2014). Temporal profiling of cytokine-induced genes in pancreatic beta-cells by meta-analysis and network inference. Genomics, 103(4), 264–275. https://doi.org/10.1016/j.ygeno.2013.12.007 48. Lee, J., Keam, B., & Jang, E. J. (2011). Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data. Osong Public Health and Research Perspectives, 2(2), 75–82. https://doi.org/10.1016/j.phrp.2011.07.005 49. Yarimizu, M., Wei, C., Komiyama, Y., Ueki, K., Nakamura, S., Sumikoshi, K., Terada, T., & Shimizu, K. (2015). Tyrosine kinase ligand-receptor pair prediction by using support vector machine. Advances in Bioinformatics, 2015, 528097. https://doi.org/10.1155/2015/528097 50. Anjos, S., & Polychronakos, C. (2004). Mechanisms of genetic susceptibility to type I diabetes: Beyond HLA. Molecular Genetics and Metabolism, 81(3), 187–195. https://doi.org/10.1016/ j.ymgme.2003.11.010 51. Zhao, L. P., Bolouri, H., Zhao, M. L., Geraghty, D. E., & Lernmark, A. (2016). An objectoriented regression for building disease predictive models with multiallelic HLA genes. Genetic Epidemiology, 40(4), 315–332. https://doi.org/10.1002/gepi.21968 52. Nguyen, C., Varney, M. D., Harrison, L. C., & Morahan, G. (2013). Definition of high-risk type 1 diabetes HLA-DR and HLA-DQ types using only three single nucleotide polymorphisms. Diabetes, 62(6), 2135–2140. https://doi.org/10.2337/db12-1398

258

S. Echajei et al.

53. Park, S. H., Lee, J. Y., & Kim, S. (2011). A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes. BMC Systems Biology, 5(2), S13. https://doi.org/10.1186/1752-0509-5-S2-S13 54. Kahn, H. S., Cheng, Y. J., Thompson, T. J., Imperatore, G., & Gregg, E. W. (2009). Two riskscoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Annals of Internal Medicine, 150(11), 741. https://doi.org/10.7326/0003-4819-150-11-200906 020-00002 55. Lindstrom, J., & Tuomilehto, J. (2003). The diabetes risk score. Diabetes Care, 26(3), 725– 731. https://doi.org/10.2337/diacare.26.3.725 56. Rathmann, W., Kowall, B., Heier, M., & Herder, C. (2010). Prediction models for incident type 2 diabetes mellitus in the older population: KORA S4/F4 cohort study. Diabetic Medicine, 27(10), 1116–1123. 57. Simon, G. J., Schrom, J., Castro, M., Li, P. P., & Caraballo, P. J. (2013). Survival association rule mining towards type 2 diabetes risk assessment. PubMed. https://pubmed.ncbi.nlm.nih. gov/24551408 58. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/bf00994018 59. Loh, W.-Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8 60. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi. org/10.1007/bf00116251 61. Mani, S., Chen, Y., Elasy, T. A., Clayton, W., & Denny, J. C. (2012). Type 2 diabetes risk forecasting from EMR data using machine learning. 62. Meng, X., Huang, Y., Rao, D., Zhang, Q. G., & Liu, Q. H. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung Journal of Medical Sciences, 29(2), 93–99. https://doi.org/10.1016/j.kjms.2012.08.016 63. Breault, J. L., Goodall, C., & Fos, P. J. (2002). Data mining a diabetic data warehouse. Artificial Intelligence in Medicine, 26(1–2), 37–54. https://doi.org/10.1016/s0933-3657(02)00051-9 64. Sun, J., Hu, J., Luo, D., & Markatou, M. (2012). Combining knowledge and data driven insights for identifying risk factors using electronic health records. 65. Wang, F., Zhang, P., Qian, B., Wang, X., & Davidson, I. (2014). Clinical risk prediction with multilinear sparse logistic regression. https://doi.org/10.1145/2623330.2623755 66. Neuvirth, H., Ozery-Flato, M., & Hu, J. et al. (2011). Toward personalized care management of patients at risk: The diabetes case study. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA (pp. 395–403). 67. Razavian, N., Blecker, S., Schmidt, A. M., Smith-McLallen, A., Nigam, S., & Sontag, D. (2015). Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data, 3(4), 277–287. https://doi.org/10.1089/big.2015.0020 68. Ho, T. K. (2002). Random decision forests. https://doi.org/10.1109/icdar.1995.598994 69. Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. (1999). Boosting algorithms as gradient descent (vol. 12, pp. 512–518). 70. LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 71. Georga, E. I., et al. (2013). Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE Journal of Biomedical and Health Informatics, 17(1), 71–81. https://doi.org/10.1109/titb.2012.2219876

Exploring the Intersection of Machine Learning and Causality …

259

72. Ganji, M. F., & Abadeh, M. S. (2011). A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis. Expert Systems With Applications, 38(12), 14650– 14659. https://doi.org/10.1016/j.eswa.2011.05.018 73. Agarwal, V., Podchiyska, T., Banda, J. M., Goel, V., Leung, T. I., Minty, E. P., Sweeney, T. E., Gyang, E., & Shah, N. H. (2016). Learning statistical models of phenotypes using noisy labeled training data. Journal of the American Medical Informatics Association, 23(6), 1166–1173. https://doi.org/10.1093/jamia/ocw028 74. Oh, W., Kim, E., Castro, M. R., Caraballo, P. J., Kumar, V., Steinbach, M. S., & Simon, G. J. (2016). Type 2 diabetes mellitus trajectories and associated risks. Big Data, 4(1), 25–30. https://doi.org/10.1089/big.2015.0029 75. Ramezankhani, A., Pournik, O., Shahrabi, J., Azizi, F., & F. Hadaegh. (2015). An application of association rule mining to extract risk pattern for type 2 diabetes using Tehran lipid and glucose study database. International Journal of Endocrinology and Metabolism, 13(2). https:// doi.org/10.5812/ijem.25389 76. Abbasi, Peelen, L. M., Corpeleijn, E., van der Schouw, Y. T., Stolk, R. P., & Spijkerman, A. M. et al. (2012). Prediction models for risk of developing type 2 diabetes: Systematic literature search and independent external validation study. BMJ, 345(2), e5900. https://doi.org/10.1136/ bmj.e5900 77. Batal, D. F., Harrison, J., Moerchen, F., & Hauskrecht, M. (2012). Mining recent temporal patterns for event detection in multivariate time series data. https://doi.org/10.1145/2339530.233 9578 78. Anderson, J. P., Parikh, J. R., Shenfeld, D. K., Ivanov, V., Marks, C., Church, B. W., Laramie, J. M., Mardekian, J., Piper, B. A., Willke, R. J., & Rublee, D. A. (2015). Reverse engineering and evaluation of prediction models for progression to type 2 diabetes. Journal of Diabetes Science and Technology, 10(1), 6–18. https://doi.org/10.1177/1932296815620200 79. Bashir, S., Qamar, U., & Khan, F. R. (2016). IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework. Journal of Biomedical Informatics, 59, 185–200. https://doi.org/10.1016/j.jbi.2015.12.001 80. Ozcift, A., & Gu¨lten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine, 104(3), 443–451. https://doi.org/10.1016/j.cmpb.2011.03.018 81. Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2015). Rule extraction from support vector machines using ensemble learning approach: An application for diagnosis of diabetes. IEEE Journal of Biomedical and Health Informatics, 19(2), 728–734. https://doi.org/10.1109/jbhi. 2014.2325615 82. Huang, G.-M., Huang, K.-Y., Lee, T.-Y., & Weng, J. T.-Y. (2015). An interpretable rulebased diagnos- tic classification of diabetic nephropathy among type 2 diabetes patients. BMC Bioinformatics, 16(S1). https://doi.org/10.1186/1471-2105-16-s1-s5 83. Leung, R. K., Wang, Y., Ma, R. C., Luk, A. O., Lam, V., Ng, M., So, W. Y., Tsui, S. K., & Chan, J. C. (2013). Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: A prospective case-control cohort analysis. BMC Nephrology, 14(1). https://doi.org/10.1186/1471-236914-162 84. DuBrava, S., Mardekian, J., Sadosky, A., Bienen, E. J., Parsons, B., Hopps, M., & Markman, J. (2016). Using random forest models to identify correlates of a diabetic peripheral neuropathy diagnosis from electronic health record data. Pain Medicine, 18(1), 107–115. https://doi.org/ 10.1093/pm/pnw096

260

S. Echajei et al.

85. Jelinek, H. F., Wilding, C., & Tinley, P. (2006). An innovative multi-disciplinary diabetes complications screening program in a rural community: A description and preliminary results of the screening. Australian Journal of Primary Health, 12(1), 14. https://doi.org/10.1071/py06003 86. Stranieri, A., Abawajy, J. H., Kelarev, A. V., Huda, S., Chowdhury, M. U., & Jelinek, H. F. (2013). An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy. Artificial Intelligence in Medicine, 58(3), 185–193. https://doi.org/10.1016/ j.artmed.2013.04.007 87. Abawajy, J. H., Kelarev, A. V., Chowdhury, M. U., Stranieri, A., & Jelinek, H. F. (2013). Predicting cardiac autonomic neuropathy category for diabetic data with missing values. Computers in Biology and Medicine, 43(10), 1328–1333. https://doi.org/10.1016/j.compbiomed. 2013.07.002 88. De La Monte, S. M., & Wands, J. R. (2008). Alzheimer’s disease is type 3 diabetes—evidence reviewed. Journal of Diabetes Science and Technology, 2(6), 1101–1113. https://doi.org/10. 1177/193229680800200619 89. Narasimhan, K., et al. (2014). Diabetes of the brain: Computational approaches and interventional strategies. Cns & Neurological Disorders-drug Targets, 13(3), 408–417. 90. Jin, H., Wu, S., & Di Capua, P. (2015). Development of a clinical forecasting model to predict comorbid depression among diabetes patients and an application in depression screening policy making. Preventing Chronic Disease, 12. https://doi.org/10.5888/pcd12.150047 91. Yusuf, N., Zakaria, A., Omar, M. I., Shakaff, A. Y., Masnan, M. J., Kamarudin, L. M., Abdul Rahim, N., Zakaria, N. Z., Abdullah, A. A., Othman, A., & Yasin, M. S. (2015). In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0601-5 92. Rau, H.-H., et al. (2016). Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Computer Methods and Programs in Biomedicine, 125, 58–65. https://doi.org/10.1016/j.cmpb.2015.11.009 93. Azit, N. A., Sahran, S., Leow, V. M., Subramaniam, M., Mokhtar, S., & Nawi, A. M. (2022). Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model. Heliyon, 8(10), e10772. https://doi.org/10.1016/j.hel iyon.2022.e10772 94. Laing, S. P., et al. (2003). Mortality from heart disease in a cohort of 23,000 patients with insulin-treated diabetes. Diabetologia, 46(6), 760–765. https://doi.org/10.1007/s00125-0031116-6 95. Jonnagaddala, J., Liaw, S.-T., Ray, P., Kumar, M., Dai, H., & Hsu, C.-Y. (2015). Identification and progression of heart disease risk factors in diabetic patients from longitudinal electronic health records. BioMed Research International, 2015, 1–10. https://doi.org/10.1155/2015/ 636371 96. Cryer, P. E., Davis, S. M., & Shamoon, H. (2003). Hypoglycemia in diabetes. Diabetes Care, 26(6), 1902–1912. https://doi.org/10.2337/diacare.26.6.1902 97. Georga, E. I., Protopappas, V. C., Ardigo, D., Polyzos, D., & Fotiadis, D. I. (2013). A glucose model based on support vector regression for the prediction of hypoglycemic events under freeliving conditions. Diabetes Technology & Therapeutics, 15(8), 634–643. 98. Sudharsan, M. P., & Shomali, M. (2014). Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. Journal of Diabetes Science and Technology, 9(1), 86–90. https://doi.org/10.1177/1932296814554260 99. Jensen, M. H., Mahmoudi, Z., Christensen, T. F., Tarnow, L., Seto, E., Johansen, M. D., & Hejlesen, O. K. (2014). Evaluation of an algorithm for retrospective hypoglycemia detection using professional continuous glucose monitoring data. Journal of Diabetes Science and Technology, 8(1), 117–122. https://doi.org/10.1177/1932296813511744

Exploring the Intersection of Machine Learning and Causality …

261

100. Tapp, R. J., et al. (2003). The prevalence of and factors associated with diabetic retinopathy in the Australian population. Diabetes Care, 26(6), 1731–1737. https://doi.org/10.2337/diacare. 26.6.1731 101. Li & Li, H. K. (2013). Automated analysis of diabetic retinopathy images: Principles, recent developments, and emerging trends. Current Diabetes Reports, 13(4), 453–459. https://doi.org/ 10.1007/s11892-013-0393-9 102. Quellec, G., Lamard, M., Cochener, B., Decenciere, E., Lay, B., Chabouis, A., Roux, C., & Cazuguel, G. (2013). Multimedia data mining for automatic diabetic retinopathy screening. https://doi.org/10.1109/embc.2013.6611205 103. Prentasic, P., & Loncaric, S. (2014). Weighted ensemble based automatic detection of exudates in fundus photographs. https://doi.org/10.1109/embc.2014.6943548 104. Ogunyemi, O., & Kermah, D. (2015). Machine learning approaches for detecting diabetic retinopathy from clinical and public health records. PubMed, 2015, 983–990. 105. Torok, Z., Peto, T., Csosz, E., Tukacs, E., Molnar, A., Maros-Szabo, Z., et al. (2013). Tear fluid proteomics multimarkers for diabetic retinopathy screening. BMC Ophthalmology, 13, 40. https://doi.org/10.1186/1471-2415-13-40 106. Torok, Z., Peto, T., Csosz, E., Tukacs, E., Molnar, A. M., Berta, A., Tozser, J., Hajdu, A., Nagy, V., Domokos, B., & Csutak, A. (2015). Combined methods for diabetic retinopathy screening, using retina photographs and tear fluid proteomics biomarkers. Journal of Diabetes Research. 107. Jin, J., et al. (2016). Development of diagnostic biomarkers for detecting diabetic retinopathy at early stages using quantitative proteomics. Journal of Diabetes Research, 2016, 1–22. https:// doi.org/10.1155/2016/6571976 108. Oh, E., Yoo, T. H., & Park, E. C. (2013). Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study. BMC Medical Informatics and Decision Making, 13(1). https://doi.org/10.1186/1472-6947-13-106 109. Ibrahim, S., et al. (2015). Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. MBEC, 53(12), 1345–1360. 110. Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2014). DREAM: Diabetic retinopathy analysis using machine learning. IEEE Journal of Biomedical and Health Informatics, 18(5), 1717–1728. https://doi.org/10.1109/jbhi.2013.2294635 111. Krishnamoorthy, S., & Alli, P. (2015). A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image. PLoS ONE, 10(5), e0125542. https://doi.org/10.1371/journal.pone.0125542 112. Pires, R., Jelinek, H. F., Wainer, J., Goldenstein, S., Valle, E., & Rocha, A. (2013). Assessing the need for referral in automatic diabetic retinopathy detection. IEEE Transactions on Biomedical Engineering, 60(12), 3391–3398. https://doi.org/10.1109/tbme.2013.2278845 113. Giancardo, L., et al. (2012). Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis, 16(1), 216–226. https://doi.org/10. 1016/j.media.2011.07.004 114. Jia, W., Kumar, B. V. K. V., & Zhang, L. (2014). Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features. IEEE Transactions on Biomedical Engineering, 61(2), 491–501. https://doi.org/10.1109/tbme.2013.2282625 115. Pinhas-Hamiel, O., et al. (2013). Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus. International Journal of Eating Disorders, 46(8), 819–825. https://doi.org/10.1002/eat.22138 116. Lagani, V., et al. (2015). Development and validation of risk assessment models for diabetesrelated complications based on the DCCT/EDIC data. Journal of Diabetes and Its Complications, 29(4), 479–487. https://doi.org/10.1016/j.jdiacomp.2015.03.001

262

S. Echajei et al.

117. Lagani, V., et al. (2015). Realization of a service for the long-term risk assessment of diabetesrelated complications. Journal of Diabetes and Its Complications, 29(5), 691–698. https://doi. org/10.1016/j.jdiacomp.2015.03.011 118. Sacchi, L., Dagliati, A., Segagni, D., Leporati, P., Chiovato, L., & Bellazzi, R. (2015). Improving risk-stratification of diabetes complications using temporal data mining. https://doi.org/10. 1109/embc.2015.7318810 119. Wright, A. P., Wright, A., McCoy, A. B., & Sittig, D. F. (2015). The use of sequential pattern mining to predict next prescribed medications. Journal of Biomedical Informatics, 53, 73–80. https://doi.org/10.1016/j.jbi.2014.09.003 120. Deja, R., Froelich, W., & Deja, G. (2015). Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes. Biomed Eng Online. 121. Herrero, P., Pesl, P., Reddy, M., Oliver, N., Georgiou, P., & Toumazou, C. (2015). Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE Journal of Biomedical and Health Informatics. 122. Karahoca, A., & Tunga, M. A. (2012). Dosage planning for type 2 diabetes mellitus patients using Indexing HDMR. Expert Systems With Applications, 39(8), 7207–7215. https://doi.org/ 10.1016/j.eswa.2012.01.056 123. Namayanja, J. M., & Janeja, V. P. (2012). An assessment of patient behavior over time–periods: A case study of managing type 2 diabetes through blood glucose readings and insulin doses. Journal of Medical Systems. https://doi.org/10.1007/s10916-012-9894-3 124. Shoombuatong, W., Prachayasittikul, V., Anuwongcharoen, N., Songtawee, N., Monnor, T., Prachayasittikul, S., Prachayasittikul, V., & Nantasenamat, C. (2015). Navigating the chemical space of dipeptidyl peptidase-4 inhibitors. Drug Design Development and Therapy, 4515. https://doi.org/10.2147/dddt.s86529 125. Patra, J. C., & Chua, B. H. (2010). Artificial neural network-based drug design for diabetes mellitus using flavonoids. Journal of Computational Chemistry, 32(4), 555–567. https://doi. org/10.1002/jcc.21641 126. Schrom, J., Caraballo, P. J., Castro, M., & Simon, G. J. (2013). Quantifying the effect of statin use in prediabetic phenotypes discovered through association rule mining. 127. Bujac, S. R., et al. (2014). Patient characteristics are not associated with clinically important differential response to dapagliflozin: A staged analysis of phase 3 data. Diabetes Therapy. https://doi.org/10.1007/s13300-014-0090-y 128. Liu, H., Xie, G., Mei, J., Shen, W., Sun, W., & Li, X. (2013). An efficacy driven approach for medication recommendation in type 2 diabetes treatment using data mining techniques. PubMed, 192, 1071. 129. Lee, Y.-C., Lee, Y. Y., & Liew, P. L. (2013). Predictors of remission of type 2 diabetes mellitus in obese patients after gastrointestinal surgery. Obesity Research & Clinical Practice, 7(6), e494–e500. https://doi.org/10.1016/j.orcp.2012.08.190 130. Lee, Y. Y., et al. (2012). Predictors of diabetes remission after bariatric surgery in Asia. Asian Journal of Surgery, 35(2), 67–73. https://doi.org/10.1016/j.asjsur.2012.04.010 131. Zeevi et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001

For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used Arifullah, Aziza Chakir, Dorsaf Sebai, and Abdu Salam

Abstract

One of the cancers that causes the greatest mortality is liver cancer worldwide. Consequently, early identification and detection of potential Cancer mortality is decreased thanks to liver cancer. Traditionally, Histopathological Image Analysis (HIA) was performed, however these take a lot of time and require in-depth understanding. We the segmentation and classification of liver cells is advised to use a patch-based deep learning approach. In this work, complete slides are categorized and divided using a two-step process (WSI is a suggested image). WSIs must first be extracted into patches since they stand besides huge toward stay input directly interested in convolutional neural networks (CNN). Supplying the patches to a modified U-Net through its comparable veneer for targeted segmentation. For arrangement responsibilities the WSIs are Arifullah (B) Department of Computer Science Faculty of Computing and Artificial, Intelligent Air University, Islamabad, Pakistan e-mail: [email protected] A. Chakir Faculty of Law, Economics and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected] D. Sebai Cristal Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia A. Salam Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_14

263

264

Arifullah et al.

mounted at 4, equivalent to 3x, 16x, and 64x. Each scale’s deleted patches and associated labels are then fed into the convolutional network. Inference is a process where we majority voting on the convolutional neural network’s output network. Better outcomes have been seen with the suggested strategy. Whole-slide image, segmentation, classification, and patch-based methods for histopathological image analysis. Keywords

Segmentation • Liver Cancer • Histopathology Images • Convolutional Network

1

Introduction

Individual of the best serious fitness issues in the domain is liver cancer. Aimed at the treatment of liver cancer and the prognosis of the condition, the graded diagnosis for the disease in biopsy pictures is crucial. A classifying scheme that employs artificial intelligence to offer pathologists and doctors with quantifiable and objective results will not only save them time, but also increase the precision of their diagnoses [1]. The primary task in the grading method is grading using cancer biopsy image nucleus segments. However, poor focus and a complicated stroma background will compromise segmentation performance. For each level of cancerization, there are various therapy strategies and patient indicators. It is therefore vital to determine the level of cancerization. The diagnosis of liver cancer can be made using a variety of techniques, such as ultrasound, nuclear magnetic resonance imaging, and pathologic biopsy. The diagnosis of liver cancer from a pathology biopsy is the one that can be the most precise among them. Rendering to six principles [2], including nuclear size, nucleocytoplasmic ratio, nuclear irregularity, hyperchromatic, anisonucleosis, and nuclear texture, pathologists classify cancerization in the liver biopsy and categories it into five grades (ranging from 0 to 4). However, because it is non-quantitative and subjective, pathologists will always have different interpretations of the uncommon instances. Consequently, having an automated grading system that serves as a diagnostic guide for doctors of pathology. The segmentation of the nucleus from biopsy pictures is the most crucial component of the automatic grading method. The automatic grading system’s accuracy will be impacted by the segmentation results. Deep-learning-based computer vision algorithms have been widely used in computeraided diagnosis (CAD) over the past several years. Pathological image analysis using deep learning has shown to be effective in computational pathology. in raising the effectiveness and precision of cancer detection [3]. The structure of the Pathologists employ nuclei as the primary component in the detection and progression of cancer. prognoses, like forecasting survival [4] and categorizing tumours based on their pathology [5]. Accurate the quality of tissue segmentation can be improved by classifying and segmenting nuclei [6]. The first and most important stage in collecting the morphological features employed is nuclei segmentation. in the analysis that comes after. However, the structural diversity of

For the Nuclei Segmentation of Liver Cancer Histopathology Images …

265

nuclei makes it difficult to studies are difficult. The karyomorphism demonstrates variation, whereas many disorders may cause variable-sized chromatin abnormalities. The segmentation of nuclei, a crucial step in the processing of histological images, is the subject of this research. The goal of nuclei segregation is to collect precise data on each nucleus in addition to counting the number of nuclei. In contrast to nuclei detection, the outputs in this case include the contour of each nucleus rather than only the location of its center points [7].

2

Related Work

Numerous studies for examining images of cell segments or images of sick images consume remained planned throughout the past limited ages. The author [8] suggested an automatic nuclear segmentation approach founded arranged trilby-top morphological tophat by reconstructing methods for the classification of neuroblastoma. Sun et al. [9] suggests a method to properly resolve the nucleus and cytoplasm based on cell visibility. The image processing method [10] proposes is based on a double-comb microscope in a low-scan focusing stage. The quantitative-phase imaging technology developed by Kelvin for study of ultra-large-scale single cells [11] is interferometry-free, great-determination, and high-quantity. Hepatocellular carcinoma (HCC) in biopsy images can be mechanically classified expending a Support Vector Machine (SVM)-based system, according to [12]. Roy et al. [13] suggested a strategy based on watershed transformation. Using homotopy rectification, pictures can be marked and sliced in many fields. The author [14] presented a method that automatically determines the edges of the cytoplasm and nucleus, ensuring a thorough edge detection process and having the ability to simultaneously identify the margins of both structures. Its obvious drawback is that it can only be used if all the nuclei in the photos have consistent intensity differences from the background. Clustering, which includes K-mean clusting [14], In paper [15], and other methods, is a common method for detecting nuclei. The properties of the nuclei have also been used to propose a few filtering-based methods [16]. All of the aforementioned techniques share one flaw in common: they are typically very sensitive to manually adjusted settings and only work well for one or a small number of very particular types of nuclei or pictures. They hardly ever manage to create a single model or methodology that is suited for all of these different images since the looks of nuclei are so varied. Supervised Learning-based strategies are drawing increasing amounts of interest. They divide each pixel into one of two groups: background or nuclei. The next step would be to divide the contacting and overlapped nuclei regions after the nuclei detection stage produces the nuclei area. Methods like elliptical fitting and bottleneck detection [17] could be used to accomplish this. If a nucleus seed is created, its contour may be determined using region growing [17] or marker controlled watershed [18]. We suggest using the FCN network to tackle the nuclei segmentation problem in a manner that is inspired by the U-net algorithm [19]. To acquire

266

Arifullah et al.

the final nuclei boundaries using current deep learning approaches for nuclei segmentation, extensive post-processing is frequently required. In order to distinguish the nuclei from the background. To create the boundaries of each nucleus, author [20] presented a complex shape deformation technique in order to estimate the nucleus and its border from the image, Lal et al. [21] created a CNN3 model. However, a lengthy post-processing step is required. Here, they developed a complete architecture for fully convolutional neural networks. for segmenting nuclei. Our nuclei-boundary segmentation approach predicts the nuclei and their outlines simultaneously, in contrast to earlier binary class which only distinguish nuclei against the backdrop. Our technique accurately predicts the nucleus and boundary, making it possible to produce the final segmentation through a quick and easy post-processing step. A pixel-wise segmentation approach is required to segment the entire slide image. However, because to a lack of contextual information, the border area of each patch cannot be anticipated with accuracy. To solve this issue, a seamless patch extraction and assembly method is suggested [22]. In the evaluation and treatment of numerous disorders like Non-Alcoholic Fatty Liver Disease (NAFLD), steatosis, and cirrhosis, WSI knowledge and ML take realized fruitful submissions in histological examination of hepatic tissue. along with hepatocellular carcinoma. The application of AI has also attracted interest can help liver transplant surgeries [23]. Neural networks come in a variety of forms, and each has a particular application. The convolutional neural network (CNN) is the most well-known and often utilized neural network.13 A CNN is a classic that performs well when handling data with a grid pattern, such as digitized slides, and that can independently extract patterns and learn spatial hierarchies of characteristics.24 The three types of layers that make up a CNN are typically convolution, pooling, and a fully linked layer. Features then patterns can be extracted from the input using convolution and pooling. The retrieved characteristics are then converted into an output by the fully connected layer. Since CNNs are independent, no separate extraction of manually created features from the image is necessary. CNN’s layers are not transparent [24].

3

Deep Learning Techniques for Classification and Segmentation of Histopathological Images

The variety in tissue forms and textures makes it difficult to create a defined then effective system on behalf of mutually ordering and division of tissue images. In order to adapt to changes in tissue morphology, algorithms must learn to generalize on the tissue heterogeneity [25]. Trains two convolutional networks, one for border detection and the other to recognize the nucleus blob. The CNNs are composed of an encoder-decoder design that extracts nuclei pixels and nuclei periphery pixels to produce, respectively, a nuclei blob and a border mask. By removing nuclei borders and separating clumped nuclei, the segmentation process is carried out. They employ the ResNet-32 deep neural network training protocol besides the random forest regression archetypal aimed at organization. Authors in

For the Nuclei Segmentation of Liver Cancer Histopathology Images …

267

[26] take suggested a network for nuclear categorization and segmentation. Now, nuclear occurrences stand simultaneously segmented after the nuclear pixels are first discovered using a post-processing technique. The relevant nuclear type is also derived from the segmented nuclear instances. Maps of horizontal and vertical distance provide the foundation of the entire network.

4

Description of the Dataset

The PAIP 2019 competition’s liver cancer WSI dataset was used [27]. Tumours exhibiting peritumoral reaction and tumours with little to no peritumoral or intratumoral reactivity were the two dataset levels provided to challenge participants. Seoul National University Deep Learning for Joint Classification and Segmentation of Histopathology Image 905 Hospital, Korea gave the data and its segmented component.1. 50 WSIs were included in the training data. The typical size of a photo is 50,000 × 50,000 pixels. The ratios of 0.6, 0.2, and 0.2 were used to divide the histopathological pictures into training, validation, and testing datasets, respectively. The histology images were converted into colored patches since they were too large to send straight into the convolutional neural networks. pixels 224 × 224 the size. The white background’s blemishes would be removed. The entire slide photos (WSI) were triple scaled before classification. Four times, sixteen times, and sixtyfour times, respectively, the first three steps were taken. In an average 4 times scaled WSI, the positive and negative classes would each include 50,000 images. The augmentation process lessened the dataset’s class imbalance. Rotation, flipping, blurring, scaling, and shifting were used as augmentation techniques. The average number of images for each positive and negative class in a 16 times scaled WSI is 20,000, whereas this number is only 10,000 in a 64 times scaled WSI. The segmentation stage did not successfully process the histopathological pictures. climb them. Only patches sized 224 × 224 pixels in size each were retrieved, as opposed to the categorization process [28].

4.1

Pre-Processing

Since the full transparency images (WSIs) remain in addition big to be handled in convolution networks all at once, tiny patches must be eliminated from them. The full slide image must also be divided into two sections: the tissue area and the background glass area. To do this, we first extract patches that don’t overlap using the patchily Python package [29]. Following patch extraction, we choose the appropriate threshold for each patch and eliminate any with a white backdrop. This white background displays the area of the background glass. It is possible to prevent additional computing work by separating them from the tissue region. This process is done each period the WSIs remain scrabbled

268

Arifullah et al.

Fig. 1 Shows the following: edge discovery (a), basis image (b), objective image (c), colour standardized image (d), colour standardized scan for taking ‘r’ space material (e), and ‘r’ space evidence afterward by means of non-linear filter (f). Edges can have noticed

on behalf of cataloguing jobs. The tissue-containing patches are the last. to carry out segmentation and classification tasks, are gathered and put into the network [30].

4.2

Segmentation

The suggested segmentation network is shown hip Character 1. These segmentation system draws inspiration since the UNet++ [31] system. In this thoroughly trained encoder-decoder system, the encoder-decoder substitute-systems are connected by a number of nested condensed skip links. Instead of using UNet++ as a feature extractor in the encoding step, the author chose EfficientNet-B0 [32]. Each node in Fig. 1 takes characteristics from the node before it by way of the up-sampled output of the inferior impenetrable block as involvements. These are combined and convolved after that. Each bump furthermore obtains multiscale topographies at various scales to merge several piece charts. We also double the scope of the piece plot for each equal. At order to match [33], transposed convolution was used at the decoding stage.

4.3

Classification

In order to classify the data, we have contrasted the findings on too many classification schemes. We started out by using EfficientNet-B0 [34]. This network is already familiar with We refine the last few layers of the ImageNet dataset. To date, employed optimization technique of stochastic gradient descent [35] method. 64-batch training is used to

For the Nuclei Segmentation of Liver Cancer Histopathology Images …

269

train the network, and the learning rate is set at 0.0001 and increased by 0.1 every time. when the patience threshold is crossed, at 8. Additionally, we used weight momentum is set at 0.0001 and decay to 0.0001. 0.9. ResNet-50 was the second classification model we employed. [36]. It has already received ImageNet training. Using Adam 64-batch optimizer [37] with optimization mode. The rate of knowledge starts between 0.000001 and When a measure no longer rises, it is lowered by a 0.1 factor. The waiting period is 25 days, with the 200th period has passed. Every patch aims to divide photos into two categories. modules created on a system of common vote scheduled hers anticipated labels for patch. Let P, a be the patch that was taken from the entirety of the slide I and L stand for the patch’s class label. P in which l = [0,1] [38].

5

Proposed Approach

Nine photographs were used for testing, nine images for validation, and 29 complete slide shots were used as the training set. Each whole slide image and its corresponding mask are used to produce patches. Using the cover by way of a guide, we remove the white background region of the WSIs during training. In order to do this, we detached reinforcements from the WSIs, checked the color threshold against the corresponding mask, and removed the patch if the color threshold was surpassed. Inference-based inference is used by the network to first distinguish the patches from the complete transparency descriptions. The collected reinforcements are referred to the system so it can create the segmentation maps. Figure 2 depicts the results of combining these distinct maps. Since then, patches have been applied and removed. As a result, some noises can be heard. We can see that the patches must include generic features in order for the patch-based technique to function equally well. Convolutional networks, which can process an entire image in a single forward pass, may be preferred if this is not possible. A Jaccard index of 0.68 and a Dice score of 0.69 were obtained. Figure 2 shows how the performance outcome turned out. The boundary has some noise and consistency difficulties, but the projected image is more accurate than the actual image.

6

Results

Nine photographs were used for testing, nine images for validation, and 29 complete slide shots were used as the training set. Each whole slide image and its corresponding mask are used to produce patches. Using the mask as a guide, we remove the white background region of the WSIs during training. In order to do this, we removed patches from the WSIs, checked the color threshold against the corresponding mask, and removed the patch if the color threshold was surpassed. Inference-based inference is used by the network to first distinguish the patches from the complete slide images. The collected

270

Arifullah et al.

Fig. 2 Proposed approach model

patches are sent to the network so it can create the segmentation maps. The result of combining these distinct maps is depicted in Fig. 3. Since then, patches have been applied and removed. As a result, particular noises can be heard. We can see that the patches must include generic features in order for the patch-based technique to function equally well. Convolutional networks, which can process an entire image in a single forward pass, may be preferred if this is not possible. A Jaccard index of 0.68 and a Dice score of 0.69 were obtained. Figure 3 shows the performance outcome. The boundary does have some noise and consistency difficulties, however the projected image is more accurate than the actual image. Table 1 shows that both models perform better on photos that have been scaled by four. And it appears that as the scale increases, learning accuracy eventually declines. At all scales, EfficientNet-B0 outperforms ResNet-50 among the models. With continual convolutional and max-pooling layers, ResNet-50 has 50 layers in total. The additional skip connection offers an additional path for the gradient and aids in remembering earlier information. EfficientNet-B0 makes clever use of depth, width, and resolution scaling in contrast to ResNet-50. As seen in Table 1, this enhances convergence, training duration, and accuracy (Fig. 4).

For the Nuclei Segmentation of Liver Cancer Histopathology Images …

271

Fig. 3 Show Various data pre-processing methods used to process the training data are shown in A RGB directed sub-spitting image, a CD preprocessing sub-image, and a CDACS preprocessing sub-image are instances of sub-images

Table 1 Shows the ResNet and efficient net models’ learning accuracy

Scale ratio

ResNet-50 (%)

EfficientNet-B0 (%)

8

90

89

32

95

96

64

89

93

Result 120 100 80 60 40 20 0 8

32 EfficientNet-B0

Fig. 4 Show overall result

64 EfficientNet-B02

272

7

Arifullah et al.

Conclusion

In this study, the subdivision and organization of liver cancer in histological pictures were both carried out. We used patch-based methods and achieved good results for both segmentation and classification tasks. For the categorization job, we scrambled the pictures three times and used the majority score to determine the winner. The provided method demonstrates how the model can accurately distinguish between liver histopathology images that are cancerous or non-cancerous. On behalf of the segmentation challenge, I rummage-sale an adapted form of the outdated U-Net, and it shaped countless outcomes. My method decreases the essential for a huge exercise quantity that has been expertly annotated by pathologists. With the augmentation technique, both the training dataset and the network’s regularization have grown.

References 1. Xing, F., Xie, Y., & Yang, L. (2015). An automatic learning-based framework for robust nucleus segmentation. IEEE Transactions on Medical Imaging, 35(2), 550–566. 2. Xie, L., Qi, J., Pan, L., & Wali, S. (2020). Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images. Neurocomputing, 376, 166–179. 3. Wong, I. H., Dennis Lo, Y. M., Zhang, J., Liew, C. T., Ng, M. H., Wong, N., & Johnson, P. J. (1999). Detection of aberrant p16 methylation in the plasma and serum of liver cancer patients. Cancer Research, 59(1), 71–73. 4. Wang, J., Liu, X., Wu, H., Ni, P., Gu, Z., Qiao, Y., & Fan, Q. (2010). CREB up-regulates long non-coding RNA, HULC expression through interaction with microRNA-372 in liver cancer. Nucleic Acids Research, 38(16), 5366–5383. 5. Ullah, A., Salam, A., El-Raoui, H., Sebai, D., & Rafie, M. (2022). Towards more accurate iris recognition system by using hybrid approach for feature extraction along with classifier. International Journal of Reconfigurable and Embedded Systems (IJRES), 11(1), 59–70. 6. Ullah, A., Khan, S. A., Alam, T., Luma-Osmani, S., & Sadie, M. (2022). Heart disease classification using various heuristic algorithms. International Journal of Advanced and Applied Sciences, 2252(8814), 8814. 7. Ullah, A., Dinler, Ö. B., & Sahin, ¸ C. B. (2021). The effect of technology and service on learning systems during the COVID-19 pandemic. Avrupa Bilim ve Teknoloji Dergisi, 28, 106–114. 8. Ullah, A., & Nawi, N. M. (2021). An improved in tasks allocation system for virtual machines in cloud computing using HBAC algorithm. Journal of Ambient Intelligence and Humanized Computing, 1–14. 9. Sun, C., Xu, A., Liu, D., Xiong, Z., Zhao, F., & Ding, W. (2019). Deep learning-based classification of liver cancer histopathology images using only global labels. IEEE Journal of Biomedical and Health Informatics, 24(6), 1643–1651. 10. Sigirci, I. O., Albayrak, A., & Bilgin, G. (2022). Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features. Multimedia Tools and Applications, 1–24.

For the Nuclei Segmentation of Liver Cancer Histopathology Images …

273

11. Shi, H. Y., Lee, K. T., Lee, H. H., Ho, W. H., Sun, D. P., Wang, J. J., & Chiu, C. C. (2012). Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS ONE, 7(4), e35781. 12. Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2021). The impact of pre-and postimage processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine, 128, 104129. 13. Roy, S., Das, D., Lal, S., & Kini, J. (2023). Novel edge detection method for nuclei segmentation of liver cancer histopathology images. Journal of Ambient Intelligence and Humanized Computing, 14(1), 479–496. 14. Rong, R., Sheng, H., Jin, K. W., Wu, F., Luo, D., Wen, Z., & Xiao, G. (2023). A deep learning approach for histology-based nucleus segmentation and tumor microenvironment characterization. Modern Pathology, 36(8), 100196. 15. Riasatian, A., Rasoolijaberi, M., Babaei, M., & Tizhoosh, H. R. (2020). A comparative study of U-net topologies for background removal in histopathology images. In: 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. 16. Ouhame, S., & Hadi, Y. (2020). A hybrid grey wolf optimizer and artificial bee colony algorithm used for improvement in resource allocation system for cloud technology. International Journal of Online & Biomedical Engineering, 16(14). 17. Muñoz-Aguirre, M., Ntasis, V. F., Rojas, S., & Guigó, R. (2020). PyHIST: A histological image segmentation tool. PLoS Computational Biology, 16(10), e1008349. 18. Mahmood, F., Borders, D., Chen, R. J., McKay, G. N., Salimian, K. J., Baras, A., & Durr, N. J. (2019). Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Transactions on Medical Imaging, 39(11), 3257–3267. 19. Loodaricheh, M. A., Karimi, N., & Samavi, S. (2021). Nuclei segmentation in histopathology images using deep learning with local and global views. arXiv preprint arXiv:2112.03998 20. Li, X., Pi, J., Lou, M., Qi, Y., Li, S., Meng, J., & Ma, Y. (2023). Multi-level feature fusion network for nuclei segmentation in digital histopathological images. The Visual Computer, 39(4), 1307–1322. 21. Lal, S., Das, D., Alabhya, K., Kanfade, A., Kumar, A., & Kini, J. (2021). NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Computers in Biology and Medicine, 128, 104075. 22. Kim, B., Yoo, Y., Rhee, C. E., & Kim, J. (2022). Beyond semantic to instance segmentation: Weakly-supervised instance segmentation via semantic knowledge transfer and self-refinement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4278–4287). 23. Jimenez-del-Toro, O., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rousson, M., & Atzori, M. (2017). Analysis of histopathology images: From traditional machine learning to deep learning. In Biomedical texture analysis (pp. 281–314). Academic Press. 24. Jaisakthi, S. M., Desingu, K., Mirunalini, P., Pavya, S., & Priyadharshini, N. (2023). A deep learning approach for nucleus segmentation and tumor classification from lung histopathological images. Network Modeling Analysis in Health Informatics and Bioinformatics, 12(1), 22. 25. Huang, P. W., Ouyang, H., Hsu, B. Y., Chang, Y. R., Lin, Y. C., Chen, Y. A., & Pai, T. W. (2023). Deep-learning based breast cancer detection for cross-staining histopathology images. Heliyon, 9(2). 26. Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018). Deep learning for imagebased cancer detection and diagnosis—A survey. Pattern Recognition, 83, 134–149.

274

Arifullah et al.

27. He, W., Liu, T., Han, Y., Ming, W., Du, J., Liu, Y., & Cao, C. (2022). A review: The detection of cancer cells in histopathology based on machine vision. Computers in Biology and Medicine, 146, 105636. 28. Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019). Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical Image Analysis, 58, 101563. 29. Elazab, N., Soliman, H., El-Sappagh, S., Islam, S. R., & Elmogy, M. (2020). Objective diagnosis for histopathological images based on machine learning techniques: Classical approaches and new trends. Mathematics, 8(11), 1863. 30. dos Santos Silva, T. D., Bomfim, L. M., da Cruz Rodrigues, A. C. B., Dias, R. B., Sales, C. B. S., Rocha, C. A. G., & Militão, G. C. G. (2017). Anti-liver cancer activity in vitro and in vivo induced by 2-pyridyl 2, 3-thiazole derivatives. Toxicology and applied pharmacology, 329, 212– 223. 31. Cui, Y., Zhang, G., Liu, Z., Xiong, Z., & Hu, J. (2019). A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Medical & Biological Engineering & Computing, 57, 2027–2043. 32. Chanchal, A. K., Kumar, A., Lal, S., & Kini, J. (2021). Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images. Computers & Electrical Engineering, 92, 107177. 33. Basu, A., Senapati, P., Deb, M., Rai, R., & Dhal, K. G. (2023). A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems, 1–46. 34. Baseer, S., & Umar, S. (2016). Role of cooperation in energy minimization in visual sensor network. In 2016 sixth international conference on innovative computing technology (INTECH) (pp. 447–452). IEEE. 35. Aznaoui, H., Raghay, S., & Khan, M. H. (2021). Energy efficient strategy for WSN technology using modified HGAF technique. iJOE, 17(06), 5. 36. Alam, T., Gupta, R., Qamar, S., & Ullah, A. (2022). Recent applications of artificial intelligence for sustainable development in smart cities. Recent innovations in artificial intelligence and smart applications (pp. 135–154). Springer International Publishing. 37. Ahmad, I., Xia, Y., Cui, H., & Islam, Z. U. (2023). DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions. Expert Systems with Applications, 213, 118945. 38. Aatresh, A. A., Yatgiri, R. P., Chanchal, A. K., Kumar, A., Ravi, A., Das, D., & Kini, J. (2021). Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images. Computerized Medical Imaging and Graphics, 93, 101975.

Applications of Artificial Intelligence in Recruitment and in Marketing

Metaverse for Job Search: Towards an AI-Based Virtual Recruiter in the Metaverse Era: A Systematic Literature Review Ghazouani Mohamed, Fandi Fatima Zahra, Chafiq Nadia, Elimadi Imane, Lakrad Hamza, Aziza Chakir, and Azzouazi Mohamed

Abstract

As part of our study, we conducted a systematic literature review on the use of the metaverse in job search and the emergence of a virtual recruiter in the metaverse era. Our review examined various recent articles, studies, and academic publications to gather in depth information on this rapidly expanding topic. We explored the potential benefits offered by the use of the metaverse in the recruitment process, such as creating an interactive virtual environment where job seekers can interact with virtual recruiters, engage in simulated interviews, and present their skills in an immersive manner. We also examined the challenges and limitations of this approach, including data privacy concerns, reliability of virtual assessments, and accessibility for all job seekers. Through this systematic review, we were able to highlight current trends, research gaps, and future opportunities in utilizing the metaverse to enhance the job search process. Keywords

Artificial intelligence • Metaverse • Virtual recruiter • Job seekers G. Mohamed (B) · F. F. Zahra · C. Nadia · E. Imane · L. Hamza · A. Mohamed Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, Morocco e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_15

277

278

1

G. Mohamed et al.

Introduction

Innovative solutions have emerged recently in a variety of fields as a result of the fusion of cutting-edge technology like artificial intelligence, virtual reality, and the metaverse. The idea of transforming the traditional job search and recruitment process has generated a lot of interest. In order to analyze the consequences, difficulties, and promise of incorporating the metaverse into the world of job searching and recruitment, this systematic literature review sets out on a thorough tour of this fascinating landscape. The immersive digital environment and interactive characteristics of the metaverse have made it possible to rethink established methods. This review aims to provide a thorough knowledge of how the metaverse is influencing both job searchers and employers by a careful analysis of recent scholarly contributions, research investigations, and expert viewpoints. Our goal is to contribute to the continuing conversation about the metaverse’s impact on the future of job search and the crucial role that virtual recruiters play in this dynamic environment by putting light on the various aspects of this evolving paradigm. The emergence of the metaverse has highlighted how the job search process has transformed into a digital and immersive experience. This change has spawned brand-new options that go beyond the limitations of conventional platforms. Our project explores the heart of this paradigm shift inside this framework, utilizing cutting-edge tech nologies including 3D avatar modeling, artificial intelligence, and vir tual reality development. The end product is an engaging and interactive platform that transforms how job seekers and employers communicate throughout the hiring process. The rise of the metaverse has also significantly altered the conventional approaches to job searching. By supplying an immersive digital environment where job seekers may easily communicate with virtual recruiters, this revolutionary technology offers a unique remedy. It enables applicants to participate in mock interviews, highlight their qualifications, and exhibit their potential in an interesting and dynamic way. New opportunities for influencing the recruitment environment of the future are revealed as firms increasingly understand the possibilities of this revolutionary landscape. The research studies, methodology, and findings that study the incorporation of the metaverse into the job search and recruitment process are examined in detail in the following sections of this review. We want to help researchers, practitioners, and other stakeholders who are interested in improving the job search process in the metaverse era make informed decisions by presenting a thorough understanding of the implications, difficulties, and opportunities presented by this paradigm shift.

Metaverse for Job Search: Towards an AI-Based Virtual Recruiter …

2

279

Related Work

Through a comprehensive analysis of recent scholarly contributions, research studies, and expert opinions, this systematic literature review aims to offer a nuanced understanding of the metaverse’s impact on job seekers and employers alike. By scrutinizing articles from reputable academic databases including IEEE Xplore, ACM Digital Library, PubMed, and Google Scholar, the review identifies major trends, advances, and research gaps in the integration of the metaverse into the job search process. The selected keywords such as “metaverse,” “virtual recruiter,” “AI-based recruitment,” “job search,” and “virtual reality” were strategically employed to capture a comprehensive range of perspectives and insights. The synthesis of this literature serves as a foundation for exploring the potential benefits, challenges, and future prospects of utilizing the metaverse for job recruitment. The reviewed papers showcase the ingenuity of different authors in employing a variety of methodologies to enhance the job recruitment process through the integration of the metaverse. “NLP-Based Bi-Directional Recommendation System,” authored by Suleiman Ali Alsaif and al, utilizes NLP-based resume matching, collaborative recommendation enhancement, and job matching and evaluation using BERT and CNN text analysis. These methods collectively enhance the accuracy of matching resumes and job offers, providing a more effective recruitment process [1]. In “Sentiment Classification and Prediction of Job Interview Performance,” by Sarah S. Alduayj and al, the incorporation of text preprocessing, traditional ML models, and neural networks for sentiment analysis leads to the identification of correlations between sentiment and interview performance[2]. Joko Siswanto’s and al “Interview Bot Development with Natural Language Processing and Machine Learning” utilizes techniques from the fields of Natural Language Processing (NLP) and Machine Learn ing (ML) to create interview bots. The technique demonstrates varying levels of accuracy and coverage rates [3]. Jitendra Purohit’s and al “Natural Language Processing based JARO - The Interviewing Chat-bot” combines automatic question generation, sentiment and result classification, and NLP frameworks to improve interview efficiency and identify emotional trends [4]. The study conducted by Iulia Stanica and al titled “VR Job Interview Simulator” utilizes virtual reality simulation, chatbot technology, and emotion recognition mechanisms to generate authentic interview scenarios. The proposed methodology provides a highly engaging and interactive experience [5]. Finally, Raj Pandey’s and al, “Interview Bot with Automatic Question Generation and Answer Evaluation” employs various NLP techniques for question generation, answer evaluation, and CV summarization. This method showcases the potential of automating interviews, and these diverse methodologies collectively contribute to shaping the future of job recruitment within the metaverse, offering innovative solutions [6]. Accuracy of matching resumes and job offers, providing a more effective recruitment process [1]. In “Sentiment Classification and Prediction of Job Interview Performance,”

280

G. Mohamed et al.

by Sarah S. Alduayj and al, the incorporation of text preprocessing, traditional ML models, and neural networks for sentiment analysis leads to the identification of correlations between sentiment and interview performance [2]. Joko Siswanto’s and al “Interview Bot Development with Natural Language Processing and Machine Learning” utilizes techniques from the fields of Natural Language Processing (NLP) and Machine Learn ing (ML) to create interview bots. The technique demonstrates varying levels of accuracy and coverage rates [3]. Jitendra Purohit’s and al “Natural Language Processing based JARO The Interviewing Chat-bot” combines automatic question generation, sentiment and result classification, and NLP frameworks to improve interview efficiency and identify emotional trends [4]. The study conducted by Iulia Stanica and al titled “VR Job Interview Simulator” utilizes virtual reality simulation, chatbot technology, and emotion recognition mechanisms to generate authentic interview scenarios. The proposed methodology provides a highly engaging and interactive experience [5]. Finally, Raj Pandey’s and al, “Interview Bot with Automatic Question Generation and Answer Evaluation” employs various NLP techniques for question generation, answer evaluation, and CV summarization. This method showcases the potential of automating interviews, and these diverse methodologies collectively contribute to shaping the future of job recruitment within the metaverse, offering innovative solutions [6].

3

Critical Study

This section provides a critical analysis of the main methodology and conclusions presented in a range of research publications that explore the incorporation of the metaverse into the process of job searching and recruitment. The aforementioned articles encompass a wide array of inventive methodologies that strive to improve recruitment strategies by leveraging cutting-edge technologies. The utilized techniques include natural language processing (NLP), machine learning (ML), virtual reality (VR) simulations, sentiment analysis, and other related methodologies. Every each study offers distinct perspectives and resolutions to transform the job recruitment domain. It is important to acknowledge that although these strategies yield notable progress, they also encounter obstacles and constraints that must be acknowledged and resolved in order to guarantee their efficacy and applicability in real-world contexts (Table 1).

4

Proposed Approach

Our proposed strategy builds upon the existing research methodologies and expands the incorporation of the metaverse into the job search and recruitment process. By utilizing advanced technologies such as Natural Language Processing (NLP) and Machine

Metaverse for Job Search: Towards an AI-Based Virtual Recruiter …

281

Table 1 Overview of innovative methodologies in metaverse integrated job recruitment Paper name

Methods

NLP-Based Bi-Directional • NLP-based resume Recommendation System: matching:(CBF) Towards Recommending Jobs • Collaborative recommendation to Job Seekers and Resumes to enhancement:(CF) Recruiters • Job matching and evaluation:(BERT) • Text analysis for job recommendation: (CNN)

Inference • Effective in matching resumes and job offers • Enhances recommendation accuracy • Evaluated using metrics like precision, recall, F1-score, accuracy • Beneficial for text analysis

Sentiment Classification and Prediction of Job Interview Performance

• Text preprocessing (removing stop words, stemming, etc.) • Use of traditional machine learning models such as Naïve Bayes, SVM • Implementation of neural networks (LSTM and CNN) • Evaluation using metrics such as accuracy, recall, F1-score

• Sentiment analysis correlates with interview performance • Language cues have predictive potential for interview success • Language-based models may not capture all interview nuances • Holistic assessment is crucial; sentiment analysis is supplementary

Interview Bot Development with Natural Language Processing and Machine Learning

• NLP and Machine Learning

• Accuracy varies (48% to 79%) and coverage varies (37.3% to 98.1%) • Data variability impacts machine learning results

Natural Language Processing based JARO - The Interviewing Chatbot

• Automatic Question Generation: (Rule-based approach, NLP models (e.g., Seq2Seq) for text generation) • Sentiment Analysis; (Naïve Bayes for sentiment classification, Neural network models for sentiment analysis) • Result Classification: (Naïve Bayes for classification, SVM for classification) • Natural Language Processing (NLP frameworks like spaCy, NLTK, Named Entity Recognition (NER) for data extraction)

• Enhanced interview efficiency • Identification of emotional trends • Effective candidate performance • Improved report generation

(continued)

282

G. Mohamed et al.

Table 1 (continued) Paper name

Methods

Inference

VR Job Interview Simulator: Where Virtual Reality Meets Artificial Intelligence For Education

• Virtual Reality (VR) Simulation • Unity 3D Game Engine Development • Chatbot Integration • Pandorabots AIML Chatbot Creation and Taining • Facial Recognition Techniques • Emotion Detection from Text (Watson API) • Electrodermal Activity (EDA) Monitoring • Integration of VR Devices and Cameras • Utilization of APIs for Chatbot, Facial Recognition, Emotion Analysis, and Data Analysis

• Virtual reality (VR) creates realistic interview simulations • Use of chatbots for simulated interview interactions • Facial recognition assesses users’ emotional responses • Emotion analysis from text provides comprehensive feedback

Interview Bot with Automatic Question Generation and Answer Evaluation

• POS Tagging et NER (Named Entity Recognition) • Question Generation Dynamique • Évaluation des Réponses avec BERT et Cosine Similarity • Chatbot avec Interface Web • Analyse de Similarité (TFIDF, BERT, Cosine, Jaccard) • Classification Automatique des Résumés de CV • Réseau de Neurones Profonds (DNN) pour Proctoring • Détection de Visages et d’Objets en Proctoring • Méthodes d’IA pour Résumé de CV et Recommandations

• Virtual reality (VR) creates realistic interview simulations • Use of chatbots for simulated interview interactions • Facial recognition assesses users’ emotional responses • Emotion analysis from text provides comprehensive feedback

Metaverse for Job Search: Towards an AI-Based Virtual Recruiter …

283

Fig. 1 Interview Chatbot

Learning (ML), our methodology endeavors to deliver an improved virtual recruitment adventure. The utilization of Unity 3D Game Engine is employed to create immersive virtual settings, while Blender is utilized for the purpose of 3D avatar modeling. In order to produce a virtual recruiter that is both dynamic and responsive, we propose employing the LangChain framework for the construction and advancement of chatbots. This technique not only optimizes interactions but also enables immediate involvement and support for candidates (Fig. 1).

5

Limitation

The research that have been analyzed reveal a wide range of novel techniques that are designed to improve the recruitment process by incorporating virtual reality and artificial intelligence. Nevertheless, it is important to acknowledge that each of these approaches presents its own set of obstacles and limitations that require further investigation. The study titled “NLP-Based Bi-Directional Recommendation System” addresses the complexities involved in matching CVs to job offers, specifically focusing on the challenges arising from the overlap of comparable job profiles and data quality concerns. The aforementioned restrictions highlight the necessity of having exact and comprehensive datasets, along with advanced matching algo rithms, in order to provide correct recommendations [1]. The study titled “Sentiment Classification and Prediction of Job Interview Performance” underscores the efficacy of sentiment analysis in forecasting the outcome of job interviews. Nevertheless, it is important to acknowledge the presence of potential

284

G. Mohamed et al.

biases and the reliance on self-reported data in sentiment analysis. This serves as a crucial reminder that a comprehensive understanding can only be achieved by supplementing sentiment analysis with a holistic assessment [2]. The approach of developing a “Interview Bot Development with Natural Language Processing and Machine Learning” shows potential for automation. However, it is important to note that there are variations in accuracy and coverage, and limitations related to data accessibility. These factors emphasize the necessity for improved algorithm selection and increased availability of diverse datasets [3]. The technique known as “Natural Language Processing based JARO The Interviewing Chatbot” demonstrates that chatbots have the potential to improve interview efficiency. However, it is important to note that these chatbots are susceptible to unanticipated responses and their effectiveness is contingent upon the quality of the natural language processing models utilized [4]. The “VR Job Interview Simulator: Where Virtual Reality Meets Artificial Intelligence For Education” encounters various problems pertaining to the intricacy of scenarios, the quality of capturing devices, and the hardware specifications [5]. The approach titled “Interview Bot with Automatic Question Generation and Answer Evaluation” ultimately showcases the possibilities of interview automation. However, it also highlights the importance of effectively handling issues such as data volume, data quality, and potential biases [6]. In conclusion, the aforementioned research present promising prospects, although their inherent difficulties underscore the necessity for inventive resolutions and a well rounded strategy to effectively exploit the capabilities of virtual reality and artificial intelligence in the realm of recruitment. By comprehending and effectively mitigating these constraints, we may pave the way for a future in which these technologies genuinely transform the processes of recruitment and being recruited.

6

Conclusion and Future Work

In summary, this systematic review of literature sheds light on the intriguing convergence of advanced technologies, including artificial intelligence, virtual reality, and the metaverse, and their influence on the conventional job search and recruitment procedures. Upon conducting an extensive examination of diverse approaches and contemporary research discoveries, a comprehensive comprehension of the ramifications, obstacles, and prospects that emerge from the in tegration of the metaverse into this particular field is attained. The analysis of several methodologies, including NLP-based recommendation systems, AI-powered interview chatbots, and virtual reality simulations, highlights the significant impact that this convergence can have. The suggested methodology utilizes various approaches, including natural language processing (NLP), machine learning, the Unity 3D game engine, and the LangChain

Metaverse for Job Search: Towards an AI-Based Virtual Recruiter …

285

framework, to develop an immersive virtual recruitment experience. Nevertheless, our research does not terminate at this point. In subsequent stages, following the suc cessful development and thorough testing of the chatbot, our inten tion is to progress towards leveraging our proprietary dataset for the purpose of deploying the chatbot in real-world scenarios. This crucial stage will enable us to implement the knowledge acquired from the literature research and preceding developmental phases. Through the utilization of authentic data, we are able to consistently improve and augment our solution, taking into account the intricacies of human interactions and modifying the model to suit the many recruitment scenarios. By striking a balance between innovation and ethical and equitable considerations, this emergent development possesses the capacity to fundamentally transform the process by which individuals seek work possibilities and employers identify skills within a dynamic and immersive digital environment. This strategy exemplifies the importance of continuous study, empirical testing, and cooperation among scholars, professionals, and individuals with vested interests in order to influence the trajectory of recruitment practices in the metaverse.

References 1. Alsaif, S. A., Sassi Hidri, M., & Ferjani, I., et al. (2022). NLP-based bi-directional recommendation system: Towards recommending jobs to job seekers and resumes to recruiters. Big Data and Cognitive Computing, 6(4), 147. 2. Alduayj, S. S., & Smith, P. (2019). Sentiment classification and prediction of job interview performance. In: 2019 2nd international conference on computer applications & information security (ICCAIS) (pp. 1–6). IEEE. 3. Siswanto, J., Suakanto, S., & Andriani, M., et al. (2022). Interview bot development with natural language processing and machine learning. International Journal of Technology, 13(2), 274– 285. 4. Purohit, J., Bagwe, A., & Mehta, R., et al. (2019). Natural language processing based jaro-the interviewing chatbot. In 2019 3rd international conference on computing methodologies and communication (ICCMC) (pp. 134–136). IEEE. 5. Stanica, I., Dascalu, M.-I., & Bodea, C. N., et al. (2018). VR job interview simulator: where virtual reality meets artificial intelligence for education. In 2018 Zooming innovation in consumer technologies conference (ZINC) (pp. 9–12). IEEE. 6. Pandey, R., Chaudhari, D., Bhawani, S., et al. (2023). Interview bot with automatic question generation and answer evaluation. In 2023 9th international conference on advanced computing and communication systems (ICACCS) (pp. 1279–1286). IEEE. 7. Boudjani, N., Colas, V., & Joubert, C., et al. (2023). AI Chatbot for job interview. In 2023 46th MIPRO ICT and electronics convention (MIPRO) (pp. 1155–1160). IEEE.

286

G. Mohamed et al.

8. Nair, P. C., et al. (2022). HR based Chatbot using deep neural network. In 2022 international conference on inventive computation technologies (ICICT) (pp. 130–139). IEEE. 9. Jailani, M. K., & Nurbatra, L. H. (2019). Virtual reality system for job interview application: A development research. Celtic: A Journal of Culture, English Language Teaching, Literature and Linguistics, 6(1), 31–50. 10. Naim, I., Tanveer, M. I., & Gildea, D., et al. (2016). Automated analysis and prediction of job interview performance. IEEE Transactions on Affective Computing, 9(2), 191–204.

Metaverse for the Recruitment Process: Towards an Intelligent Virtual Recruiter Nadia Chafiq, Imane Elimadi, and Mohamed Ghazouani

Abstract

This article examines the metaverse’s potential as a platform for an intelligent virtual recruiter, transforming the recruiting process and improving job searchers’ experiences. As a result, the use of the metaverse in the recruitment process raises a variety of considerations about its benefits and limitations. Our research has two objectives. First, we offer a model based on an intelligent virtual recruiter and the metaverse, displaying the relationship between candidate and employer and demonstrating the synergy between both dimensions to optimize the recruitment process. The study’s second goal is to investigate candidates’ perceptions of the usage of metaverse and artificial intelligence to match employment offers with demands: Is the employment of an intelligent virtual recruiter opening up new avenues for connecting prospects and companies? To that goal, a questionnaire was issued to 56 Ben M’Sick Faculty of Science graduates who were looking for work. We were able to investigate candidates’ perceptions of the utilization of an intelligent virtual recruiter by analyzing the data acquired. Keywords

Metaverse • Intelligent virtual recruiter • Artificial intelligence • Recruitment process Meta-recruitment • Candidate experience

•

N. Chafiq (B) · I. Elimadi · M. Ghazouani Information and Education Sciences and Technologies Laboratory (LASTIE), Observatory of Research in Didactics and University Pedagogy (ORDIPU)Laboratory “Information Technologies and Modeling (TIM)”, Faculty of Sciences Ben M’Sik, Hassan II University Morocco, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_16

287

288

1

N. Chafiq et al.

Introduction

In an era of accelerating change and various and diversified uncertainties regarding the qualities of the workforce, the steps involved in recruiting new employees are getting increasingly difficult, particularly in companies whose work organization is changing dramatically. Recruitment is an important activity in the life of any business. It is a long-term commitment for the organization that is sometimes overlooked. Companies’ quest for productivity and competitive advantage has made flexibility and reaction time important problems. Recruitment is a delicate activity that can affect a company’s destiny, as well as a costly one that necessitates a precise strategy. According to a survey conducted by ManPower, HR Voice, and Open Sourcing [1], a recruitment failure costs a company between e30,000 and e150,000. The topic revolves around employment difficulties. That is why the saying “Find the right person, at the right time, in the right place” (Holy-Dis) [2] is more relevant today than ever. For positions that could “normally” be relocated, with talents that could be validated remotely and executed online (copywriter, web designer, accountant, etc.), remote recruitment was already typical. Having access to the metaverse and virtual reality technology broadens global recruitment opportunities for positions that traditionally required physical meetings: nurse, security guard, pilot, and so on. Before the final candidate is transported to another place, recruitment can now be completed totally online. This tenfolds the pool of exploitable applicants, increasing the likelihood of hiring the best candidate on a worldwide scale. In addition, the metaverse is a virtual world where we may create avatars such as an intelligent virtual recruiter. In this universe, we would be able to engage in emotionally and sensory-rich recruitment circumstances, for example. The metaverse’s goal is to take our remote conversations a step further and create a genuine virtual social area. We frequently lament the lack of spontaneous interaction in telecommuting, those informal instances of touch near the coffee machine that remain impossible to digital. As a result of the entrance of the meta-verse, the recruitment process has been drastically transformed, giving rise to what is now known as “meta-recruitment.” We provide a model based on the employment of an intelligent virtual recruiter to demonstrate the procedures required in meta-recruitment. However, as the Metaverse and its present components bring recruitment tools into question, new questions arise, notably regarding how the use of an intelligent virtual recruiter might optimize the recruitment process. As a result, the use of the metaverse raises a number of concerns concerning its benefits and limitations in the recruiting process: can the employment of an intelligent virtual recruiter open up new avenues for linking prospects and the company? We used empirical analysis to answer this study question. A questionnaire on the use of an intelligent virtual recruiter was issued to 56 graduates of Hassan II University’s Ben M’Sick Faculty of Science in Casablanca who were looking for work. We were able to investigate

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

289

candidates’ perceptions of the utilization of an intelligent virtual recruiter by analyzing the data acquired. We will provide the major findings of our study, noting limitations, to examine and discuss the results and bring our paper to a close.

2

Review of Literature

2.1

Meta-Recruitment Process

Since the COVID crisis, a slew of recruiting firms and companies have turned to digital techniques to provide a sense of (virtual) presence. This has enabled enterprises in demand to compensate for social distance and contribute to their business continuity plans. This approach is still in use today, and is mostly used for early conversations with prospects. This technology has been implemented into specific areas, like as recruitment, as a result of recent technical breakthroughs, including metaverse. This technology accelerates and streamlines the various steps of the recruitment process. The term “metaverse” originally appears in Neil Stephenson’s novel Snow Crash in 1992 [3]. Citizens use digital avatars to explore an online virtual environment. It allows them to escape from an anxiety-inducing world. The word metaverse rose to prominence with the Second Life game in the early 2000s. But nothing compares to the attention it’s gotten since Mark Zuckerberg [4] declared it the linchpin of the “Internet of the Future.” The term “metaverse” is a combination of the words “meta” (beyond) and “universe.” The word refers to a parallel, immersive, persistent, and shared digital cosmos that may be accessed via linked equipment (virtual reality headsets or goggles). Metaverse are collective virtual places, such as offices, that are constructed using a combination of virtual reality (VR) and augmented reality. One of the most important tools in these metaverses is, of course, the company’s famous avatar, which is equipped with artificial intelligence (AI) and may accept candidates. Part of the recruitment process will be managed by the presence of multiple conversational corporate avatars. Everyone would be able to create their avatar in sec onds and move freely about the metaverse to meet with firms or conduct interviews. The metaverse appears to be a future human resources tool, owing to its numerous applications in the recruitment process. Avatars provide absolute immersion in this fictitious realm. According to recruiters and metaverse advocates, immersive universes offer the advantage of encouraging inclusiveness and diversity through the usage of avatars. Companies including Samsung, Hyundai, Siemens, PwC, and Accenture have already included the metaverse into their hiring processes. Platforms like Microsoft Mesh and Meta’s Horizon Workrooms are making it easier to move HR procedures to the metaverse. The majority of these studies have been extremely successful in terms of pre-recruitment and quick access to applicants at no significant cost.

290

N. Chafiq et al.

Metaverse can provide fascinating opportunities for both recruiters and candidates in the context of recruitment. Metaverse are already attracting the attention of recruiters in specific industries (like as IT, which is looking for data analysts, data scientists, and programmers). In this environment, human resources departments must recognize that metaversity is more than just a technological and digital transformation issue; it is also a big employer brand issue. Recruiters no longer need to devote a significant portion of their time to certain duties. Recruiters, for example, spend an average of 30 s to 1 min reading a CV during the CV sorting step. Suppose that a company receives 1,500 CVs in response to a job posting on an employment portal. This means that recruiters would spend around 18 h selecting these CVs, which equates to 2–3 8-h work days (Castillo, 2017) [5]. The average duration of the selection step has been significantly decreased without altering due to the employment of artificial intelligence and metaverse. Meta-recruitment arose as a result of the introduction of the metaverse in the recruitment industry. This is a fake virtual environment in which a recruiter and candidate are immersed through the use of their avatar. The latter is a question-asking chatbot. It improves the candidate experience, while it automates low-value-added tasks in human resources, allowing recruiters to emphasis on their main mission: discovering talent. This chatbot can be augmented with a third-party service that specializes in organizing recruitment interviews, or by sourcing software that stands alone. The latter is a system that chooses candidates from a database on professional networks such as LinkedIn based on predetermined criteria. The algorithms used in this program are based on keyword filtering. The software will rate the profiles based on the expected needs by analyzing their educational background, professional experience, and talents in order to recommend the most compatible profiles. This method is very useful when a company is looking for the best applicant in a competitive industry. “Algorithms also take candidates’ personalities and company culture into account in order to predict the affinity between a potential candidate and his or her future employer” (Delfort, 2020) [6]. Furthermore, the meta-recruitment process is a collection of recruiting processes that culminate in the validation and integration of a new employee into a firm via metaverse. From one organization to the next, the processes in the meta-recruitment process are largely the same. Each organization, however, can create its own meta-recruitment strategy (see Fig. 1) [6]. The meta-recruitment process is often divided into five stages: Stage 1: Business Needs Analysis The first stage in meta-recruitment is a thorough examination of the company’s specific requirements. The employer evaluates the abilities, qualities, and attributes required for a certain role while keeping the business culture and objectives in mind. This preliminary stage establishes the groundwork for the hunt for the appropriate applicant.

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

291

Fig. 1 Meta-recruitment stages

Step 2: Conduct an Online Job Search Candidates begin their job search online by looking for jobs that match their talents and objectives. He investigates numerous digital platforms and channels in order to locate relevant positions that match his professional profile and career objectives. Step 3: The Intelligent Virtual Recruiter Distributes and Selects Candidates The Intelligent Virtual Recruiter goes into action once the applications are received. It targets the job posting, receives applications, and uses artificial intelligence algorithms to analyze each candidate. The virtual recruiter looks at skills, experience, aptitudes, and other factors to find the most qualified and acceptable candidate for the job. This automatic stage ensures that the selection process is ob- jective and fair. Step 4: Validation and Recruitment by the Employ The employer evaluates the profiles of the shortlisted candidates and verifies the final selection based on the advice of the intelligent virtual recruiter. Additional interviews and tests might be conducted to ensure the best cultural and professional fit. Once the candidate has been chosen, the company goes through the formal recruitment procedure and offers the employment. Step 5: Intelligent Virtual Recruiter Onboarding Training Following recruitment, the Intelligent Virtual Recruiter is critical in integrating the new employee into the organization. He or she creates and implements tailored onboarding training sessions to assist candidates in adjusting to their new role and the corporate culture. These engaging seminars walk new employees through internal processes, company values, and professional standards.

292

N. Chafiq et al.

The meta-recruitment scheme, as a result of the synergistic combination of metaverse and artificial intelligence, represents a substantial evolution in the traditional recruitment process. Meta-recruitment provides an innovative method for companies and applicants alike by optimizing every stage, from the initial business need to the effective integration of the new employee. This transformation reimagines how businesses identify, choose, and integrate future talent, while also increasing the efficiency and relevance of each stage of the recruitment process. B-Meta-recruitment is unique and inventive, and it is compatible with the new generation. Innovative recruitment is required since the younger generation does not respond to traditional recruitment methods, and businesses are failing to hire talent. Metaverse is a tool that is deeply embedded in our daily life. It is now also utilized in recruitment. Employers can not only demonstrate their company’s full potential, but also present it as an innovative, modern, and distinguishing structure by developing immersive and engaging experiences. This strategy is especially crucial for younger generations, who are more difficult to attract and keep. There are currently only a handful metaverse recruitment initiatives. Companies that are the first to offer this type of experience will gain points with applicants. This is especially true for young people, who are already acquainted with these virtual worlds as a result of their daily use of video games. “The use of metaverse could be a distinguishing and enticing argument for candidates in technology professions.” “A sort of innovation bonus,” Edouard Bliek [7] adds. Carrefour, for example, conducted its first metaverse recruitment effort in May 2022. Through a unique and novel experience, the goal is to promote the company’s technical fiber and attract uncommon profiles (data scientists and data analysts). As a result, Carrefour established a virtual campus and asked forty students from Polytechnique and Mines Télécom to register and participate in the recruitment process. According to Recruiting Brief [8], slightly less than 90% of job applicants said the quality of their candidate experience influenced their ultimate employment decision and their perception of a certain employer. This figure is emphasized by Generation Z, who are more sensitive to consideration and, as digital natives, more sensitive to the digital part of work. As a result, including the exciting chal- lenges and competition of virtual reality, as well as the entertaining and fascinating characteristics of augmented reality, will improve the candidate experience. However, while the metaverse can be used in the recruitment process, meeting in person is still necessary to determine whether the desire to collaborate is genuine. Metaverse cannot be the only point of contact with the candidate. To comprehend each other, you’ll need to mix and match. While metaverse do not replace the necessary human interaction, they do increase access to talent. They can be targeted more extensively since we can look beyond regional limits to find other candidate pools in the metaverse. Of course, this is

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

293

just the beginning of the recruitment process, and it should not totally replace traditional recruitment, but rather supple- ment and support. C-Situational Meta-Recruitment Based on an Immersive Candidate experienceWhile certain skills, such as precision, speed and rigor, are difficult to detect on a CV, the immersive universe enhances the value of these particular skills. Human resources professionals can receive candidates in the fictitious room of their choice. They will have prepared it in advance according to the message they wish to convey. Likewise, they can simulate multiple situations to understand, for example, the reactions of applicants in working conditions. In fact, the development of virtual tests enables them to put them to the test in a practical situation. Web 3.0 assists recruiters in refining their recruitment approach by adding a new dimension to the prospect experience. This will make it easy for them to find the perfect individual. According to Edouard Bliek of Stedy [9], “for this to truly serve a purpose, companies must ultra-contextualize metaverse.” They should not only be utilized for virtual interviews, but also to help applicants address practical problems by analyzing their actions in difficult settings. Metaverse will allow you to simulate several scenarios and examine the candidate’s reactions. You can envision virtual scenarios and develop scenarios in various industries to test and determine a candidate’s skills: Metaverse also provide corporations with the option to assess behavioral and social abilities in very realistic corporate environments. Recruiters can design virtual worlds that are personalized to their specific demands and recruitment criteria. The recruiter can then evaluate the candidates’ natural reactions. Candidates’ gestures and movements are more genuine, as opposed to a well-prepared interview or written test, which just reveals one aspect of their personality. As a result, recruiters now have more dependable and easier access to the information they seek. He can see how a candidate-driver reacts to a storm on the road, how a candidate-salesperson behaves in front of various sorts of customers, or how a candidate-teleworker-accountant concentrates while working without company supervision. As a result, there is a better likelihood of hiring the appropriate individual and a smaller margin for error. Virtual reality can be used in recruitment to create a hypothetical setting in which candidates must solve an issue and demonstrate their real-life talents and abilities. It could also be a game in which the candidate’s progress in the game demonstrates his or her soft talents. For example, Walmart [10], one of the largest retailers in the United States, has started utilizing virtual reality to recruit managers since 2019. It organizes virtual reality exams to detect each candidate’s reactions in a stressful circumstance—in a simulated environment.

294

3

N. Chafiq et al.

Methodology

The quantitative methodology was used for this study. The purpose of this poll was to assist us in identifying the needs of laureates in terms of the use of metaverse in the recruitment process. From June 9 to July 29, 2023, we performed a survey on the Google Forms platform (https://docs.google.com/forms/d/e/1FAIpQLSesLt7Io2a_SZe QLa_OwqKvqh-k_7sis40DzsvzcSnXmN7AAw/viewform?usp=sf_link). The survey was completed by 56 laureates in all. This survey was required to analyze laureates’ impressions of the usage of metaverse in the recruiting process, in order to investigate the present limits that recruiters will experience when applying this technology. The development and application of metaverse is a relatively new phenomenon. As a result, during this study, we targeted 56 graduates of Hassan II University’s Ben M’Sick Faculty of Sciences in Casablanca who were looking for work. Our goal was to reach out to those who are either looking for work or who have recently found work. It should be noted that our questionnaire lasts an average of 5 min and is divided into three sections: the first is about the participants’ profiles (A), the second is about the difficulties students have in finding job opportunities, and the third is about students’ perceptions of the use of metaverse.

3.1

Result

A General Description of the Sample The students were chosen at random and comprised 56 undergraduates (semester 6). The sample is depicted in the Fig. 2: B-Difficulties in Finding Job Offers Question. Have you encountered any difficulties in finding job offers after completing your studies? (Figs. 3 and 4). An examination of the responses to question 1 reveals that graduates with a Bachelor’s degree are in a variety of situations when it comes to finding work. 63.6% of respondents had not yet began their job hunt, which can be explained by a range of factors such as further education or other commitments. However, 23.6% of prize winners who have started their job hunt reported difficulty obtaining offers that match their goals and capabilities. This pattern shows that the job hunting process may be difficult for certain laureates. The perceptions of C-candidates on the usage of metaverse and artificial intelligence in the recruitment process. Question. What specific features or tools would you like to see in a metaverse/AI system to set up an efficient recruitment process? (Select the appropriate answers) (Fig. 5). An examination of the figure corresponding to question 2 demonstrates that students are particularly interested in certain functions of a metaverse system for recruiting. 78% of

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

295

Fig. 2 Sample (bachelor’s degree graduates)

Fig. 3 Difficulties in finding job offers

respondents, in particular, would want to have access to online training geared at strengthening their professional skills. Furthermore, 64% of respondents believe it is critical to be able to schedule virtual interviews with knowledgeable recruiters. These findings clearly show a demand for online training possibilities and novel ways to communicate with recruiters. When candidates are hired, the metaverse becomes a training tool. Users are placed in their prospective working environment, with tasks such as restocking or facing, cashiering, customer service. Companies who hire thousands

296

Fig. 4 Main reasons for difficulties in finding job offers

Fig. 5 Features to be integrated into a metaverse platform

N. Chafiq et al.

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

297

Fig. 6 Using an intelligent virtual recruiter

of workers each year are highly interested in the metaverse experience. They are welcomed into corporate discovery virtual worlds where they may immerse themselves in the company’s culture and values and interact with colleagues from all around the world. Question Do you think the use of an intelligent virtual recruiter offers new opportunities for connecting candidates and the company? (Fig. 6). According to the answers of question 3, 64% of students polled perceive the intelligent virtual recruiter as a possible source of new connections between candi- dates and organizations. This optimistic attitude indicates that the majority of students believe that this technology has the potential to improve and facilitate interactions during the recruitment process. In a virtual environment, the virtual recruiter may interpret and evaluate job seekers’ profiles, match them to appropriate employment openings, and conduct interactive interviews. Job applicants are thus immersed in an engaging recruitment process that allows them to effectively demonstrate their talents and qualifications. The virtual recruiter also eliminates geographical barriers. Indeed, recruitment will become more multinational, adaptable, and open. This mode of communication will go beyond videoconferencing, establishing a higher quality link and gamifying the relationship. Furthermore, the intelligent functions of the virtual recruiter improve the accuracy and efficiency of candidate selection, boosting the overall recruitment process for both job searchers and recruiters. Question What is your perception of the use of metaverse and artificial intelligence to match job offers and demands? (Fig. 7). In response to question 4, 58% of laureates feel that metaverse and artificial intelligence may significantly improve the matching of employment offers and ap- plications. However, a sizable number (32%) admit to knowing nothing about metaverse. This discovery

298

N. Chafiq et al.

Fig. 7 Winners’ perceptions of metaverse us

raises issues about information and awareness surrounding technological developments, emphasizing the need for greater knowledge on this subject to be disseminated. Question Would you be willing to take part in a recruitment process based on metaverse and artificial intelligence? (Fig. 8). According to the results of question 5, 57% of respondents are willing to participate in a recruitment procedure based on metaverses and artificial intelligence.

Fig. 8 Participation in a meta-recruitment process

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

299

This promising number indicates an openness to innovative recruitment tactics, which may affect how organizations approach the candidate selection process. Question. What do you see as the potential benefits of using metaverse and artificial intelligence in the field of employment? (Fig. 9). The majority of students expressed the view that the use of metaverse would enable a better match between candidates’ skills and available job offers. This perspective reinforces the potential of metaverse to facilitate a more precise match between candidates’ expectations and companies’ needs. Question What do you see as the potential challenges associated with the use of metaverse and artificial intelligence in the field of employment? (Fig. 10).

Fig. 9 The benefits of using meta-recruitment

Fig. 10 Potential challenges in using meta-recruitment

300

N. Chafiq et al.

53% of students believe that using meta-recruitment may result in a loss of the human aspect and the candidate-company relationship. However, it is worth noting that these students appear to be amenable to a strategy that combines the benefits of meta-recruitment with features of traditional recruitment. In summary, the results show that students are increasingly interested in the integration of meta-recruitment and artificial intelligence in the field of recruiting. These findings indicate a good potential for novel recruiting strategies, while emphasizing the significance of improving laureates’ awareness of impending technical innovations like as meta-recruitment.

4

Discussion

The findings of this study reveal a range of perspectives among students on the usage of metaverse in the recruitment process. Each kid has a unique perception and set of needs, demonstrating the complexities of attitudes toward this evolving technology. However, artificial intelligence and metaverse recruitment technologies can help prospects have a better job search experience. When seeking for a new job, for example, they will wait less time for a response to their application, allowing them to enter the job market sooner. Students express their desire to use metaverse for recruiting, but only under specified conditions. The necessity for sufficient training for recruiters to eliminate bias and discrimination in the process is a major worry that emerges. This concern highlights the necessity of implementing metaverse in recruiting in an ethical and transparent manner, where candidates have the right to be informed and safeguarded regarding the use of their personal data. When it comes to the future of metaverse in recruiting, the majority of students believe that, despite the in- corporation of technology, the human part of the recruitment process will be preserved. This goal emphasizes the importance of striking a balance between technical efficiency and the human, relationship dimension of recruitment. Integrating metaverse into recruitment thus necessitates an ethical and open strategy. Companies must tell candidates about the use of metaverse in their recruitment process, describing the methods required and ensuring the privacy of personal information. Although digital communication and avatars can be employed as a virtual depiction of a candidate in a recruitment process, they cannot totally replace physical presence and actual human connection. Indeed, face-to-face meetings might provide significant advantages during the recruitment process. A face-to-face meeting, for example, can assist employers in more effectively assessing candidates’ interpersonal abilities. In addition to their body language and nonverbal communication. Face-to-face encounters can also assist candidates gain a better understanding of the work environment, corporate culture, and team with which they may be working. Finally, metaverse provide a possibility to improve the efficiency of the hiring

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

301

process. The observed performance advantages with this technology highlight its potential. However, it is critical to understand that metaverse will not entirely replace traditional techniques of recruitment. Metaverse recruitment can be viewed as a hybrid solution that works in tandem with traditional procedures, with the final choice always remaining in the hands of human specialists. This research demonstrates the complexities of attitudes toward metaverse in recruiting. Laureates are receptive to the use of this technology, but they have ethical reservations and expectations regarding the preservation of the human part of the process. Integrating metaverse into recruiting successfully necessitates careful consideration of ethical issues, training of the actors engaged, and the pursuit of a balance between technical innovation and human engagement. Furthermore, not only is the candidate screening stage critical, but so is the onboarding of new employees, which is critical in guaranteeing their engagement from the start. The metaverse can provide new employees with an immersive, entertaining experience, making them feel more interested and involved in their integration. According to a Bersin by Deloitte industry study: Onboarding Software Solutions 2014: On-Ramp for Employee Success [11], 4% of new employees depart after only one day, and 22% of staff turnover happens during the first 45 days. Following on from recruitment, metaverse can be used for onboarding: by simulating realistic, and sometimes critical, scenarios, metaverse aid in the development of new recruits’ skills. Hyundai [12] has gone a step forward by providing its new hires with an integration activity in the form of an interactive onboarding tour. The metaverse can provide new employees with an immersive experience, making them feel more interested and involved in their integration. New workers, for example, can be welcomed into a virtual environment where they can learn about the company’s culture, beliefs, colleagues, and accessible resources. They can also communicate with avatars of their coworkers, which helps to break the ice and develop social bonds. Using metaverse to show the reality of one’s organization, on the other hand, increases employer attraction from the first meeting. The metaverse has the potential to transform onboarding as we know it. As candidates, we frequently desire some kind of immersion during the recruitment process. The metaverse provides far more opportunities for getting to know the firm, its culture, and promoting member conversations. This means that a faraway applicant can have a significantly more immersive virtual world experience than a videoconference. Aside from the metaverse craze, meta-recruitment (recruitment in the metaverse) offers further opportunities: – conduct interviews for selection – Establish a distinct and personalized first point of contact. – focus on the employer brand’s image, promoting it as innovative (especially to attract IT professionals).

302

N. Chafiq et al.

– Make the applicant experience more fun. Can hire people from all around the world without having to pay for pricey travel or lodging. While the metaverse opens up a limitless field of possibilities, it also comes with its own set of perils, which you should research before taking your initial steps. “There are new pathologies as soon as there is a new paradigm,” observes the founder of Metaverse College. We must create a legal framework to regulate the use of metaverse as soon as possible in order to limit the hazards related with cyberstalking, cybersecurity, and scams. From a psychological standpoint, continuously transitioning between one’s avatar and one’s true identity can cause psychological issues. Companies must consequently educate their personnel and share best practices, such as through a charter. Among the current challenges for recruiters are the following: The relatively high expense of using metavers for recruitment. While major firms can easily invest in virtual reality headsets (Oculus Rift, HTC Vive, and so on), procuring this cutting-edge technology can be more difficult for smaller organizations. According to the Global Web Index [13], the biggest reason for the non-adoption of metaverse in all industries is still cost. It necessitates high-speed Internet access (especially when video calls are already shaky…). Current telecommunications infrastructures are insufficient to host and provide a metaverse worthy of “Ready Player One” and available to all. The issue of personal data security. The issue of digital divide discrimination, i.e. the disparity in opportunity be- tween those who can afford to apply in a metaverse context and those who can’t (availability of a virtual reality headset, a 5G connection, etc.) and between those who are digital natives and those who are of the older generation, with a moderate level of comfort with hightech tools. Between candidates, there is already a digital divide. It is a source of concern for the next 10–15 years, but it should democratize after that. The employment of metaverse increases our reliance on digital tools even further. We are still grappling with the psychosocial hazards involved with this absorption in virtuality at the price of reality. “The most important thing is not to substitute the metaverse for the human, and not to confuse digitalization with robotization,” says Alexandre Pham [14]. If we are to succeed in this environment, as well as in the digital world in general, we must regard it as a type of social connection in addition to the physical meeting. Companies must take care to have genuine recruiters behind the avatars in order to retain human touch and the quality of personalised advise.

Metaverse for the Recruitment Process: Towards an Intelligent Virtual …

5

303

Conclusion

The metaverse is constantly changing, and the number of metaverse platforms is staggering. The metaverse is a business concept that has the potential to significantly impact various industries. To keep up with these developments, businesses must be prepared to provide novel recruitment tools, attractive candidate experiences, and more immersive interactions. In this research, we identified the contributions and challenges of metaverse in the recruitment process after investigating laureates’ perspectives of their use. According to the findings of this study, in order to use metaverse effectively, it is critical to remember that metaverse cannot replace the human factor. It’s a supplement to contacting a candidate and a gateway to the traditional recruitment procedure. However, if the entire recruitment process is carried out through metaverse, with an intelligent virtual recruiter, there is a possibility of dehumanization. However, we should not rule out the potential of what the metaverse’s future may bring. Indeed, new, complementary tools could emerge to enable the successful use of metaverse in the recruitment process. Finally, we discovered that recruiters are looking for “innovative” recruitment methods (how to select candidates in a more innovative, interactive, motivating, and meaningful way using metaverse?), whereas the recruiter should also be interested in the case studies that will be proposed in metaverse, i.e. what to evaluate in metaverse? When it comes to judging candidates’ soft and hard skills in metavers, it is essentially this question that is at stake. Metaverse have the potential to transform the workplace, beginning with the hiring process. However, if metaverse remain nothing more than avatar exchange rooms, we will simply be gamifying recruiting. Human resources will have little to look forward to in terms of additional value. Meta-recruitment will undoubtedly be a one-of-a-kind experience in terms of company brand and applicant experience. It is a technology that should not be disregarded because it will modernize the image of a corporation. Will companies, however, take the risk of relying solely on this instrument for their recruitment processes? Obviously, this question extends much beyond the findings of the current study. In practice, the answer to this question will be delivered as part of future study based on metaverse experimentation as part of a business recruitment process.

References 1. HR Barometer WeSuggest x Parlons RH. (2022). Evaluation of soft skills: Why recruiters need to step up their game?. 2. Quote available at : https://citation-celebre.leparisien.fr/citation/la-bonne-personne 3. The Virtual Samurai by Neal Stephenson, ISBN-10: 2253083186. March 2017. Publisher: Le Livre de Poche.

304

N. Chafiq et al.

4. “Second Life,” the metaverse created and managed by its users, by Mathilde Loire. Published on February 20, 2022. 5. Castillo, A. (2017). Popular creative CVs. Le Temps. Published on December 21, 2017. 6. Delfort, T. (2020). Opinion | How AI will allow recruiters to refocus on the human. Les Echos. Published on February 13, 2020. 7. Laidet, S., & Di Pasquale, S. (2022). Recruitment in the metaverse: What awaits candidates (or not). Cadremploi. Published on January 17, 2022. 8. Belhout, D. (2019). Job search: Should candidates be evaluated on how they organize their search? Published on July 26, 2019. 9. Metaverse: Don’t Miss the Train When It Passes, says Edouard Bliek, CEO of Stedy Consulting, by Christian Gladieux. Published on March 25, 2022. 10. Gobin Mignot, É., Wolff B. (2020). 20. Training with virtual reality. In M. Barabel (ed.), Le grand Livre de la formation. Techniques et pratiques des professionnels du développement des compétences. Paris, Dunod, Hors collection. https://doi.org/10.3917/dunod.barab.2020.02.0479 11. First Day: Best practices for being welcomed into a company. Welcome to the Jungle. Published on November 16, 2017. 12. When Recruitment Enters the Metaverse by Philippe Jean Poirier. Published on 10 March 2022. 13. Metaverse and Meta-Media, A 3rd Chapter of Interne by KATI BREMME. Fall-Winter 2021. 14. Recruitment: When Mistertemp’ Explores the Metaverse and Virtual Reality by Julie Le Bolzer. Published on December 6, 2022.

Enhancing Immersive Virtual Shopping Experiences in the Retail Metaverse Through Visual Analytics, Cognitive Artificial Intelligence Techniques, Blockchain-Based Digital Assets, and Immersive Simulations: A Systematic Literature Review Ghazouani Mohamed, Fandi Fatima Zahra, Zaher Najwa, Ounacer Soumaya, Karim Yassine, Aziza Chakir, and Azzouazi Mohamed

Abstract

The future of retail is rapidly evolving with the emergence of immersive virtual shopping experiences in the retail metaverse. To enhance this digital landscape, a combination of cutting-edge technologies is being utilized. Visual analytics allows retailers to gather valuable insights from vast amounts of data, enabling them to understand consumer preferences, behavior, and trends. Cognitive AI techniques take virtual shopping experiences to the next level by providing personalized recommendations, virtual assistants, and chatbots that mimic human interactions, thereby creating a more engaging and tailored experience for shoppers. Furthermore, the integration of blockchain-based digital assets ensures secure and transparent transactions, enabling the seamless exchange of virtual goods and services within the metaverse. This systematic literature review uses Web of science and Scopus as databases to store and analyze the existing research on Virtual Shopping Experiences. The purpose of this G. Mohamed (B) · F. F. Zahra · Z. Najwa · O. Soumaya · K. Yassine · A. Mohamed Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco e-mail: [email protected] K. Yassine · A. Chakir Faculty of Law, Economics and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_17

305

306

G. Mohamed et al.

review is to provide the scholar community with a current overview from 2019 to 2023. Keywords

Virtual Shopping Experiences • Metaverse • Blockchain

1

Introduction

The retail industry is undergoing a swift and radical transformation that will usher in a new era characterized by immersive virtual shopping experiences within the vibrant retail metaverse. This metamorphosis is propelled by the seamless integration of cuttingedge technologies, each contributing to a symphony of innovation that is reshaping how consumers engage with products and services. As we delve into this realm of technological fusion, we encounter a trio of revolutionary elements—visual analytics, cognitive AI techniques, and blockchain-based digital assets—all orchestrating a harmonious ballet of advancement [1]. Visual analytics, a powerful tool in the retailer’s arsenal, has emerged as a beacon of insight in the vast sea of data generated by contemporary retail interactions. This technology empowers retailers to decipher intricate patterns from voluminous datasets, unlocking invaluable revelations regarding consumer preferences, behaviors, and trends. By harnessing the prowess of visual analytics, retailers gain a deeper understanding of the elusive nuances that shape the retail experience, ultimately enabling them to curate personalized journeys for every shopper. The symphony of innovation crescendos with the introduction of cognitive AI techniques, propelling virtual shopping experiences to uncharted heights of engagement and customization. These techniques form the backbone of virtual assistants, chatbots [Ai chatbots provide consumer services 24/7 costumer services and simplify return procedure answering queries in Real time] [2], and personalized recommendation systems that mirror human interactions. In doing so, cognitive AI transcends the conventional boundaries of technology and forges an authentic connection between shoppers and the digital retail environment. As a result, shoppers find themselves immersed in an experience that not only caters to their preferences but also resonates on a human level. Amidst this symphony of technological innovation, blockchain technology emerges as the safeguard of trust and transparency within the retail metaverse. By integrating blockchain-based digital assets, the retail ecosystem is fortified with a secure and unalterable ledger, [3] ensuring the integrity of transactions in the virtual realm. The seamless exchange of virtual goods and services becomes a reality, underpinned by a foundation of cryptographic security that fosters a new era of confidence and fluidity in digital commerce. This systematic literature review embarks on a journey to distill the essence of these transformative forces. Leveraging the comprehensive databases of Web of Science and Scopus, this review delves into the corpus of research spanning from 2019 to 2023. The primary objective of this review is to present the scholarly community with an up-to-date panorama of the evolving landscape of Virtual Shopping Experiences. As

Enhancing Immersive Virtual Shopping Experiences in the Retail …

307

we traverse through the pages of this exploration, we unravel the tapestry of innovation woven by visual analytics, cognitive AI techniques, and blockchain-based digital assets, all converging to shape the future of retail in the immersive realm of the metaverse.

2

Methodology

Starting in April 2023, we undertook an extensive quantitative review of the literature by utilizing the Web of Science, Scopus, and ProQuest databases and eBooks. Our search strategy was centered around key terms, including “Shopping Experiences” + “Metaverse,” “visual analytics,” “Blockchain-based Digital Assets,” “cognitive artificial intelligence techniques and algorithms,” and “immersive digital simulations.” These chosen search terms were deliberate selections based on their prevalence and significant relevance within the body of analyzed literature. Our comprehensive examination encompassed studies published between the years 2019 and 2023. After excluding contentious results, outcomes lacking replication support, overly vague content, and duplicate titles, we curated a selection of 19 primary sources this systematic literature review demonstrated the positive impact of cognitive AI techniques, visual analytics, and block chain-based assets on enhancing outcomes for retail businesses within the Metaverse, as evidenced by the findings extracted from the identified papers (refer to Tables 1 and 2).

Table 1 Topics and types of scientific products identified and selected

Topic

Identified

Metaverse + live shopping

13

Selected 3

Metaverse + artificial intelligent

15

2

Metaverse + visual analytics

20

5

Metaverse + Blockchain

15

8

60

18

6

0

Type of paper Original research Review Survey

7

2

Newsletter

1

1

eBook

2

1

308

G. Mohamed et al.

Table 2 A general summary of the evidence related to the focus subjects and descriptive results Paper

Discussed Ideas

– Retail analytics can build Live shopping in the brand awareness metaverse visual and cognit1ve art1ficia intelligence – Computer vision algorithm can harness contextual techniques [1] consumer shared virtual environment by customizing user experiences – By consumers interaction companies can generate a real t1me dataset

Perspectives Future studies should investigate livestream purchasing in extended reality, while subsequent analysis should explore cutting-edge metaverse brands. Moving forward, emphasis should be placed on improving digital buying convenience and customer service throughout immersive 3D environments

Virtual consumerism: an – Centric strategies to generate Enhancing security in the exptorat1on of e-commerce in increased engagement and metaverse and make Tams to metaverse [2] revenue in virtual market govern goods – Yield valuable insight for strategies marketers and policy navigating this digital transformation in consumer behavior – Brand awareness by cited 3example of meta -commerce (NIKeIand, Gucci virtual world and shopify) Fusing blockchain and Al with – The evolution of metaverse. A Survey [4] e-commerce from 2 to 3d world related to improvement of Ai based assistant – Metaverse can viewed as a complete self-consistent economy – Flat currency cannot satisfy the Metaverse demands – Smart contract are a crucial role in metaverse transacl1on because it guaranty trust1ness and transparency

In order to work toward an open, just, and rational future metaverse, academia and industry must collaborate for a promising experiences in metaverse

(continued)

Enhancing Immersive Virtual Shopping Experiences in the Retail …

309

Table 2 (continued) Paper

Discussed Ideas

Perspectives

Blockchain meets metaverse – Metaverse and blockchain and digital asset management: integration – Blockchain in metaverse and a comprehensive survey [5] asset management. Applications. social finance, metaverse social services, identity management, decentralized governance, data management – Digital asset management, blockchain, NFTs, challenges

In anticipation to actualize the full-flesh metaverse in the upcoming years, additional innovations are needed to create appropriate and effective blockchain-based solutions enabling various metaverse infrastructure functions and applications

Exploring the metaverse in the – The use of Ai technology digital economy an overview can improve knowledge of and research framework [6] fashion consumers – Pioneering strategies for crossing the digital frontier for companies, and related stakeholders

Illumining novel perspectives on the metaverse’s developmental trajectory. As these evolving scenarios hold the potential to profoundly reshape our lives, it is imperative to acknowledge that novel challenges tied to the metaverse necessitate rigorous deliberation and resolution

3

The Metaverse: A New Frontier for Retails

Following the unprecedented COVID-19 pandemic, our society has experienced a seismic change in its fundamental dynamics, which has reshaped many aspects of our daily life [4]. The world of physical and economic activities has undergone one of the most significant changes. The restrictions placed on by the epidemic, intended to slow the virus’s rapid spread, prevented in-person communication and halted conventional forms of trade. A large gap in the retail environment developed as physical establishments closed their doors to follow safety procedures and reduce the danger of infection, necessitating the need for creative solutions and adjustments. In this altered environment, e-commerce emerged as a resounding beacon of hope and a solution to the challenges posed by the pandemic. As people sought ways to fulfill their purchasing needs while adhering to safety guidelines, online platforms became the lifeline for shopping. Consumers turned to digital channels, opting for the convenience and safety that e-commerce offered. This marked a turning point in consumer behavior, triggering an unforeseen surge in online transactions. This paradigm shift, while necessitated by adverse circumstances, catalyzed a profound transformation within the e-commerce sector. What ensued was an astounding proliferation of e-commerce platforms, as businesses rushed

310

G. Mohamed et al.

to establish a robust online presence to remain relevant and accessible to their customer base. As brick-and-mortar stores faced an uncertain future, brands swiftly adapted to the evolving landscape, opting to leverage the vast potential of the digital realm. Consequently, a cascade of physical stores transitioning to online platforms ensued. Brands that had once relied on the allure of in-person shopping experiences were now embracing the digital frontier, opening e-commerce websites to meet the burgeoning demand. This transition was not merely an adjustment to the circumstances; it marked a fundamental shift in the way businesses engage with their customers. Among this transformative wave, another powerful force was at play—the fourth paradigm (Artificial intelligence (AI)) characterized by the rapid advancement of virtual reality (VR), augmented reality (AR), blockchain technology, and the introduction of fifthgeneration (5G) connectivity. These technologies ushered in a new era of possibilities, one that beckoned the retail industry to venture into uncharted territory. Within this burgeoning ecosystem, the concept of the Metaverse emerged as a focal point of innovation and exploration in the economic system. The Metaverse, a virtual realm encompassing immersive digital experiences, became the canvas upon which the future of retail was painted. Brands and retailers, recognizing the potential for unparalleled personalized interactions, embarked on a race to establish their presence within the Metaverse. At the heart of this metamorphosis lies the promise of exceptional personalized experiences for customers. The Consumers can interact with goods and services in in three-dimensional spaces, and interact with brands on an unprecedented level of intimacy in ways that go beyond what is possible in the physical world. By seamlessly incorporating AI-driven algorithms, virtual reality simulations, and immersive augmented reality interfaces customers can embark on a journey of discovery, interaction, and purchase that is tailored to their individual preferences and needs. Also, this transformation went beyond a mere adaptation to circumstances; it represented a fundamental change in how businesses interacted with their clientele. The shutdown of brick-and-mortar establishments led to a reduction in operational expenses and eradicated the geographical limitations that once defined a brand’s outreach. The virtual realm unveiled a worldwide marketplace, empowering brands to transcend barriers and access a more extensive spectrum of potential customers [7].

4

Role of Visual Analytics in Comprehension of Customer Preferences

Aiming to fill the existing gaps in the literature, our paper provides comprehensive discussions, the role of visual analytics stands as a pivotal cornerstone in enhancing the incomes of retail businesses. Visual analytics transcends traditional data analysis methods by infusing them with the power of immersive and interactive visual representations. In this digital realm, where consumers navigate virtual aisles and explore products within

Enhancing Immersive Virtual Shopping Experiences in the Retail …

311

a simulated environment, visual analytics emerges as a potent tool to decode consumer behaviors, preferences, and trends. Visual analytics serves as a bridge between the virtual and real worlds, enabling retailers to harness the immense potential of the data generated by consumer interactions. Through sophisticated algorithms and advanced data visualization techniques, retailers can transform raw data points into meaningful insights, unveiling patterns that might otherwise remain concealed. This data-driven approach empowers retailers to make informed decisions about product placements, pricing strategies, and inventory management, all to optimize sales, revenue and help retail in consumer segmentation. Consider a scenario within the Metaverse where a consumer engages with a virtual clothing store [8]. Visual analytics can meticulously track the consumer’s interactions, recording details such as the products they browse, the items they try on virtually, and the amount of time they spend within specific sections of the store [9]. By translating this rich stream of data into comprehensible visual representations, retailers can discern nuanced behavioral cues. They may identify trends in clothing styles that resonate with a particular demographic, gauge the popularity of specific color palettes, or even pinpoint areas of the store that draw the most foot traffic. These insights become a catalyst for strategic decision-making. Retailers can strategically position high-demand products within virtual storefronts, optimizing their exposure to potential customers. By analyzing consumer engagement and preferences, they can tailor their offerings to match evolving trends and customer expectations. Moreover, the ability to track consumer journeys through the Metaverse provides retailers with a deeper understanding of the consumer decision-making process [2]. This knowledge, in turn, empowers retailers to enhance the overall shopping experience, curating it to align seamlessly with individual preferences. Visual analytics does not only provide a retrospective view of consumer interactions; it also enables real-time adjustments. Imagine a scenario where a retailer notices a sudden surge in interest for a specific line of products. Visual analytics allows them to swiftly respond by dynamically adjusting product placements, pricing, and promotional strategies to capitalize on this newfound demand. In the virtual shopping world of the Metaverse, where the boundaries between reality and simulation blur, visual analytics emerges as a transformative force. By deciphering complex consumer behaviors and translating them into actionable insights, it empowers retailers to adapt, innovate, and refine their strategies. As a result, the use of visual analytics becomes a potent catalyst for driving higher incomes within retail businesses. It enables them to orchestrate a symphony of customer engagement, product optimization, and strategic decision-making that harmoniously resonates with the ever-evolving demands of the virtual shopping landscape.

312

5

G. Mohamed et al.

How AI Cognitively Enhances Retail Business in the Immersive Virtual Shopping

We review several representative studies related The integration of cognitive AI techniques, which has catalyzed a revolutionary upheaval in the retail business field, reshaping the immersive realm of virtual shopping into an unprecedented era of opportunities and engagement. At its core, the transformative potential of AI-driven advancements is exemplified by the remarkable capabilities of text mining and sentiment analysis [10]. These tools empower AI systems to navigate vast expanses of textual data, engaging with customer reviews, interactions, and sentiments. Through this deep exploration, AI not only comprehends explicit preferences but also unearths the underlying emotional undercurrents of customer sentiments. This profound understanding allows businesses to tailor their products, marketing strategies, and user experiences, crafting an intimate connection that resonates with consumer desires and emotions, thus fostering enduring loyalty [11]. The cornerstone of this metamorphosis is the advent of AI-powered Chabot assistants that stand as tireless companions, ever-present and ready to engage customers on a 24/ 7 basis [11]. Enriched by natural language processing, these virtual shopping aides initiate real-time dialogues, swiftly delivering responses, product insights, and personalized recommendations. The harmonious partnership between AI and chatbots ushers in a new dimension of customer interactions, establishing a dynamic and interactive shopping environment. Here, customers can access guidance, product inquiries, and assistance at any moment, transcending the temporal confines of conventional business hours [2]. In parallel, the synergy between cognitive AI techniques and deep learning algorithms propels the evolution of retail business within immersive virtual shopping. Through the exhaustive analysis of expansive datasets, these algorithms uncover intricate correlations and patterns, offering insights that often elude human observation. This invaluable knowledge enables businesses to predict customer behaviors, customize product assortments, and optimize inventory strategies. As AI delves into the labyrinth of customer preferences and historical trends, it continually refines its insights, elevating the virtual shopping experience to an unparalleled realm of personalization and relevance [12]. the assimilation of cognitive AI techniques into the fabric of immersive virtual shopping redefines the retail landscape, infusing it with heightened customer-centricity and sophistication. By leveraging text mining, sentiment analysis, chatbot support, and deep learning algorithms, businesses create a dynamic and tailored retail journey that resonates deeply with individual customers. This symbiotic alliance between AI and immersive virtual shopping not only amplifies business outcomes but also inaugurates an era where shopping evolves beyond mere transactions. It metamorphoses into a personalized, immersive, and emotionally resonant experience, fostering unwavering customer loyalty and propelling the retail sector into uncharted realms of triumph and innovation. Through these transformative technologies, personalized virtual shopping experiences shatter the conventional confines of commerce, forging a new paradigm where customer engagement

Enhancing Immersive Virtual Shopping Experiences in the Retail …

313

is not only intuitive and tailored but also finely attuned to the ever-evolving needs and aspirations of individuals in the digital age.

6

Virtual Reality and Augmented Reality Technologies Effects on the Retails Sector’s Transformation

The impact of virtual reality (VR) and augmented reality (AR) technologies on the transformation of the retail industry is nothing short of revolutionary. These immersive technologies have ushered in a new era of consumer engagement, fundamentally altering the way customers interact with products and brands [13]. VR and AR have transcended the boundaries of traditional shopping, offering consumers the ability to explore virtual stores, visualize products in real-world settings, and even try them on virtually. This transformative shift enhances the overall shopping experience by adding an interactive and sensory dimension, blurring the lines between the physical and digital realms. Retailers, on the other hand, have leveraged VR and AR to create captivating and memorable shopping journeys, fostering deeper connections with their audience and differentiating themselves in a competitive market. Furthermore, in a paper of International Journal of New Media Studies [2], there is an example that is indicated of an innovative project that enables customers to browse a virtual store, interact with products, and even try them on electronically is Nike’s “NIKEland.“ This ground-breaking idea ties together the in-store and online shopping experiences by allowing shoppers to interact physically with products, judge fit, and envision how items might appear in real life. It provides an experience journey that goes beyond just 2D visuals and enables clients to make knowledgeable purchasing decisions. Similar to how Gucci’s Virtual World, which offers a thorough fashion-social platform, has elevated virtual shopping to new heights. Customers have the option to create and customize avatars, dress them in Gucci clothing, and communicate with other avatars in a communal virtual space. This clever tactic not only displays things but also fosters the feeling of interconnectedness and individuality, resulting in a distinctive amalgamation of shopping and interactive social engagement. The benefits of virtual shopping experiences for both consumers and retailers are multifaceted. For consumers, the immersive nature of VR and AR provides an unparalleled opportunity to make informed purchasing decisions. These technologies enable customers to preview products in a personalized context, alleviating doubts about fit, design, and functionality. This leads to higher customer satisfaction and reduced returns, thus streamlining the shopping process. Moreover, virtual shopping experiences introduce an element of entertainment and novelty, making the act of shopping more engaging and enjoyable. On the retailer’s side, VR and AR allow for more efficient use of physical space by offering an extended product range within a limited area [14]. Retailers can also gather invaluable insights into customer behavior, preferences, and interactions, refining

314

G. Mohamed et al.

their strategies and optimizing inventory management. Furthermore, these technologies facilitate targeted marketing and promotional campaigns, enhancing brand visibility and customer engagement. Also, VR and AR technologies drive sales by allowing customers to make informed decisions. Augmented reality applications enable customers to see how products would fit into their environments, thereby reducing uncertainty and the likelihood of returns. This boosts revenue, decreases product returns, and improves customer happiness. For example, as indicated in a newsletter found in LinkedIn website titled “The Impact of Virtual Reality on In-Store Shopping Revolutionizing the Retail Experience” platforms that employ augmented reality for visualization have seen a notable decline in return rates, with Shopify claiming a 40% decrease in returns as a result of augmented reality and 3D visualization.

7

Blochchain Necessary Tool for Secure Metaverse Transactions

The first concern raised is what about security in a promising environment like metaverse where individuals submit their personal information and goods and what are the mechanisms that ensure a secure exchange in this world? We found response after reading many significant paper in the security field “Blockchain”. As is defined in IBM web site Blockchain is a decentralized, immutable database that makes it easier to track assets and record transactions in a corporate network. Is used to store transactions and data. In particular, transactions submitted by network nodes are grouped into blocks, and blocks are subsequently connected to build chains using hash functions. Each node in the network holds a copy of this chain, which is dispersed throughout it. Like Is mentioned in the book [15] Blockchain can help creating the economic system of the metaverse with NFT, cryptocurrency and DeFi Blockchain has exceptional qualities like immutability, transparency, decentralization, and security because of this design. And in this surge of research on how blockchain has won over shops in the Metaverse, we keep coming back to a report that was published in 2006 [16] this case study demonstrating successful implementation of blockchain-based digital assets in the retail metaverse by implementing block chain in sandbox. This virtual world enables users to create, own, and monetize gaming experiences and assets. Built on the Ethereum blockchain, The Sandbox employs non-fungible tokens (NFTs) to represent unique digital items, ensuring verifiable ownership and scarcity. Users can trade these NFTs securely, as ownership history is traceable and unalterable. The platform’s success showcases how Blockchain enhances the value of virtual goods, providing users with real ownership and the ability to participate in a thriving virtual economy. Also as discuss in International Journal of Computational Science and Engineering paper [17] blockchain fostering a peer-to-peer economy by eliminating the need for thirdparty intermediaries like banks. Traditionally, financial transactions required banks to validate, process, and oversee transfers, resulting in delays, fees, and potential privacy

Enhancing Immersive Virtual Shopping Experiences in the Retail …

315

Fig.1 The difference between E-commerce shopping (based fiat-currency) and Metaverse shopping (based cryptocurrency)

concerns. With blockchain, transactions occur directly between parties within a decentralized and secure network. Smart contracts ensure automated execution of agreed-upon terms, reducing the need for human intervention. This not only expedites the transaction process but also significantly lowers costs (Fig. 1).

8

Challenges of Use Blockchain

There were several recent surveys discuss Blockchain technology has encountered a number of important challenges, particularly in the context of cryptocurrencies Bitcoin, including the idea that they are more expensive than conventional fiat currencies. The thing that impact retails business in particularly, Transaction costs and price volatility are the two main focuses of this problem [3]. Bitcoin, including the idea that they are more expensive than conventional fiat currencies. The thing that impact retails business in particularly, Transaction costs and price volatility are the two main focuses of this problem [3] recent surveys discuss Blockchain technology has encountered a number of important challenges, particularly in the context of cryptocurrencies like Bitcoin, including the idea that they are more expensive than conventional fiat currencies. The thing that impact retails business in particularly, Transaction costs and price volatility are the two main focuses of this problem [3].

316

G. Mohamed et al.

The rapid and unpredictable fluctuations in the value of cryptocurrencies have led to concerns among potential users and businesses about the stability of their assets. This volatility can hinder the mainstream adoption of cryptocurrencies for everyday transactions and as a store of value. This effect Can make consumer and producer hesitant to use cryptocurrencies as a means of payment or store of value as said in a paper in international journal of production economy [18]. The erratic nature of cryptocurrencies causes uncertainty in pricing and income estimates for producers. They run the danger of receiving dramatically different values for their goods or services based on market movements if they accept bitcoins as payment. This unpredictability can make it difficult for firms to successfully manage their budgets, spending, and investments, which can complicate financial planning.

9

Challenges

However, amidst the potential advantages, virtual shopping experiences also pose challenges for both consumers and retailers. For consumers, issues like motion sickness and discomfort in prolonged VR sessions remain concerns that need to be addressed. Additionally, the initial cost of acquiring VR and AR devices can be a barrier for some customers, and not everyone has the necessary high-speed internet connectivity to fully experience the shopping metaverse [19]. On the retailer’s side, implementing VR and AR technologies demands significant investments in infrastructure, software development, and employee training. Ensuring a seamless and user-friendly experience across different devices and platforms can be complex and time-consuming. Privacy and security concerns related to data collected during virtual shopping experiences must also be carefully managed to maintain customer trust [20]. As such, the transformative potential of virtual reality and augmented reality in the retail industry comes with a set of challenges that stakeholders must navigate to fully realize the benefits of immersive shopping experiences.

10

Synthesis of the Major Results of Research

The Metaverse it a mine which provide an immense amount of real time data that can translate to a manful insight that help brands and retails to understands preference of costumers and make segmentations their client which result a significant improve in their sales also the role of AI that can personalized customer experiences by make their disposition an assistant who help answerer to their question in real-time, and finally how blockchain is crucial tool for transaction in 3d virtual worlds …

Enhancing Immersive Virtual Shopping Experiences in the Retail …

11

317

Limitation

By analyzing several paper and resources especially that one which are published between 2019 and 2023. we found a lot of limitations which is considered as an obstacle against development of business retails in Metaverse in this right moment. we cite the principals one finding in our research study as the lack of utilization of VR/AR tools from the customer’s perspective and the absence of experts in the development of 3D virtual worlds that merge with reality, the legal frameworks governing personal property and privacy laws, as well as the integration of 5G connectivity into the Economic system, contribute to these limitations. Additionally, a significant challenge arises from the high value of cryptocurrencies compared to fiat currency. It becomes necessary to contemplate the creation of a cryptocurrency equivalent to fiat currency that maintains stability, thereby, encouraging investors to confidently participate in Meta commerce. All this limitation can be developed and implement in Metaverse for a brilliant future retail business.

12

Conclusion

Our systematic review is integrated into all of our investigations indicating how The retail business, fueled by AI-driven cognitive techniques, empowered by visual analytics, and underpinned by blockchain-based assets, has embarked on a voyage into the Metaverse. Our study adds clarity to existing evaluations of how deep neural networks and natural language processing algorithms representing by Ai chatbots can increase consumer engagement and build brand loyalty It is a realm where customer experiences are elevated, data is a strategic asset, and the boundaries of physical limitations are dissolved. Also we highlight in our paper the important role of blockchain which has given a strong point for the retail business in the metaverse by guaranteeing security and transparency in peer-to-peer transaction. As brands and retailers navigate this uncharted terrain, the future of retail in the Metaverse beckons with endless possibilities and untold potential. It is a future where innovation is the compass, personalized experiences are the guiding stars, and the pursuit of excellence in customer engagement is the ultimate destination.

References 1. Kliestik, T., Novak, A., & L˘az˘aroiu, G. (2022). Live shopping in the metaverse: Visual and spatial analytics, cognitive artificial intelligence techniques and algorithms, and immersive digital simulations. Linguistic and Philosophical Investigations, 2022, vol. 21, p. 187–202. 2. Rathore, B. (2017). Virtual consumerism: an exploration of e-commerce in the metaverse. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 4(2), 61–69.

318

G. Mohamed et al.

3. Yang, Q., Zhao, Y., Huang, H., et al. (2022). Fusing blockchain and AI with metaverse: A survey. IEEE Open Journal of the Computer Society, 3, 122–136. 4. Ifdil, I., Situmorang, Dominikus D. B., Firman, F., et al. (2023). Virtual reality in Metaverse for future mental health-helping profession: an alternative solution to the mental health challenges of the COVID-19 pandemic. Journal of Public Health, 45(1), e142–e143. 5. Truong, V. T., Le, L. B., & Niyato, D. (2023). Blockchain meets metaverse and digital asset management: A comprehensive survey. IEEE Access. 6. Cheng, X., Zhang, S., Fu, S., et al. (2022). Exploring the metaverse in the digital economy: an overview and research framework. Journal of Electronic Business & Digital Economics, no ahead-of-print. 7. Duan, H., Li, J., Fan, S., et al. (2021). Metaverse for social good: A university campus prototype. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 153–161). 8. Nalbant, K. G., & Aydin, S. (2023). Development and transformation in digital marketing and branding with artificial intelligence and digital technologies dynamics in the Metaverse universe. Journal of Metaverse, 3(1), 9–18. 9. Hamilton, S., et al. (2022). Deep learning computer vision algorithms, customer engagement tools, and virtual marketplace dynamics data in the metaverse economy. Journal of SelfGovernance and Management Economics, 10(2), 37–51. 10. Khakurel, J., Penzenstadler, B., Porras, J., et al. (2018). The rise of artificial intelligence under the lens of sustainability. Technologies, 6(4), 100. 11. Chen, J.-S., Le, T.-T.-Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512–1531. 12. Bratu, S., & Sab˘au, R. I. (2022). Digital commerce in the immersive metaverse environment: cognitive analytics management, real-time purchasing data, and seamless connected shopping experiences. Linguistic and Philosophical Investigations, 21, 170–186. 13. Rathore, B. (2017). Exploring the intersection of fashion marketing in the metaverse: Leveraging artificial intelligence for consumer engagement and brand innovation. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 4(2), 51–60. 14. Bonetti, F., Warnaby, G., & Quinn, L. (2018). Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda. In Augmented reality and virtual reality: Empowering human, place and business (pp. 119–132). 15. Kiong, L. V. (2022). Metaverse Made Easy: A Beginner’s Guide to the Metaverse: Everything you need to know about Metaverse. NFT and GameFi. Liew Voon Kiong. 16. Wright, W., Schroh, D., Proulx, P., et al. (2006). The Sandbox for analysis: concepts and methods. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 801–810). 17. Sarode, R. P., Poudel, M., Shrestha, S., et al. (2021). Blockchain for committing peer-to-peer transactions using distributed ledger technologies. International Journal of Computational Science and Engineering, 24(3), 215–227. 18. Li, Y., Jiang, S., Shi, J., et al. (2021). Pricing strategies for blockchain payment service under customer heterogeneity. International Journal of Production Economics, 242, 108282. 19. Njoku, J. N., Nwakanma, C. I., & Kim, D.-S. (2022). The role of 5g wireless communication system in the metaverse. In: 2022 27th Asia Pacific Conference on Communications (APCC) (pp. 290–294). IEEE. 20. Ha, H.-Y. (2004). Factors influencing consumer perceptions of brand trust online. Journal of Product & Brand Management, 3(5), 329–342.

Enhancing Customer Engagement in Loyalty Programs Through AI-Powered Market Basket Prediction Using Machine Learning Algorithms Mohamed Meftah, Soumaya Ounacer, and Mohamed Azzouazi

Abstract

Artificial Intelligence (AI) has paved the way for numerous technological advancements in various fields, seamlessly connecting one domain to another. In the realm of large-scale retail, AI has been particularly impactful in understanding consumer behavior, as machine learning algorithms play a significant role in this area. In pursuit of this objective, the present work focuses on predicting the next market basket for each user among consumers engaged in loyalty programs. By adopting a hybrid approach and incorporating customer transaction data from those participating in these programs, a link between large retailers and their customers is created. This encourages a deeper understanding of consumer behavior and the improvement of purchasing strategies. To achieve this goal, different machine learning methods have been utilized to predict the next market basket, including Support Vector Machines (SVM), Decision Trees, Random Forests, Logistic Regression, and k-Nearest Neighbors (k-NN). This assortment of methods enables selecting the one that exhibits the best performance, guaranteeing the highest precision of predictions. By harnessing the power of AI and machine learning algorithms, a connection can be established between consumer behavior prediction and effective marketing strategies. In turn, this empowers retailers to enhance their understanding of their customers, leading to increased satisfaction and the optimization of customer engagement in loyalty programs.

M. Meftah (B) · S. Ounacer · M. Azzouazi Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_18

319

320

M. Meftah et al.

Keywords

Artificial Intelligence (AI) • Consumer behavior • Market Basket prediction Machine learning algorithms • Loyalty programs

1

•

Introduction

In large retail environments, understanding customers’ purchasing habits is crucial for optimizing sales and marketing strategies. The complex nature of modern retail businesses, with their vast product offerings and diverse customer bases, presents a significant challenge in comprehending consumer behavior. Retailers must grapple with the everevolving preferences of their customers, seasonal trends, and the influence of competitors, all of which contribute to the intricate landscape of consumer behavior. Machine learning, a subset of artificial intelligence, offers a powerful solution to this challenge by enabling machines to analyze data, identify patterns, Kelleher, J.D. et al. (2015), and make decisions or predictions based on those patterns [1]. This technology has the potential to revolutionize the way retailers approach their sales and marketing efforts, allowing them to better cater to their customers’ needs and preferences. By utilizing advanced machine learning algorithms, it is possible to analyze customer transaction data to predict the products likely to appear in each consumer’s next market basket. These predictive models can suggest the most relevant products to customers, enhancing their shopping experience and allowing retailers to design targeted loyalty programs and promotions that encourage customers to purchase complementary products or return to the store. However, effectively employing machine learning algorithms requires considering several key factors, such as average purchase days, reorder percentage, the number of times a product has been reordered, and the percentage of product repurchase. These data must be meticulously analyzed to accurately predict each consumer’s next market basket in large retail environments. By accounting for these factors, retailers can gain a better understanding of their customers’ purchasing habits and improve their engagement in loyalty programs. Consequently, predictive models based on machine learning algorithms can assist large retailers in optimizing their distribution and sales processes by providing personalized suggestions to customers and increasing sales through targeted promotions and offers. In summary, machine learning proves to be an invaluable technique for understanding customer behavior in large retail environments and optimizing retailers’ sales and marketing strategies by creating predictive models from customer data. The insights gained from these predictions enable retailers to create more personalized shopping experiences, optimize inventory management, design targeted marketing campaigns and promotions, enhance loyalty programs, and identify trends and patterns in consumer behavior. Ultimately, this helps retailers to drive sales, increase customer satisfaction, and improve their

Enhancing Customer Engagement in Loyalty Programs Through …

321

overall performance. The primary goal of this research is to investigate the capabilities of various machine learning algorithms in predicting the next market basket for each consumer in large retail environments and to assess the impact of considering key factors and integrating information from loyalty program participants. The following research questions are addressed: How can machine learning algorithms be applied to analyze customer behavior and predict their next market basket in large retail environments? What is the effect of considering key factors such as average purchase days, reorder percentage, the number of times a product has been reordered, and the percentage of product repurchase on the accuracy of market basket predictions? How does integrating information from loyalty program participants enhance the predictive power of machine learning algorithms and improve retailers’ understanding of their customers’ purchasing habits? How can insights gained from market basket predictions be utilized by retailers to optimize sales and marketing strategies, including personalization, inventory management, targeted promotions, and loyalty program enhancements? The recent advancements in this domain, as showcased by A. H. Patwary and colleagues in 2021, underline the significance of Market Basket Analysis. This method emphasizes the strategic placement of products and promotional activities to bolster customer loyalty and increase revenue [2]. In order to address the research questions, a comprehensive literature review will be undertaken to collect information on various machine learning algorithms and their use in large retail environments like retail chains and supermarkets. The challenges and limitations of these techniques will be examined, considering key factors such as average purchase days, reorder percentage, the number of times a product has been reordered, and the percentage of product repurchase. Drawing from these findings, a proposed solution will factor in these crucial aspects and integrate information from loyalty program participants, presenting a holistic approach to applying machine learning algorithms for predicting customer market baskets in large-scale retail settings. This approach will also evaluate the influence of incorporating these factors and loyalty program data on the accuracy of market basket predictions. Additionally, the research will delve into how the insights obtained from market basket predictions can be employed by retailers to optimize sales and marketing strategies, encompassing personalization, inventory management, targeted promotions, and loyalty program enhancements. This will enable retailers to gain a deeper understanding of their customers’ purchasing habits and enhance the efficacy of their sales and marketing initiatives. This paper’s organization is described as follows: The first section provides an introductory context, highlighting the problems and research objectives targeted by the study. In the second section, there is a discussion on traditional machine learning algorithms, emphasizing their challenges when applied in extensive retail settings. Section 3 introduces a method that takes into account important factors and integrates data from customer

322

M. Meftah et al.

loyalty programs, explaining their relevance in enhancing the prediction of customer market baskets. This section also highlights the value of these factors in improving retailers’ comprehension of customer purchasing patterns. Section 4 describes the dataset employed in the study, the chosen algorithm, and the results of the analysis, along with a discussion of the insights gained. In conclusion, Section 5 summarizes the paper, emphasizing the effectiveness of the suggested approach in real-world retail scenarios and its potential to optimize various aspects of sales and marketing strategies.

2

Related Work

This section explores prior research in the area of analyzing and predicting market baskets, outlining various methodologies employed to understand and anticipate customer behavior. Guidotti et al. (2017) developed a new method for market basket prediction that takes into account recurring sequences with temporal annotations that are specific to individual users [3]. They expanded upon this research in 2018, focusing on personalizing customer services through predicting subsequent shopping lists [4]. Kaur and Kang (2016) conducted research on evolving market trends by employing various techniques in their study [5]. Their study emphasized the value of identifying market patterns and applying this knowledge to anticipate unknown or absent attribute values. Kapadia and Kalyandurgmath (2015) conducted a study analyzing consumer purchasing behavior in a lifestyle store through market basket analysis. Their work illustrated how assessing shopping baskets as a unit of analysis can assist retailers in creating accurate predictions and establishing association models for enhanced decision-making [6]. Anispremkoilraj, Sharmila, and colleagues (2021) concentrated on personalized market basket prediction, underlining the importance of market basket analysis in marketing, sales, decision-making, and customer relations [7]. Kamakura (2012) examined sequential market basket analysis, contrasting predictions derived from traditional market basket analysis with sequence-based approaches to discern potential benefits [8]. Gangurde, Kumar, and Gore (2017) analyzed the application of market basket analysis for prediction modeling, suggesting that using market basket analysis outcomes to foresee future customer behavior is advantageous [9]. Similarly, Jain, Sharma, Gupta, and Doohan (2018) introduced a system for predicting business strategies using market basket analysis, generating results and employing them for prediction purposes [10]. A great deal of research has been conducted to enhance the accuracy of market basket data analysis. Mild and Reutterer (2001) studied the effects of various approaches on analyzing market basket data, proposing an advanced technique for anticipating customer product category preferences [11]. Lately, the focus of researchers has shifted towards developing algorithmic methods to generate predictions of customer behavior. Maske and

Enhancing Customer Engagement in Loyalty Programs Through …

323

Joglekar (2019) proposed an algorithmic approach that relied on correlations between purchased items data [12], while Mansur and Kuncoro (2012) combined market basket analysis with artificial neural networks to predict product inventory needs in small and medium-sized enterprises [13]. In a recent systematic review, Rehman and Ghous (2021) explored the applications of deep learning and association rules in market basket analysis, highlighting its relevance and significance in the retail industry [14]. Their review offers valuable insights into the diverse approaches and techniques used by researchers in this domain. In conclusion, the related work section showcases the continued interest and progress in market basket analysis and prediction. These prior studies provide valuable context and emphasize the importance of contributions to this research field.

3

Proposed Approach

Expanding on previous research, our proposed solution considers essential aspects by incorporating information from loyalty program participants, thereby offering a more comprehensive and holistic approach to applying machine learning algorithms for predicting customer market baskets in large-scale retail settings. This innovative method also examines the impact of integrating various factors, such as customer demographics, preferences, and loyalty program data, on the accuracy of market basket predictions. Furthermore, the study delves deeper into how the insights obtained from market basket predictions can be effectively employed by retailers to optimize various aspects of their sales and marketing strategies. This encompasses areas such as personalization, inventory management, targeted promotions, and loyalty program enhancements, all aimed at providing a tailored shopping experience for customers. By enabling retailers to gain a deeper understanding of their customers’ purchasing habits and preferences, this approach enhances the efficacy of their sales and marketing initiatives, leading to increased customer satisfaction and loyalty. The proposed solution combines traditional data analysis with insights gained from customer transactions, resulting in a more accurate representation of real-world customer behavior. This hybrid approach allows retailers to make more informed decisions about product placement and optimize their purchasing strategies based on the analysis of customer market baskets. In summary, our proposed method for predicting customer market baskets in largescale retail settings offers a unique and comprehensive perspective compared to traditional approaches. By utilizing data from loyalty programs and considering various factors that influence customer behavior, this approach aims to contribute significantly to the development of more effective and efficient retail practices.

324

4

M. Meftah et al.

Results and Discussion

Expanding on previous research, our proposed solution considers essential aspects by.

4.1

Dataset

The database utilized for this research comprises a set of relational files that detail customer orders over time. It contains more than 3 million orders from over 220,000 users, with information on the week, hour of the day, and the time elapsed between orders. The database is composed of several tables, such as "aisles.csv" containing aisle IDs and names; "departments.csv" with department IDs and names; "products.csv" featuring product IDs, names, aisle and department IDs; "orders.csv" consisting of 3,421,083 orders and seven attribute columns like order ID, user ID, order number, order series, day of the week, hour of the day, and days since the previous order; and "order_products.csv" detailing the products purchased in each order. This last table is split into two subfiles: "order_products__prior.csv" for past product orders for all customers, and "order_ products__train.csv" for the most recent product orders for a subset of customers only. Both files contain four attribute columns, including order ID, product ID, add-to-cart order, and reordered status. The graph above provides an overview of the tables previously mentioned (Fig. 1):

Fig. 1 Database tables diagram

Enhancing Customer Engagement in Loyalty Programs Through …

4.2

325

Length Feature Engineering: Identifying Key Factors for Enhancing Market Basket Prediction Accuracy

Feature engineering involves utilizing domain expertise to derive new variables, also known as features, properties, or attributes, from raw data. A feature represents a common characteristic of independent units that are subject to analysis or prediction. These features are pivotal for the efficacy of forecasting models and can significantly sway the results. Brainstorming or feature testing involves the following steps to retain the most useful features for our predictive model: 1. Deciding which features to create; 2. Feature creation; 3. Testing the impact of identified features on the task; 4. Improving your features if necessary; 5. Repeating the process. The proposed features are summarized in the following Tables 1, 2 and 3. The aim of categorizing data into three distinct groups: attributes related to the user, attributes related to the product, and interactions between user and product, is to accurately forecast a consumer’s upcoming purchase. Attributes linked to the user delve into a shopper’s behavior and purchasing habits, like the cumulative number of purchases, the typical Table 1 Product features Variable

Description

mean_add_to_cart_order

The average order in which a product is added to all carts (Float)

total_times_product_purchased The number of times the product has been purchased (integer) num_users_product_purchased The number of users who have purchased the product (integer) total_times_product_reordered The number of times the product has been reordered (integer) percentage_product_reordered

The percentage of times the product has been reordered (Float)

Organic

Indicates whether the product is organic or not (boolean)

Table 2 User features Variable

Description

mean_add_to_cart_order

The average order in which a product is added to all carts (Float)

total_times_product_purchased

The number of times the product has been purchased (integer)

num_users_product_purchased

The number of users who have purchased the product (integer)

total_times_product_reordered

The number of times the product has been reordered (integer)

percentage_product_reordered

The percentage of times the product has been reordered (Float)

max_products_in_cart

The maximum number of products added to a cart by the user (Float)

mean_products_in_cart

The average number of products added to a cart by the user (Float)

326

M. Meftah et al.

Table 3 Product_user features Variable

Description

total_product_by_user

Count of times a product is bought by a user (integer)

total_product_reorder_by_user

Count of times a user repurchases a product (integer)

percentage_product_reorder_by_user

The percentage of times a user has reordered a product (Float)

mean_add_to_cart_order_by_user

The average order in which a user adds a product to their cart (Float)

mean_days_between_product_purchases_by_ user

The average number of days between a user’s purchases of a product (Float)

last_cart_number_of_product_purchase

The cart number of the last purchase of a product (integer)

duration between orders, and the usual count of items in an order. As for attributes linked to products, they highlight the qualities and features that outline its appeal and distinctiveness. This can encompass details like how often an item has been bought, how frequently it’s reordered, and if it’s organic. Interactions between the user and product meld insights from both user and product attributes to detail the unique dynamics between a shopper and an item, such as how many times an item has been bought by a shopper and the typical sequence in which a shopper adds an item to their shopping cart. By examining these three categories of attributes, machine learning algorithms can effectively anticipate a shopper’s next purchase by studying their historical buying habits and product interactions. This provides retailers with the tools to refine their stock management, product positioning, and marketing approaches, ultimately enhancing customer contentment and loyalty.

4.3

Feature Selection

Feature selection is a method employed in predictive modeling to decrease the number of input variables, potentially reducing modeling computational costs and, in some instances, enhancing model performance. We achieved this by utilizing the SelectKBest approach, which maintains the top k features of X based on their scores. In our research, we employed the chi2 score function with the SelectKBest approach to compute the chi2 statistic between each feature of X and y (presumed to be class labels). In Python, we executed this selection using the sklearn.feature_selection.SelectKBest function, providing the number K and score function as parameters.

Enhancing Customer Engagement in Loyalty Programs Through …

327

The Chi-squared distribution is a statistical distribution that represents the sum of centered reduced normal distributions, denoted as "kai-squared." It is commonly used to compute the chi-squared statistic for categorical targets, which is less affected by the nonlinear relationship between the predictor variable and its target. Specifically, we can represent independent random variables as X1, ..., Xk. By definition, the variable X is such that: X :=

k ∑

χ 2i

(1)

i=1

In our case, we have chosen K as the number of features, which is 8.

5

Feature Scaling

Feature scaling is a data preprocessing technique used to normalize independent features to a fixed range. It is performed to handle highly variable quantities, values, or units. Several techniques are available for scaling, and we have chosen to use StandardScaler(), which is considered the least risky one. This technique assumes that the data is normally distributed and will recalculate each feature so that the data is centered around 0 and has a standard deviation of 1, according to a predefined relationship. xi − mean(x) stdev(x)

(2)

With mean(x) being the mean and stdev(x) being the standard deviation.

6

Models Explored

In classification problems, a variety of machine learning techniques are available. This section focuses on providing an overview of the models employed in our study and their operating principles.

6.1

Logistic Regression

Logistic regression analysis is used to determine the relationship between predictors and the chance of a specific binary outcome using a logistic function. This technique uses labeled data to fine-tune the parameters of the logistic function, aiming to minimize

328

M. Meftah et al.

discrepancies between predicted and observed values. Despite its simplicity and interpretability, logistic regression comes with constraints. It presumes a linear connection between the predictors and the outcome, and it mandates the independence of features. It’s less suited for multi-class classification scenarios. P(Y = 1 ∨ X ) = (

1 1 + e(−(w∗x+b))

)

(3)

where: P(Y = 1∨X) denotes the probability that the response Y is 1 given the predictors X. w is the weight vector. x represents the vector of input features. b stands for the intercept or bias term. The product w*x gives the result of the dot product between the weight vector and the input feature vector. – e symbolizes the base of the natural logarithm, commonly recognized as Euler’s number, approximately 2.71828. – – – – –

6.2

Support Vector Machines (SVMs)

SVMs are a robust category of learning algorithms. Originally crafted for classification objectives, they were later adapted to address regression tasks as well. These algorithms utilize hypothesis functions that comprise hyperplanes within a characteristic space F. This space, F, is implicitly determined from its original domain using a non-linear transformation, typically facilitated by a kernel function. The main goal of SVMs is to predict a function y = f(x) based on a select set of samples, represented as S = (x_1, y_1), …, (x_n, y_n), while considering certain assumptions about the nature of H. The best estimator is the one that minimizes the risk R[h], measured using the mathematical expectation of a loss function L(y, h(x)): [ ] R[h] = E L(y, h(x))

(4)

However, the joint distribution P(x, y) is generally unknown and inaccessible a priori, necessitating the approximation of the previous equation. Statistical learning theory provides results in the form of inequalities, where the confidence term is a function of the number of observations n, the capacity of the subclass containing the hypothesis h, and the probability that the inequality holds. The structural risk minimization procedure proceeds by dividing H into nested subclasses with increasing capacities, allowing the search for the hypothesis that minimizes the inequality.

Enhancing Customer Engagement in Loyalty Programs Through …

329

In the context of SVMs, the optimal hyperplane of the classifier is obtained by solving the following equation: Minimize : W (h) =

1 ||w||2 + C ∗ L 2

(5)

where C is a constant to be chosen. SVC is a related method that also relies on kernel functions but is suited for unsupervised learning.

6.3

Decision Tree

Decision Tree Learning is an approach that utilizes decision trees as predictive models, primarily in data mining and automation fields. In these tree structures, the leaves denote target variable values, while branches represent input variable combinations leading to those values. In decision analysis, decision trees help to clearly illustrate decisions and their associated processes. In learning and data mining, the decision trees that describe the data serve as a foundation for the process. Decision trees are one of the most widely used algorithms in the domain of machine learning. There are two main types of decision trees in data mining: • Classification trees estimate the category label for the target variable. • Regression trees predict a real quantity, in this case, the prediction is a numerical value. The CART method, first presented by Breiman and colleagues (1984), is one of the most renowned approaches [15]. Advantages: • Decision trees do not necessitate data normalization. • The construction of decision trees is not substantially impacted by missing data values. • Decision tree models are highly intuitive and straightforward to communicate with technical teams and stakeholders. Disadvantages: • Minor alterations in the data may lead to considerable changes in the decision tree structure, causing instability. • Training a decision tree model typically requires more time. • Training decision trees can be relatively costly, as they demand greater complexity and time investment.

330

6.4

M. Meftah et al.

Random Forest

Random Forest can be seen as an ensemble of binary decision trees in which randomness has been introduced. Numerous models have been created that correspond to various ways of incorporating randomness into the trees. For example, we can mention: • Tree Bagging introduces randomness into the initial sample by selecting some points rather than others and lets the tree grow until each node contains a single element. •Random Subspace consists of randomly selecting K variables at each node and, among these, choosing the one that minimizes a certain criterion. • Random Forest combines CART, bagging, and random subspace: for each tree, a sample is drawn from the initial sample. At each node, K variables are chosen randomly, and among these, the one that minimizes the CART algorithm criterion is selected. The tree is allowed to grow until there is only one element in each node. • Random Select Split selects the K best splits and randomly chooses one among them. The split position is also calculated randomly. These methods adapt better to the diversity of data than more traditional methods and can produce much more powerful results. Indeed, no assumptions are made about the distribution of the response variable, unlike many models such as the generalized linear model. We propose using the Random Forest technique, initially introduced by Breiman (in 2001) [16].

6.5

K-nearest Neighbors Algorithm (k-NN)

The k-NN technique is a favored method in machine learning suitable for both classification and regression activities. It functions on the resemblance principle, where an entry gets an associated result based on the prevailing trait amongst its k closest data points. In a classification scenario, this method categorizes an entry by evaluating the dominant class tag amongst its k closest data points. Conversely, for regression, it determines the trait’s value by averaging the trait values of its k closest data points. A primary merit of the k-NN method is its uncomplicated nature and straightforward setup. But, it might react differently based on the chosen k value and the metric applied to pinpoint the closest data points. To boost its efficacy, professionals might favor the weighted versions of the k-NN method, where each data point’s significance is gauged by a factor related to its distance from the primary entry. Advantages: • The algorithm is straightforward and simple to implement. • No assumptions are needed for its application.

Enhancing Customer Engagement in Loyalty Programs Through …

331

• Suits both categorization and regression activities. Disadvantages: • Result accuracy heavily relies on data integrity. • For vast data collections, forecasting might take longer. • The algorithm is susceptible to the data’s scale and irrelevant features.

7

Creating Predictive Models

7.1

Evaluating Model Performance

7.1.1

Data Balancing

When the two classes of the target variable Y are not equally represented in the sample, or more specifically when one of the two classes is predominant, we have imbalanced data. This class imbalance leads to an increase in the learning difficulty for classification algorithms, resulting in biased predictions toward the negative population and less robust outcomes. Traditional strategies to address this issue include: • Adjusting performance metrics for imbalance. • Resampling data to approach a balanced situation. We opted to work with the second method, which we will describe in the following sections.

7.1.2

Random Under-Sampling of Data

A strategy to tackle the uneven class distribution is by random adjustment of the training set. The principal techniques for such adjustment in a skewed dataset involve excluding instances from the prevalent class, termed as under-sampling, and replicating instances from the less represented class, referred to as over-sampling. The following two graphs depict the data balancing we performed based on the undersampling method (Fig. 2).

7.1.3

Data Partitioning

By dividing the available data into two subsets (train and test), we considerably reduce the number of samples that can be utilized for model training, and the outcomes may rely on a specific random choice for the training set. One solution to this issue is a procedure called stratified cross-validation. Incorporating the concept of stratified sampling in

332

M. Meftah et al.

Fig. 2 Data balancing

cross-validation guarantees that the training and testing sets have the same proportions of significant features as the original dataset. By doing so, the cross-validation process can more accurately reflect the true generalization error. For example, if a variable is binary with a distribution of 25% zeros and 75% ones, ensuring this distribution in the splits helps validate the effectiveness of the model. In our approach, we designated 80% of the data for training and 20% for testing.

7.1.4

Model Performance Results

Assessing model performance is crucial. The preliminary step involved building models to get an initial understanding of the potential of various machine learning algorithms. It’s typical in these projects to experiment with multiple models to identify which one offers the most promising results. Although we strive for the best performance, it’s worth noting that there’s always scope for enhancement. Therefore, our goal is to enhance the model as much as we can. An important factor that affects the performance of these models is their hyperparameters. Once we have set appropriate values for these hyperparameters, the model’s performance can be significantly improved. We used GridSearchCV to find the best hyperparameter values for the model, as doing this manually would require a lot of time and resources. The table in Fig. 3 summarizes the initial performances we achieved. • • • •

Classifier_name: the name given to the algorithm. Classifier: the type of algorithm. Best_params: the best hyperparameters (after applying GridSearchCV). ROC_AUC: obtained by calling the sklearn.metrics.roc_auc_score function.

333

Model performance

Enhancing Customer Engagement in Loyalty Programs Through …

Fig. 3 Model performance

The Receiver Operating Characteristic (ROC) graph is a visual representation designed to understand a classification model’s effectiveness across various decision boundaries. This graph sheds light on the balance achieved between the rate of true positives and that of false positives (Fig. 4). To gauge the classification model’s comprehensive efficacy, one can employ the AUC, standing for “Area Underneath the ROC Graph.” AUC quantifies the bidimensional space under the ROC graph, determined using integration, spanning from coordinate (0,0) to (1,1). This offers a consolidated measure reflecting the model’s overarching performance. This graph in Fig. 5 illustrates the performance of a classification model by depicting the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings. The ROC curve is represented by the dashed blue line. The AUC, shaded in gray, quantifies the overall ability of the model to discriminate between positive and negative classes across all thresholds. Additionally, as observed in Fig. 3, the RandomForest model has the best score for our model.

ROC curve.

Fig. 4 ROC curve

334

M. Meftah et al.

AUC - Area Under the ROC Curve.

Fig. 5 AUC—area under the ROC Curve

7.2

Evaluation of the Predictive Model

7.2.1

Performance Metrics

• Accuracy: In binary classification, accuracy can be determined by considering both positive and negative outcomes as follows: accuracy =

VP + VN V P + FP + V N + FN

(6)

• Precision: Precision addresses this query: What percentage of the identified positive cases were actually accurate? precision =

VP V P + FP

(7)

• Recall: Recall answers the following question: What proportion of actual positive results was correctly identified? recall =

VP V P + FN

(8)

• F1-score: F1 − score = 2∗recall∗precision recall+precision (9) With: -TP: True positives -TN: True negatives -FP: False positives -FN: False negatives • Confusion matrix: The confusion matrix, also known as the error matrix, is a table that displays the different predictions and test results and compares them to the actual values.

Enhancing Customer Engagement in Loyalty Programs Through …

335

We obtained the following results.

7.2.2

Results

We evaluated the performance of our model using RandomForest classification. The results are shown in Fig. 6 and the confusion matrix is shown in Fig. 7. Please do not include section counters in the numbering. The results in Figure 6 show that our model achieved a similar F1-Score for both classes (0.70). However, the precision and recall values were higher for class 1 (precision=0.71, recall=0.68) than for class 0 (precision=0.68, recall=0.71). The confusion matrix in Figure 7 shows the TP, FP, FN, and TN for each class. The model correctly predicted 169,907 out of 240,000 instances of class 0, and 169,810 out of 248,648 instances of class 1. However, it also made 78,838 false positive predictions for class 0, and 70,000 false positive predictions for class 1. The model’s performance can be considered satisfactory as it achieved a balanced F1Score for both classes. Nonetheless, further improvements are necessary, particularly in decreasing the number of false positive predictions.

Fig. 6 Model evaluation with RandomForest

Fig. 7 Confusion matrix

336

8

M. Meftah et al.

Discussion

In this study, we focused on predicting market basket prediction in a large-scale distribution company using machine learning algorithms. We also proposed an innovative approach to predict customer market baskets in large-scale retail settings. Our proposed approach for predicting customer market baskets expands on previous research by incorporating information from loyalty program participants, customer demographics, preferences, and loyalty program data. This comprehensive and holistic method offers a more accurate representation of real-world customer behavior, enabling retailers to optimize various aspects of their sales and marketing strategies, including personalization, inventory management, targeted promotions, and loyalty program enhancements. We addressed the issue of imbalanced data in predicting market basket prediction by performing random under-sampling, ensuring that the machine learning algorithms could better predict the minority class. Furthermore, we implemented stratified cross-validation to prevent overfitting and give a more precise evaluation of how the model would perform on new, unseen data. The use of GridSearchCV enabled us to efficiently identify the best hyperparameters for our models, thus improving their overall performance. After assessing the RandomForest technique for forecasting market baskets, we derived measures like accuracy, precision, recall, and F1-score. Such measures offer crucial insights into how our model fares, specifically concerning true predictions, incorrect positives, and incorrect negatives. The confusion matrix delivers a graphic depiction of the comparison between model forecasts and real outcomes, facilitating comprehension of the findings. Our suggested strategy for anticipating market baskets in expansive retail environments presents a distinct and in-depth viewpoint, diverging from conventional methods. By harnessing data from rewards schemes and factoring in assorted elements influencing shopping habits, this strategy strives to notably advance retail methodologies in efficiency and effectiveness. Nonetheless, it’s pivotal to recognize that the model’s efficacy can oscillate based on the dataset and the distinct challenge addressed. Therefore, ongoing oversight and refinement of the model are imperative to ensure its precision and potency in basket predictions. In prospective studies, there’s potential to incorporate varied machine learning models or cutting-edge methods, like deep learning, to amplify the model’s forecasting prowess.

9

Conclusion

This study aimed to develop an innovative approach for predicting customer market baskets in large-scale retail settings using machine learning algorithms. By incorporating information from loyalty program participants, customer demographics, preferences, and loyalty program data, the proposed method offers a comprehensive and holistic

Enhancing Customer Engagement in Loyalty Programs Through …

337

perspective, enabling retailers to optimize various aspects of their sales and marketing strategies. In an effort to counteract data imbalance and ward off overfitting, the investigation employed techniques like random under-sampling and stratified cross-validation. The effectiveness of the machine learning approaches was gauged through metrics such as accuracy, precision, recall, and the F1-score. Notably, the RandomForest approach stood out with superior results. In essence, the introduced method paves the way for refining retail strategies, taking into account diverse factors impacting consumer behaviors. Nonetheless, it’s pivotal to persistently oversee and refine the model to ensure its precision. Prospectively, delving into sophisticated techniques might present avenues to amplify its predictive prowess.

References 1. Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press. 2. Patwary, A. H., Eshan, M. T., Debnath, P., & Sazzad, S. M. M. (2021). Market Basket Analysis Approach to Machine Learning. 2021 12th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–5. https://doi.org/10.1109/ICCCNT51284. 2021.9589061 3. Guidotti, R., Rossetti, G., Pappalardo, L., & Pedreschi, D. (2017). Market basket prediction using user-centric temporal annotated recurring sequences. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 70–79). IEEE. https://doi.org/10. 1109/DSAA.2017.61 4. Guidotti, R., Rossetti, G., Pappalardo, L., & Pedreschi, D. (2018). Personalized market basket prediction with temporal annotated recurring sequences. IEEE Transactions on Knowledge and Data Engineering, 30(8), 1484–1497. https://doi.org/10.1109/TKDE.2018.2797966 5. Kaur, M., & Kang, S. (2016). Market basket analysis: Identify the changing trends of market data using association rule mining. Procedia Computer Science, 85, 78–85. https://doi.org/10. 1016/j.procs.2016.05.266 6. Kapadia, G., & Kalyandurgmath, V. (2015). Market basket analysis of consumer buying behaviour of a lifestyle store. In International Conference on Technology and Business Management (ICTBM) (pp. 23–25). 7. Anispremkoilraj, P., Sharmila, V., & Suganya, G. (2021). Personalized Market Basket Prediction. International Journal of Advanced Science and Technology, 30(8), 4381–4388. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/73156 8. Kamakura, W. A. (2012). Sequential market basket analysis. Marketing Letters, 23(3), 505–516. https://doi.org/10.1007/s11002-012-9187-8 9. Gangurde, R., Kumar, B., & Gore, S. D. (2017). Building prediction model using market basket analysis. International Journal of Innovative Research in Computer and Communication Engineering, 5(6), 13269–13275. 10. Jain, S., Sharma, N. K., Gupta, S., & Doohan, N. (2018). Business strategy prediction system for market basket analysis. In Quality, IT and Business Operations (pp. 339–354). Springer. https:// doi.org/10.1007/978-981-10-5570-9_24

338

M. Meftah et al.

11. Mild, A., & Reutterer, T. (2001). Collaborative filtering methods for binary market basket data analysis. In Active Media Technology: 6th International Computer Science Conference, AMT 2001 Hong Kong, China, December 18–20, 2001 Proceedings (pp. 135–150). Springer. https:// doi.org/10.1007/3-540-45265-9_11 12. Maske, A. R., & Joglekar, B. (2019). An algorithmic approach for mining customer behavior prediction in market basket analysis. In Innovations in Computer Science and Engineering (pp. 37–45). Springer. https://doi.org/10.1007/978-981-13-2035-4_4 13. Mansur, A., & Kuncoro, T. (2012). Product inventory predictions at small medium enterprise using market basket analysis approach-neural networks. Procedia Economics and Finance, 4, 3–12. https://doi.org/10.1016/S2212-5671(12)00302-9 14. Rehman, I., & Ghous, H. (2021). Structured critical review on market basket analysis using deep learning & association rules. International Journal of Scientific & Engineering Research, 12(4), 168–183. 15. Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software. 16. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/ A:1010933404324v

Applications of Artificial Intelligence in Industry and in Agriculture

Application of Artificial Intelligence in the Oil and Gas Industry Muhammad Hussain, Aeshah Alamri, Tieling Zhang, and Ishrat Jamil

Abstract

The oil and gas industry substantially influences global energy production due to its complexity and faces different challenges. In various industries, including the oil and gas sector, Artificial Intelligence has recently developed as a potent technology due to its numerous benefits. In this chapter, Authors focus on overall application of AI in Oil and Gas and provide a comprehensive overview of how AI can help the petroleum sector to promote safety culture, reduce repair and maintenance costs, increase production, enhance asset performance through various uses in exploration, transportation, and refining activities. This work also examines AI applications in midstream operations, including pipeline integrity management (PIM), and transportation and logistics optimization. It focuses on AI applications for refinery optimization, predictive maintenance for refining processes, and quality control in downstream operations. Authors further explore AI’s safety and environmental applications, discussing AI-powered risk assessment and safety monitoring, environmental impact analysis and mitigation, and real-time incident detection and response systems. Lastly, this chapter discusses future trends and outlook, discussing emerging technologies of AI in the energy sector and M. Hussain · T. Zhang University of Wollongong, Wollongong, NSW 2522, Australia e-mail: [email protected] A. Alamri (B) Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia e-mail: [email protected] I. Jamil University of Baltistan, Skardu, Pakistan

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_19

341

342

M. Hussain et al.

analyzing its potential impact on oil and gas’s future. It identifies opportunities for further research and development and highlights the significance of AI in transforming the energy sector, driving innovation, and improving efficiency. Overall, this chapter analyzes the importance and impact of AI in the petroleum sector, offering valuable insights into how this transformative technology can revolutionize the sector and address its unique challenges. Keywords

Artificial Intelligence • Machine Learning (ML) • Predictive Analytics • Oil and Gas Industry • Asset Management • Energy Pipelines • Safety and Environment

1

Introduction

The oil and gas sector holds a crucial position in meeting the global demand for energy. It involves exploring, extracting, refining, and distributing hydrocarbon resources to keep up with the increasing global energy demands. Technological advancements have revolutionized this industry over time. As emerging technologies are being unveiled to enhance asset performance and tackle the oil and gas industry’s complex challenges, they are paving the path for increased growth and efficiency. While there is a connection between the Internet of Things, Big Data Analytics, Artificial Intelligence are a few concepts that requires research on how it can be effectively utilized in the oil and gas sector. While Big Data encompasses data gathered from sources of IoT and enables the acquisition of data for input into AI systems [1–3], AI has gained attention across various industries reshaping how businesses operate and add value to the current business process. The capacity of AI to process vast datasets, make rapid decisions, and glean insights from patterns has revolutionized the landscape of innovation and optimization. Currently, AI is playing a crucial role with significant potential to transform operations decision-making processes and safety protocols within the energy sector. Thus, the application of AI in the oil and gas industry aims to gain an advantage and address challenges faced by different oil and gas projects [4]. Other sectors like healthcare, finance, and manufacturing have successfully implemented AI to boost productivity, enhance accuracy, and gain business insights. Similarly, the oil and gas industry can significantly benefit by applying AI in areas such as exploration and production management, reservoir analysis and planning refining techniques enhancement supply chain streamlining, as safety improvement. AI has the potential to bring about efficiency, cost reduction, environmental impact mitigation, and improved worker safety when integrated into these industries. It is projected that, over the decade around the world, the energy sector will contribute in excess of 5% to global GDP compared to its current contribution of approximately 3% [5, 6]. The application of technology can bring about advancements while also addressing sustainability in AI. For example, achieving levels of sustainability is paramount in the oil and gas industry due to its growing complexity and market demands, which pushes

Application of Artificial Intelligence in the Oil and Gas Industry

343

companies towards adopting cutting-edge methods than conventional ones [7]. Although AI is expected to enhance all aspects of projects in this sector, it is important to consider potential downsides as well [8]. The rapid progress of AI could potentially reduce the demand for resources in sectors leading to privacy concerns. It must be cautious in development of AI as its misuse could disrupt the economy and pose a threat to industries heavily reliant on human resources. The applications of AI should expand beyond our imagination as global environmental trends are gradually shifting. In 2021 the global oil and gas sectors produced over $5 trillion USD. As this industry grows it will continue to make a substantial impact on the global economy [9]. The primary theme of this chapter is to analyses the application of AI with a particular focus on the energy pipelines. It aims to highlight the advantages, challenges, and prospects associated with its implementation. By providing insights into aspects of AI usage, this chapter seeks to equip industry professionals, researchers, and students with the knowledge to utilize AI technologies effectively. Moreover, it addresses challenges related to deploying AI, like data quality assurance, cybersecurity measures, ethical considerations, and workforce adaptation. In summary, integrating AI into the oil and gas industry represents a significant opportunity for transformative change. This chapter serves as a guide to navigating the evolving landscape of AI applications, empowering stakeholders to make the right decisions at the right time and harness the power of AI for increased efficiency, sustainability, and innovation in the energy sector.

2

Background

2.1

Overview of the Oil and Gas Sector

The global economy heavily relies on petroleum industry, which encompasses activities like exploring, producing, refining, transporting, and distributing oil and gas resources. This sector plays a vital role in satisfying the world’s energy demands, serving as the power source for various industries including transportation, manufacturing, residential, and commercial sectors. In 2023, the worldwide crude oil market witnessed substantial growth, increasing from $2,747.78 billion in 2022 to $2,904.09 billion, achieving a compound annual growth rate (CAGR) of 5.7%. The market is projected to continue expanding, reaching $3,481.5 billion by 2027, with an expected CAGR of 4.6% [10]. The average daily oil production in the world in 2022 was 80.75 million barrels, including condensates [11]. Russia, Saudi Arabia, and the United States generate the most oil [12]. Table 1 depicts few countries and their production capacities with revenue generated per annum. Though many countries around the world are working on green energy projects, however, annual demand for oil and gas continues to upsurge. Therefore, companies are working to maximize production, cut maintenance and repair costs, and damage the

344

M. Hussain et al.

Table 1 Top producers of natural gas in 2021 Company

Country

Production (cubic feet)

Revenue

Gazprom

Russia

18.2 trillion

$137.71 billion

China National Petroleum

China

4.4 trillion

$366 billion

Sinopec

China

1.2 trillion

$383.72 billion

ExxonMobil

U.S

3.1 trillion

$285.64 billion

BP

U.K

2.9 trillion

$164.19 billion

Chevron

U.S

2.8 trillion

$155.6 billion

Royal Dutch Shell

Netherlands

2.6 trillion

$272.66 billion

Total Energies

France

2.6 trillion

$205.86 billion

Rosneft

Russia

2.3 trillion

$122.65 billion

Lukoil

Russia

1.1 trillion

$131.6 billion

environment due to the impact of energy production. To achieve this, more than conventional methods, petroleum exploration and production are now looking for alternative solutions to predict issues in advance and use data-based decision-making to enhance asset performance and increase profits.

2.2

Oil and Gas Sector and Its Complexity

In Global Economy, the oil and gas sector is making a significant contribution. This sector consists of exploration, refining, and distribution, which are used as a major source of energy worldwide [13]. This large section is often divided into three main sectors as follows; • Upstream • Midstream • Downstream.

2.3

Key Challenges

Regulatory requirements, environmental issues, increasing maintenance cost, fluctuating global demand, integration of advanced technologies, supply chain complexity and other issues are listed in below Table 2.

Application of Artificial Intelligence in the Oil and Gas Industry

345

Table 2 Faced challenges in oil and gas companies Challenge

Brief details

Price volatility

Oil prices are currently experiencing high volatility. In December 2014, oil price dropped from over $100 per barrel to a less than $60 [14]. These fluctuations in oil prices can have a substantial impact on the profitability and long-term planning of oil and gas companies

Regulatory requirements

Implementing these digital solutions must comply with regulatory and standards requirements. This process might be complex due to security, especially when dealing with sensitive information and critical operations

Environmental issues

Companies around the Globe are working towards green energy solutions and is trying to reduce carbon emissions. Oil and Gas Industry is under huge pressure to reduce emission and operate under safe operating parameters

Increasing maintenance cost

Due to increasing cost especially after Covid19, people are aiming to reduce their maintenance cost and many companies are cutting their maintenance budget to increase profitability. Such reductions can affect asset life and reduce efficiency with time if they are not properly look after or managed. The consequences are expansive repairs and business downtimes

Data management and utilization

Due to its complexity, oil and gas are using large number of sensors to collect data on a daily basis from various sources, including sensors, equipment, drilling operations, and reservoir simulations. Most of the business in energy sector are facing the problem that they are not effectively managing, analysing, and deriving actionable insights from the data to improve operational efficiency, reduce repair cost, predict anomalies and decision-making [15, 16]

Talent gap

With time, experienced people get retired and unfortunately the knowledge is not transfer to the next generation which created a huge gap in the industry. Second challenge is utilising latest technologies to monitor asset health, and this needs a good understanding of the technology and its associated system. Due to needs skilled professionals who can understand and leverage digital technologies effectively

Data quality and standardization

The accuracy and consistency of data collected from various sources are essential for meaningful insights and decision-making using different machine learning and AI models. Maidla et al. [17] discussed technical issues on the data recording sensors, its limitations, interval of data recording and also the quality (continued)

346

M. Hussain et al.

Table 2 (continued) Challenge

Brief details

Budget issues

Most of the companies are facing financial issues to adopt AI on their assets [18, 19]. Preveral et al. [21] proposed that each oil and gas business owner needs to develop their own analytics tools that can analyse the data, and a storage facilities to reduce the cost of software ownership

Data transfer

The issue of accessing and circulating information is indirectly connected to utilise AI applications. Institutional and technical challenges arise when data is stored in locations and formats that impede retrieval and transfer [22–25]

Data privacy

The most sensitive matter pertains to privacy, encompassing conceptual, legal, and technological implications. This concern underscores its importance within the realm of big data [26–28]

Data storage

The digitization of the energy industry involves the use of numerous sensors that generate vast amounts of data daily. Addressing the storage and protocol of this data is a crucial issue to maximize the benefits derived from the collected information [16, 18, 19, 29, 30]

Development issues

An inherent challenge in development revolves around constructing a Big Data analytics system [31]. The pivotal issue is whether to scale down existing algorithms and systems or create new ones, all the while ensuring a specific level of accuracy and precision in the generated outputs [32]

Cybersecurity

Increasing utilisation of advanced digital systems and connectivity exposes oil and gas industry to greater cybersecurity risks. Oil and gas companies have been investing huge amount to protect their system from any security breaches. Such breach could lead a company to disruptions, safety concerns, potential environmental hazards and leaking of confidential information etc.

2.4

Addressing the Challenges of the Industry with AI Solutions

The progress in applying AI to the oil and gas industry has been relatively limited, though there is some evidence that some of the companies like Saudi Aramco, BP, Shell utilized some of the AI models to address the problems and boost overall equipment effectiveness. The intricate nature of the oil and gas industry introduces a multitude of challenges that can potentially be mitigated through the application of artificial intelligence solutions. Few of the examples are shown in below Table 3. Implementing AI solutions in the oil and gas industry requires careful planning, data integration, and collaboration between domain experts and AI specialists. It can

Application of Artificial Intelligence in the Oil and Gas Industry

347

Table 3 Application of AI in oil and gas industry Challenge Exploration and reservoir management

Production optimization

AI solution Seismic data analysis

AI algorithms can assist drilling companies to enhance the analysis of seismic data, which will help to identify potential drilling locations and estimating more accurately reservoir properties

Reservoir modelling

In complex reservoir analysis, AI helps in creating complex reservoir models by integrating various data sources and updating models in real-time

Predictive maintenance

By preventing unwanted downtimes and expansive repairs as a result of human mistake, maintenance prediction can enhance productivity tenfold [33]. Predictive maintenance helps to monitor and analyse real-time data and predict any issues, where AI can play a good role to predict asset failures and maintenance needs, reducing downtime and operational costs. The equipment has exhibited a 5% increase in uptime as a result of the adoption of predictive maintenance using advanced analytics tools [34]

Production forecasting

AI models can be used to forecast production rates based on historical data, repair history, operating parameters, and current operational conditions, aiding in resource planning. Chakra et al. [35] used neural networks to predict the oil production rate (continued)

348

M. Hussain et al.

Table 3 (continued) Challenge

AI solution

Health, safety, and environment (HSE)

Risk assessment

AI can be a good tool to analyse previous historical data and live real-time data to identify potential safety hazards and proposed mitigation measures. With the continued deployment of AI, risk assessment and management will become more dynamic since AI dramatically enhances the ability to locate information that is relevant to a given risk by analysing enormous amounts of data [36]

Supply chain management

Demand forecasting

Some of the oil and gas companies start using AI algorithms to analyse market trends, weather patterns, and geopolitical factors to forecast demand and optimize supply chain logistics. This is really helping to assist production team on customer requirements and support to do a planned shutdowns to maintain their assets in well planned manner

Inventory management

Spares in the oil and gas is a major issue, especially after Covid19, most the companies closed their business which created big gap to supply spares to oil and gas companies. Lead time and required stock assessment with more correct data is really required in the oil and gas industry where AI can optimize inventory levels, reducing surplus and preventing shortages

Energy consumption optimization

To analyse energy consumption, AI can read the patterns and suggest strategies to minimize energy usage, as well as reducing carbon emissions

Energy efficiency and sustainability

(continued)

Application of Artificial Intelligence in the Oil and Gas Industry

349

Table 3 (continued) Challenge

AI solution Carbon footprint reduction As most companies around the world are working towards Green Energy solutions. AI can help them to identify areas where emissions can be reduced and suggest operational changes or suggest an alternative technology

Data management and analytics

Data integration

As discussed earlier in this chapter, oil and gas sector is collecting data from multiple resources, integrates this data and provides a comprehensive view of operations and changes in the process (KPIs). AI can assist to analyse the pattern and provide insights to take proactive action on the changes in the process etc

Regulatory compliance

Regulatory monitoring

Due to safety and few other factors, Oil and Gas have few regulatory requirements that need to be comply with the requirements. AI can greatly help to continuously monitor operations and compare current operating values to regulatory standards, alerting operators to potential violations

result in improved operational efficiency, reduced costs, enhanced safety, and increased sustainability, contributing to the industry’s overall resilience and success.

2.5

Overview of Artificial Intelligence

AI involves the development of computer systems capable of executing tasks that traditionally rely on human intelligence. It is an interdisciplinary field that combines elements of computer science, mathematics, statistics, and cognitive science to design intelligent machines with the ability to learn, reason, and solve problems. AI seeks to emulate human intelligence through data analysis, pattern recognition, and decision-making based on available information. AI has revolutionized the way of the industry in generating and utilizing information for decision-making. It has also brought about significant changes

350

M. Hussain et al.

across various industries. These advancements have greatly influenced industries and management practices, resulting in the development of sustainable products and services. The collaboration between technologies and human intelligence relies on algorithms that aim to assist business decision makers in making decisions. This has led to a shift where a vast amount of data, connections and interactions have become parts of standard organizational management [37]. Mathematical models employed by AI simplify tasks by providing organized and categorized information. In fact, previous research has even demonstrated that these models are often more effective than decision-making, in scenarios [38]. Artificial Intelligence has garnered substantial attention in recent years owing to its potential to revolutionize diverse industries, including the oil and gas sector. While integration of AI in the oil and gas industry is a relatively recent development, it has quickly gained momentum and is being adopted to enhance operational efficiency, reduce repair and maintenance costs, and optimize decision-making processes. In the 1990s and early 2000s, AI technologies, such as expert systems, were first applied in the oil and gas industry. Expert systems were used for tasks like well log interpretation, production optimization, and reservoir modeling. The market size for AI in the oil and gas industry is projected to be USD 2,380.17 million in 2023 and USD 4,211.55 million in 2028, rising at a CAGR of 12.09% over the forecast period (2023–2028) [38]. The industry has witnessed the prominence of data-driven AI techniques, propelled by advancements in computing power and the abundance of available data. Machine learning algorithms started being used to analyze vast datasets collected from various sources, including sensors, drilling logs, and seismic data. These algorithms enabled predictive maintenance, anomaly detection, and optimized production strategies [9, 39].

2.6

Artificial Intelligence Techniques

With the advent of technologies and advanced analytical approaches, companies can achieve outcomes by making data-driven decisions. As explained by researchers [40] in methods of extracting petroleum, there are three types of models: • mathematical, • physical, and • empirical. Mathematical models rely on principles such as mass, energy, or momentum conservation, whereas empirical models are constructed based on observations and experiments. However, due to limitations in models under operating conditions and the inaccuracies associated with empirical modelling, these models often require assumptions and struggle to handle complex relationships, noise, and missing data. In the realm of petroleum exploration and production sites, a significant amount of data is generated daily through

Application of Artificial Intelligence in the Oil and Gas Industry

351

operations. Data mining techniques along with data analytics are being employed to extract insights from these datasets. The benefits derived from various methods have resulted in an improvement in oil production ranging from 6 to 8% [41]. Additionally, a 2018 survey revealed that 81% of executives identified the deployment of Big Data as one of their top three priorities for oil and gas companies, according to General Electric and Accenture. Furthermore, in a study conducted in 2016 [42], it was noted that the petroleum industry has come to rely on Big Data as a component. This section will delve into the application of AI and ML techniques in research endeavors that revolve around data-driven decision-making. Initially, we will explore the utilization of machine learning models and analytical tools within the oil and gas sector. Subsequently, we will examine areas where these techniques have found utility and how they can be harnessed to extract insights from data for effective decision-making purposes. We will also investigate how artificial intelligence techniques are employed in studying surface petrophysical properties, exploration activities, drilling operations reservoir analysis, production processes, as well as oil and gas transportation, via pipelines. In today’s oil and gas industry, there is a growing use of sensor-based tools that gather an amount of unstructured data. Various modeling techniques are employed to analyse this data and bring about improvements, in aspects, such as enhancing reservoir production capacity and predicting extreme events. However, despite these advancements, there seems to be some reluctance in adopting these methods. Several plausible reasons for this can be identified. (a) Many universities and institutions do not include data analytics in their petroleum engineering curriculum, which makes it challenging for petroleum engineers to embrace these methods [43]. (b) With the rise of oil and gas fields the threat of cyber-attacks has become more prominent. The importance of having cyber security specialists emphasized in oil and gas companies to safeguard their assets. (c) Another issue revolves around data ownership and the control of enterprise software used for recording, processing, and analyzing data [44]. Big Data services in the oil and gas industry had a market value of $5.41 billion in 2020. Automatically collecting data from wells and organizing them into subsets, with distinct characteristics allows for the identification of trends and informed decision making. As per a 2019 Deloitte Analysis Report [45], Shell employs data analysis to oversee its operational and supply chain efficiency, while ONGC is strategizing to harness data analysis for enhancing production output and efficiency. Halliburton Services is actively integrating Big Data tools into its operations to enhance drilling and well planning in the field [46]. Additionally, Intertek Laboratories has entered into a partnership with Robert Gordon University UK to conduct research aimed at helping oil and gas companies leverage data to achieve improved asset utilization, increased productivity, cost reduction, and enhanced safety in their activities. According to a 2016 survey conducted by Accenture Consulting,

352

M. Hussain et al.

most of the oil and gas industry executives, specifically 56%, expressed their belief that prioritizing data utilization would be of paramount importance in the upcoming 3–5 years.

3

AI Applications in Upstream Operations

Upstream operations is most capital centered by comparing to midstream and downstream in the oil and gas industry [47] due to its operations with heavy equipment. To monitor day to day operations, sensors are installed on these complex assets. These sensors collect data after every second, however most of the oil and companies are not utilizing or getting insights from this large amount of data. To get best from this data, AIincluding machine learning can assist upstream industry to better understand the asset utilization, performance, unwanted downtimes, reduce maintenance cost, enhance safety and less stress on the engineers and operators. For instance, Temirchev et al. [48] utilized machine learning techniques to identify calculations in conjunction with traditional reservoir modeling tools. The tools they used solved partial differential equations mathematically, illustrating the mechanics of reservoir streams. The 3D matrix, which typically has between one million and several billion cells, was used to implement the calculations. Even with the cutting-edge HPC workstations and servers limiting the number of possible runs, the calculations took a while. One of the obvious bearings for AI advancements is the acceleration of reservoir modeling. Modern proxy reservoir models bundle the numerical issue measurement and predict the time subordinates with another calculation motor depending on deep ML networks, ensuring speedups of 100–1000 times the regular models while maintaining comparable utility [49]. Table 4 shows some examples of how AI can assist upstream operations to increase production.

4

AI Applications in Midstream Operations

In the oil and gas industry, midstream operations hold importance as they handle the transportation, storage, and distribution of hydrocarbon products. As the industry strives to optimize its operations and improve efficiency while prioritizing safety, AI has emerged as a game-changing technology. The application of AI in midstream operations has brought about a revolution in the oil and gas industry by offering productivity, improved safety measures and proactive decision-making capabilities. Through maintenance monitoring pipelines optimizing transportation and logistics as well as implementing safety and security measures AI empowers midstream companies to streamline their operations, reduce costs and minimize their environmental footprint. With advancements in this field the future holds potential for AI to continue transforming the industry towards a safer, more efficient, and sustainable midstream sector.

Application of Artificial Intelligence in the Oil and Gas Industry

353

Table 4 Examples of AI is used in upstream operations Benefits

Details

Exploration and seismic data analysis AI algorithms can help to analyse seismic data to identify potential hydrocarbon reservoirs. Machine learning techniques can help interpret seismic images, detect anomalies, and predict the likelihood of finding oil and gas deposits Drilling optimization

AI can be used to optimize drilling operations by analysing real-time data from drilling sensors and making predictive models. It can provide insights into the drilling process, optimize drilling parameters, and enhance drilling efficiency

Reservoir modelling and simulation

AI techniques can be employed to create accurate reservoir models and simulate fluid flow within the reservoir. This enables engineers to make informed decisions about reservoir management, production strategies, and well placement

Production optimization

AI algorithms can analyse production data and identify patterns or anomalies to optimize production rates and minimize downtime. By combining historical data with real-time sensor data, AI systems can detect potential equipment failures, recommend maintenance actions, and optimize production processes

Predictive maintenance

AI can analyse sensor data from production equipment, such as pumps, turbines, fans, compressors etc. to predict anomalies and assist maintenance team to take proactive actions to avoid any unplanned downtimes. By monitoring equipment health in real-time, AI systems can help to schedule planned repair activities proactively, reducing downtime and optimizing asset performance

Well integrity and safety

AI can help monitor well integrity by analysing data from sensors installed in wells. It can detect anomalies such as pressure or temperature variations, gas leaks, or casing failures, enabling early intervention to prevent accidents and ensure safe operations (continued)

354

M. Hussain et al.

Table 4 (continued) Benefits

Details

Environmental monitoring

AI can be used to monitor and manage environmental aspects of upstream operations. It can analyse sensor data to detect and mitigate potential environmental risks, such as leaks or spills. AI-powered systems can also assist in optimizing resource usage, reducing carbon footprint, and complying with environmental regulations

Data analytics and decision support

AI techniques, including machine learning and data analytics, can assist in data integration, visualization, and decision-making processes. By analysing vast amounts of data from multiple sources, AI systems can provide insights and recommendations to improve operational efficiency and optimize resource allocation

The integration of AI applications in midstream operations holds the potential to revolutionize the industry by offering insights, predictive analytics, and automation capabilities. This section illustrates exploring the state of the industry by highlighting types of AI applications utilized in midstream operations and their associated benefits. Realworld examples and case studies will be examined to provide an understanding of how AI is shaping the present and future of midstream operations. The midstream sector faces challenges in managing logistics, maintenance tasks, monitoring safety measures and complying with regulations. Traditionally these operations heavily relied on processes with approaches. However, with the integration of AI technologies, a new era of efficiency and safety has been ushered in. AI applications, including machine learning, natural language processing, and computer vision, are reshaping midstream operations by facilitating decision-making processes and streamlining day-to-day activities. The utilization of AI in midstream operations provides advantages, such as enhanced efficiency. AI algorithms play a role in midstream operations by optimizing asset performance and minimizing downtime through maintenance using real-time sensor data, and asset performance to forecast and prevent equipment failures. By implementing maintenance strategies, midstream companies can optimize asset performance, reduce downtime, and prioritize safety. Additionally, AI-powered systems are employed for pipeline monitoring and leak detection. These systems leverage real time data from sensors, drones, and satellite imagery to detect leaks promptly, monitor pipeline integrity continuously and identify risks. By utilizing these applications midstream companies can enhance safety measures while minimizing impact. Moreover, AI algorithms optimize transportation routes inventory management practices and supply chain logistics in the midstream sector. By analyzing data along with market trends and demand patterns AI systems enable resource allocation reducing costs and enhancing overall operational efficiency.

Application of Artificial Intelligence in the Oil and Gas Industry

355

The future potential for AI in midstream operations is immense offering opportunities for advancements, in productivity, safety measures, and environmental sustainability. As technology continues to progress advancements in AI algorithms, robotics and automation will play a role in improving efficiency, safety measures and environmental conservation. Overall, the integration of AI into the aspects of midstream operations brings forth benefits. These include improved efficiency, optimized asset management, detection of leaks or potential risks enabling efficient resource allocation and bolstering safety measures.

4.1

Pipeline Integrity Management Using AI Algorithms

The integrity of energy pipelines is paramount for ensuring reliable and safe operations, given the substantial investments required for their infrastructure. It serves as a crucial factor in safeguarding against the risks associated with degradation or deterioration [50]. Such risks could result in costly downtime, environmental hazards, and potential threats to human safety. In recent years, the surge in failure incidents has drawn attention to the integrity and condition assessment of gas pipelines [51–54]. Figure 1 below illustrates some of the causes of gas pipeline failures in Europe [55]. The concept of pipeline integrity encompasses themes related to the prevention of failures, inspection activities, and repair procedures. It also involves products, practices,

8% 16%

26%

6% 16%

Corrosion Construction defects/ Materials failures Ground Movement Fig. 1 Causes of gas pipeline failures

28%

External interference Hot Tap Other failures

356

M. Hussain et al.

and services that assist stakeholders in enhancing the condition and performance of their assets [54, 56, 57]. The integrity management strategy, which takes into account both the likelihood or probability of failure and the consequences of a failure, is oriented towards known risks [58]. The Pipeline Integrity Management System (PIMS) is a program designed to control risk and implement cost-control techniques, instruments, and activities to assess the health conditions of pipelines and plan inspection and maintenance activities [22]. To maintain the integrity of the pipeline, AI algorithms can play a significant role in enhancing pipeline integrity management by analyzing large amounts of data, identifying potential risks, and enabling proactive maintenance and decision-making. Below Table 5 shows few AI applications in energy pipeline integrity management.

5

AI Applications in Downstream Operations

Downstream operations in the oil and gas industry encompass a wide range of activities, including refining, distribution, and marketing. Artificial intelligence (AI) has various applications in downstream operations, which refer to the activities involved in the processing and refining of raw materials in the oil and gas industry. AI-based solutions are used in the downstream segment’s refining and processing processes as predictive maintenance. Refinery breakdowns and unscheduled outages can be detected using machine learning (ML) approaches, and steps can be recommended to increase asset lifetime and load while optimizing scheduled operation cycles. Additionally, ML algorithms are being used to improve refineries by making difficult decisions about which kinds of crude to run and how to improve conditions to maximize the yield of the most valuable products. Based on the constantly changing values of the products, these algorithms can modify refining activities in real-time [13]. Few of AI applications in downstream operations are discussed in below: 1. Refinery Optimization: AI can be used to optimize refinery operations by analyzing large volumes of data and identifying areas for process improvement. Machine learning algorithms can analyze historical data on process variables, such as temperature, pressure, and flow rates, to predict optimal operating conditions and improve energy efficiency. AI can also help in predicting equipment failures and optimizing maintenance schedules. 2. Process Monitoring and Control: AI-based systems can continuously monitor and control various processes in a refinery. These systems can use sensor data to detect anomalies, predict process deviations, and recommend corrective actions in real time. By leveraging machine learning algorithms, AI can adapt to changing conditions and optimize the control of complex processes.

Application of Artificial Intelligence in the Oil and Gas Industry

357

Table 5 Application of AI in pipeline integrity management Benefits

Brief Details

Predictive maintenance

To predict any anomaly and its consequences, AI algorithms can really help the pipeline operators to analyse real-time data by detecting early warning signs of potential integrity and reliability problems. Such early detection will assist the operators to schedule repair or planned maintenance proactively, which will reduce the risk of unplanned downtime and safety accidents

Anomaly Detection

AI algorithms can learn patterns from real time data and identify anomalies that deviate from normal integrity operating parameters. These anomalies may indicate leaks, corrosion, cracks, or other integrity threats. By continuously monitoring this real time data and applying machine learning techniques, AI algorithms can flag abnormal conditions and trigger alerts for immediate attention

Leak detection

AI algorithms have the capability to analyze data from diverse sources, including pressure, temperature, flow rates, and acoustic sensors installed on oil and gas pipelines. This enables them to detect and pinpoint the location of leaks in pipelines. By analyzing the sensor data and applying pattern recognition techniques, AI algorithms can identify leak signatures and differentiate them from normal operating conditions, enabling rapid response and minimizing environmental and safety risks

Risk assessment

AI algorithms can assess and prioritize the integrity risks associated with different pipeline sections, depending on integrity operating windows (IOWs). By considering factors such as material design properties, operational parameters, environmental parameters, maintenance and repair history, AI algorithms can generate risk profiles and support decision-making processes regarding inspection, maintenance, and required resource and budget (continued)

358

M. Hussain et al.

Table 5 (continued) Benefits

Brief Details

Decision Support

AI can assist operators in making informed decisions by processing and analyzing large amounts of data collected from different sources. By integrating data from inspection reports, sensor readings, historical maintenance records, and external sources. AI can provide insights and recommendations for optimizing maintenance schedules, resource allocation, and operational strategies

Data integration and visualization AI can assist in integrating data from various resources and provide a unified view of pipeline integrity. By developing and then utilizing advanced data visualization techniques, AI can present complex information in a user-friendly manner, enabling operators to identify trends, patterns, and potential integrity risks more efficiently

3. Supply Chain Optimization: AI can enhance supply chain operations by analyzing market data, demand forecasts, and historical trends. AI algorithms can optimize inventory levels, transportation routes, and scheduling of deliveries to ensure efficient and costeffective supply chain management. AI can also help in predicting demand fluctuations, optimizing product blending, and improving logistics planning. 4. Safety and risk management: Safety and risk management are critical considerations in downstream operations, and AI has proven invaluable in mitigating risks and enhancing safety measures. Predictive risk assessment models employ AI algorithms to identify potential hazards, quantify risks, and prioritize mitigation strategies. Real-time safety monitoring systems powered by AI analyze data from various sources, including video surveillance and sensor networks, to detect and alert for safety incidents or deviations from established safety protocols. This enables timely interventions and enhances overall safety across the operations. 5. Marketing and customer engagement: In marketing and customer engagement, AIdriven applications have enabled downstream companies to gain deeper insights into customer behavior and preferences. Customer segmentation models leverage AI algorithms to identify distinct customer segments and tailor marketing strategies accordingly. AI-powered pricing optimization models enable dynamic pricing strategies that maximize revenue and profitability while considering market dynamics and competitive factors. Personalized marketing campaigns driven by AI recommendations enhance customer engagement and improve overall marketing effectiveness. 6. Energy Management: AI can be utilized to optimize energy consumption and reduce energy costs in downstream operations. By analyzing energy usage patterns, AI algorithms can identify energy-saving opportunities, recommend energy-efficient operating

Application of Artificial Intelligence in the Oil and Gas Industry

359

strategies, and provide real-time energy management insights. This helps refineries and petrochemical plants minimize their environmental footprint and improve sustainability. 7. Quality Control: AI can assist in real-time quality control by analyzing sensor data and historical production data. By monitoring process variables, AI algorithms can detect deviations from the desired quality standards and trigger alerts for corrective actions. This helps ensure consistent product quality and reduces waste or rework. 8. Digital Twin: Digital twin involves creating virtual replicas of physical assets or processes. AI algorithms can integrate with digital twins to simulate and optimize refinery operations. This allows for testing different scenarios, identifying bottlenecks, and optimizing performance in a virtual environment before implementing changes in the physical refinery. 9. Environmental Impact Assessment: AI is also being applied to address environmental impact assessment challenges in downstream operations. Emissions monitoring systems leverage AI technologies to monitor and analyze emissions in real-time, enabling companies to identify and implement emission reduction strategies. Waste management and recycling processes are optimized using AI algorithms to minimize waste generation and enhance recycling efficiency, contributing to environmental sustainability. The application of AI in downstream operations has revolutionized the oil and gas industry. From process optimization and control in refineries to supply chain management, safety and risk management, marketing, and environmental impact assessment, AI-driven solutions have provided significant benefits such as improved operational efficiency, enhanced safety, optimized resource allocation, and better customer satisfaction. As technology continues to advance, the potential for AI to further transform downstream operations is immense, paving the way for a more sustainable and efficient industry.

5.1

Predictive Maintenance for Refining Processes

The oil and gas sector stands out as one of the largest and most intricate industries globally, involving processes such as exploration, extraction, refining, and the distribution of hydrocarbon resources. The advent of new technologies has led to a significant surge in data generation within this sector, giving rise to the concept of “big data.“ Big data refers to the substantial quantities of structured and unstructured data generated at a rapid pace, displaying considerable diversity. Big Data Analytics has become a crucial element in the ongoing digital transformation of the oil and gas industry [59]. Leveraging big data technology, the petroleum industry can manage massive volumes of data, as illustrated in Table 6 for example [60].

360

M. Hussain et al.

Table 6 The amount of data captured by various oil and gas sector Data collection section

Amount of data

Pipeline inspection

1.5 TB (600 km)

Plant operational

8 GB (annually)

Vibration

7.5 GB (per customer annually)

Plant process

4–6 GB (daily)

Submersible pump monitoring

0.4 GB/well (daily)

Drilling

0.3 GB/well (daily)

Wireline

5 GB/well (daily)

Seismic

100 GB – 2 TB/survey

The availability of data is increasing in oil and gas which is helping the owners to make wise decisions [61] like maintenance management [62] and continuous improvement [33, 63, 64]. AI can play a major role in predicting any issue, preventing equipment failures, and optimizing maintenance schedules. To make it more effective machine learning and AI models are more suitable for dealing with complex problems. Machine learning based PdM can be divided into two groups: (a) Supervised and (b) Unsupervised. Below Figure 2 shows brief process to how AI framework on the process of AI development and implementation. Benefits of predictive maintenance in the for refining processes using AI include: a. b. c. d. e. f. g. h.

Increased Equipment Reliability Reduce Maintenance and Repair Cost Improved Safety Enhanced Plant Efficiency Increase Asset Performance Increase Production Detect Early Anomaly Extended Equipment Lifespan.

Application of Artificial Intelligence in the Oil and Gas Industry

361

Fig. 2 AI Process in oil and gas processing facility

6

Safety and Environmental Applications of AI

In recent years AI has emerged as a tool for addressing safety concerns and environmental issues. The potential uses of AI in domains related to safety and the environment highlighting its ability to enhance risk management optimize resource utilization and enable decision making. AI algorithms have the capability to analyze datasets from sources like sensors, satellite imagery and historical records. By identifying patterns and predicting safety hazards well as environmental risks. For instance, they can monitor processes for anomalies in time and issue early warnings to prevent accidents while minimizing environmental harm. By utilizing machine learning algorithms, historical data and real-time information can be analysed to predict the occurrence and impact of disasters like floods, wildfires, and earthquakes that can impact pipeline integrity and increase the chance of oil and pipeline failures. This empowers authorities to take proactive action, efficiently manage the risk and allocate resources effectively to avoid any incident. Additionally, AI can optimize energy processes in industries leading to reduced footprints and improved sustainability.

362

6.1

M. Hussain et al.

AI-Powered Risk Assessment and Safety Monitoring

The oil and gas industry plays a role in meeting the energy demands but it also faces various safety risks and operational hazards. Ensuring the safety of personnel, assets and the environment is of importance in this industry. Operating in hazardous environments like platforms, refineries, chemicals, petrochemicals, fertilizers, and drilling sites makes the oil and gas industry face numerous high-risk activities including well drilling, transportation, and storage of hazardous materials. Accidents or incidents within this realm can lead to consequences such as loss of life, environmental damage, or economic losses. Therefore, having risk assessment systems along with robust safety monitoring is vital to minimize these risks while ensuring operations. When applied to risk assessment within the oil and gas sector, AI has the ability to analyse data from sources such as sensor data, historical records and operational parameters. This analysis helps in identifying hazards, determining their likelihood, and predicting their impact. The emergence of AI-powered systems has transformed safety monitoring by offering real time insights, predictive capabilities, and autonomous monitoring features. These systems utilize AI algorithms to analyse data gathered from sources like sensors, video feeds and operational parameters. The primary goal is to detect anomalies while identifying safety breaches promptly through timely alerts. With continuous monitoring of parameters enabled by AI driven safety systems ensures interventions are possible leading to reduced accident risks and improved overall safety performance. The integration of AI powered risk assessment techniques alongside safety monitoring, in the Oil and Gas industry presents benefits [6, 8, 65].

6.2

Environmental Impact Analysis and Mitigation Using AI

As the world grapples with the task of addressing climate change and promoting sustainability there is an increasing need for approaches within industry to reduce its impact on the environment. In times AI has emerged as a tool in analysing and mitigating environmental impacts in the oil and gas field. It explores applications, benefits and challenges associated with AI-powered environmental impact analysis and mitigation efforts [66]. The use of AI based techniques has brought about a transformation in the field of environmental impact analysis. These techniques enable us to process and analyse amounts of data from sources. With the help of machine learning algorithms AI can examine satellite images, sensor data and historical records to identify patterns in the environment, evaluate risks and predict the impacts associated with activities. By uncovering relationships AI models provide insights that support informed decision making. By utilizing AI algorithms and advanced analytics this industry can optimize resource allocation, enhance efficiency, and reduce emissions.

Application of Artificial Intelligence in the Oil and Gas Industry

363

The utilization of AI technology for analysing and mitigating impacts holds great promise for the oil and gas industry. By leveraging AI capabilities, the industry can enhance its practices of assessing impacts, optimize resource utilization and effectively implement mitigation measures.

7

Challenges and Limitations of AI in the Oil and Gas Industry

The incorporation of AI technologies in the oil and gas sector holds the potential for optimizing operations, improving efficiency, and enhancing decision-making processes. However, it is important to acknowledge that AI also faces challenges and limitations within this industry. Additionally, limitations in accessing data may arise due to concerns about information and data privacy regulations well as difficulties in ensuring compatibility between different systems and software platforms. The other challenges involve integrating and harmonizing data sources. In the oil and gas industry, data is often compartmentalized within systems using formats, which makes it difficult to aggregate comprehensively for analysis purposes. AI algorithms depend on integrating types of data like production data to generate accurate and meaningful insights. However, merging data from sources can present challenges. Compatibility issues between systems and modern AI platforms can also emerge. Interpretability and explainability are factors when considering AI algorithms that employ learning techniques. The lack of transparency in AI models raises concerns regarding bias, reliability and accountability which can impede stakeholders’ acceptance of AI solutions [67–69]. Creating AI models for the oil and gas sector necessitates a grasp of its obstacles, workflows, and established methods. The process of imparting domain knowledge to AI systems necessitates collaboration among data scientists, engineers, and subject matter experts, however, there is a shortage of individuals who possess both expertise in AI and deep understanding of the oil and gas industry. The oil and gas industry is inherently filled with uncertainties and risks spanning from exploration and production to market fluctuations. AI models might face difficulties in handling uncertainty and accurately quantifying risks associated with systems. While techniques like Monte Carlo simulations and probabilistic modeling can address aspects of uncertainty, the unpredictable nature of the oil and gas industry continuously presents challenges when it comes to precise risk assessment and management. Moreover, it is necessary to evaluate concerns related to the oil and gas industry so that AI technologies are not used in ways that worsen risks. While there are advantages offered by AI in the oil and gas industry it is important to recognize the challenges and limitations that come with its implementation [70–72].

364

7.1

M. Hussain et al.

Data Quality and Availability Challenges

Data quality is defined by its alignment with the intended application; in other words, data possesses quality if it meets the requirements of its desired use. Conversely, poor data quality arises when it fails to meet these requirements. Another perspective is that data quality is as much determined by its intended use as by the inherent characteristics of the data itself. For data to fulfill its intended purpose, it must exhibit qualities such as accuracy, timeliness, relevance, completeness, understandability, and trustworthiness [73]. Data quality has emerged as a significant concern in a world captivated by the expansive possibilities of AI, including machine learning for predictive analysis. Businesses are under pressure as they rely on reliable data to deliver personalized experiences yet grapple with the challenge of dealing with inaccurate data. Whether it’s pipeline owners/ operators or other enterprises, there’s a collective desire to make informed decisions using big data applications. Data serves as the bedrock for ensuring assurance and trust, essential for decision-making and enhancing oil and gas asset management systems. However, the downside is that data can be misleading; any flaw in data or its processing poses a potential danger to achieving the goals that drive an organization’s daily operations [74]. Figure 3 below is an illustrative example of poor data quality. The figure showcases the data quality of In-Line Inspection (ILI) data in three distinct ILI runs on a gas pipeline. Evidently, significant errors were present in the interpretation of ILI tools, highlighting issues with data accuracy and quality [75, 76]. To develop and implement successful predictive analytics system in the oil and gas sector using AI, it involves several data quality challenges that can affect the accuracy, reliability, and ethical implications of the models. Due to complexity of the asset and process, oil and gas sector collected data from different resources and these sources could be from different stakeholders, resources, or systems. These different resources may have varying data formats, structures, or quality standards, leading to inconsistencies and compatibility issues. One-shot learning involves enhancing an AI model by pre-training it on a comparable dataset and incorporating the expertise of researchers [77]. Fig. 3 Growth paths of individual anomalies determined by ILI measurement

Application of Artificial Intelligence in the Oil and Gas Industry

7.2

365

Regulatory and Security Concerns

There are many applications of artificial intelligence in the oil and gas industry, however, it also raises certain regulatory and security concerns that need to be addressed. Few key considerations are shown in Table 7.

8

Future Trends and Outlook

Artificial intelligence is rapidly becoming dominant across industries presenting opportunities for growth and innovation. It refers to machines performing tasks that require intelligence, such as speech recognition, learning and problem solving. The utilization of AI solutions than human centric approaches enable companies to enhance efficiency and create value in the oil and gas industry. Although the oil and gas industries have been relatively slow in adopting digitalization, they are increasingly relying on AI to improve data handling and processing capabilities through machine learning. Among the used technologies, in the oil and gas industry, are Fuzzy Logic, Artificial Neural Networks and Genetic Algorithms [60, 85–87].

8.1

Potential Impact of AI on the Oil and Gas Future

AI has become a force, across industries, including the oil and gas sector. The current trends in the oil and gas industry are witnessing advancements in areas such as data analytics, automation, and digitalization. Key trends that are influencing this industry include adopting remote operations capabilities embracing analytics and integrating devices. These trends provide a foundation for implementing AI solutions that can effectively utilize volumes of data to enhance decision-making processes and drive operational excellence [88, 89]. AI possesses capabilities within the industry through: (a) Advancing Exploration and Production: Through algorithms AI enables analysis of data, alongside surveys and historical drilling records to optimize exploration efforts while boosting production efficiency. By identifying opportunities to predict reservoir behavior accurately and improving drilling methods AI has potential to increase efficiency while reducing costs. (b) Predictive Maintenance and Asset Management: With the assistance of AI driven maintenance techniques equipment can be monitored in time to anticipate failures proactively. (c) Safety Improvement: AI applications such as computer vision and video analytics can monitor work sites, identify safety hazards, and ensure compliance with safety

366

M. Hussain et al.

Table 7 Regulatory and Security concerns using AI Challenge

Challenge brief explanation

Regulatory compliance

There are strict regulatory compliance requirements that encompasses safety, environmental protection, and operational standards that oil and gas industry need to comply. Before developing and implementing AI technologies in this sector, plant owners must think and ensure compliance with relevant regulations and standards, such as those set by regulatory bodies

Cybersecurity

The interconnected nature of AI systems heightens vulnerability to cyber threats. As the oil and gas industry experiences a rise in attacks, safeguarding systems from external threats, such as third-party damages, becomes imperative. Initiatives like PipeSecure2020 have been launched to establish higher levels of protection for gas pipelines [78]. Nevertheless, the successful implementation of such initiatives requires more advanced systems capable of detecting suspicious activities and triggering alarms for immediate action

Data privacy and protection Privacy stands out as a particularly delicate issue, encompassing conceptual, legal, and technological implications. This concern amplifies its significance, especially within the context of big data [26–28, 79]. Rethinking security for information sharing in big data use cases is another extremely important direction to pursue [28, 70, 80–82]. Securing data and information throughout the entire transmission process, from the sending node through the communication connection, network, and up to the receiving node, requires global identification and end-to-end data encryption when sharing information over the internet [71, 83, 84] Ethical use of AI

AI algorithms should be designed and deployed in an ethical manner, considering potential biases and discriminatory outcomes. Transparency and accountability in AI decision-making processes are crucial for building public trust and ensuring fairness in various areas, such as hiring practices, resource allocation, and risk assessment

Workforce impact

The integration of AI in the oil and gas industry may potentially result in workforce displacement or alterations in job roles and skill requirements. It is imperative to consider the impact on employees, offer sufficient training and reskilling opportunities, and ensure a seamless transition to new job roles or industries Recent ChatGPT have some negative impact on people at workplace. For example, people start using ChatGPT while writing emails or reports. Most of the companies in Australia ban ChatGPT in their offices due to misuse of the AI application

Application of Artificial Intelligence in the Oil and Gas Industry

367

protocols. By strengthening incident prevention measures and response capabilities, AI contributes to creating a working environment. (d) Environmental Impact Mitigation: AI can analyze the impact of operations by monitoring emissions and detecting leaks. This proactive approach allows for interventions that comply with regulations while promoting sustainability and minimizing the industry footprint. Overall, the potential impact of AI on the oil and gas industry is tremendous. By leveraging AI technologies, the industry can achieve efficiency gains, enhance safety protocols minimize impact and promote practices. As the oil and gas industry continues its journey AI emerges as a tool capable of revolutionizing processes while providing significant cost savings.

8.2

Opportunities for Further Research and Development

Research and development hold importance in propelling innovation and progress across industries. In our changing world, where technology is revolutionizing established norms, there exist avenues for further research and development across different sectors. This section delves into the state of research and development, identifies prospects obstacles, and offers recommendations on how to effectively capitalize on these opportunities. By presenting insights and data our intention is to underscore the significance of their endeavors and their potential to shape tomorrow. The present scenario of research and development in initiatives has gained momentum due to technological advancements, wider data accessibility and the demand for groundbreaking solutions. Sectors like healthcare, energy, manufacturing, and information technology have witnessed investments in research and development leading to discoveries and transformative technologies. However, there still lies potential with room for further exploration. There are several key prospects for advancing research and development including: (a) Emergent Technologies: The swift progress of technologies such as Artificial Intelligence, Internet of Things, blockchain and quantum computing opens up possibilities for research and development investigations. Exploring the potential and possibilities of these technologies can lead to groundbreaking discoveries and transformative solutions. (b) Sustainability and Renewable Energy: Urgent actions are needed to address climate change and transition towards practices. Extensive research and development in energy sources, energy storage and carbon capture technologies can revolutionize energy production while minimizing impact.

368

M. Hussain et al.

(c) Biotechnology: The field of healthcare and biotechnology holds promise for improving treatments, disease prevention and personalized medicine. Areas like genomics, precision medicine and regenerative therapies offer avenues for exploration and game-changing discoveries. (d) Cybersecurity and Data Privacy: As industries become increasingly digitalized it is crucial to invest in research efforts aimed at developing cybersecurity measures while safeguarding data privacy. Exploring encryption techniques secure communication protocols as emerging threats can strengthen our digital resilience. Despite of all the benefits, however, there are big challenges in this field such as: (a) Funding and Resources: Adequate funding support along with resources are vital for research endeavors. Collaborative efforts between governments, private sector organizations and academic institutions are essential to ensure investment in research and development projects while providing researchers with the required support. (b) Collaboration and Knowledge Sharing: Encouraging collaboration among researchers across institutions and industries is key to leveraging collective expertise for fostering innovation. Open initiatives that promote the sharing of knowledge and data can expedite progress, overcome barriers between areas of research. (c) Ethical considerations are of importance as researchers explore frontiers. They must navigate frameworks addressing potential biases and ensure responsible innovation that benefits society. We can maximize opportunities through several steps as follows: a. Encourage collaboration across disciplines by fostering partnerships between academia, industry, and government agencies. This will bring together perspectives and expertise. It is also essential to establish funding mechanisms that incentivize research and development projects. b. Invest in education and talent development by supporting programs that cultivate scientific curiosity and equip researchers with the necessary skills. Collaboration between academia and industry can bridge the gap between knowledge and practical applications [6, 90]. c. Strengthen research infrastructure by enhancing facilities such as laboratories, data centers and computational resources. This will facilitate experimentation data analysis and simulation-based research. d. Foster international cooperation by encouraging collaboration through research networks, partnerships, and joint projects. By working on shared challenges that transcend boundaries we can accelerate progress.

Application of Artificial Intelligence in the Oil and Gas Industry

369

Overall, research and development play a role in driving innovation, across industries. Embracing the technologies tackling sustainability issues advancing healthcare solutions and ensuring cybersecurity measures are just a few examples of the wide array of possibilities for further research and development. By overcoming obstacles promoting collaboration and investing in individuals and resources societies can unlock the potential of research and development and shape a more promising future.

References 1. Kaplan, A. M. (2012). If you love something, let it go mobile: Mobile marketing and mobile social media 4x4. Business horizons, 55(2), 129–139. 2. Kaplan, A. M., & Haenlein, M. (2016). Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster. Business horizons, 59(4), 441–450. 3. Hussain, M., et al. (2022). Impact of Covid-19 and needs of digital transformation to protect assets from corrosion. Corrosion Management, 165, 31. 4. Al Dhaif, R., A.F. Ibrahim, and S. Elkatatny, Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques. Journal of energy resources technology, 2022. 144(3). 5. Leal Filho, W., et al. (2023). Deploying digitalisation and artificial intelligence in sustainable development research. Environment, development and sustainability, 25(6), 4957–4988. 6. Waqar, A., et al., Applications of AI in oil and gas projects towards sustainable development: a systematic literature review. Artificial Intelligence Review, 2023: p. 1–28. 7. Di Vaio, A., et al. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. 8. Pishgar, M., et al. (2021). REDECA: A novel framework to review artificial intelligence and its applications in occupational safety and health. International journal of environmental research and public health, 18(13), 6705. 9. Iliinskij, A., et al. Digitalization of the oil and gas research infrastructure. in XIV International Scientific Conference “INTERAGROMASH 2021” Precision Agriculture and Agricultural Machinery Industry, Volume 1. 2021. Springer. 10. Company, T.B.R., Crude Oil Global Market Report Jan 2023. 11. Bhutada, G. How Big is the Market for Crude Oil? June 2023. 12. Administration, U.S.E.I. What countries are the top producers and consumers of oil? May 2023 [cited 2023 July]; Available from: https://www.eia.gov/tools/faqs/faq.php?id=709&t=6. 13. Brewer, D. The Role of AI in the Oil and Gas Value Chain - Midstream and Downstream Segment. [cited 2023 July]; Available from: https://www.citationcompliance.com/blog/role-of-ai-in-theoil-and-gas-value-chain-midstream-downstream-segment. 14. Deloitte, O. and G.R. Check, A look at the top issues facing the oil and gas sector. 2015. 15. Neri, P. Big data in the digital oilfield requires data transfer standards to perform. in Offshore Technology Conference. 2018. OTC. 16. Gidh, Y., et al. WITSML v2. 0: Paving the Way for Big Data Analytics Through Improved Data Assurance and Data Organization. in SPE Intelligent Energy International Conference and Exhibition. 2016. OnePetro. 17. Maidla, E., et al. Drilling analysis using big data has been misused and abused. in IADC/SPE Drilling Conference and Exhibition. 2018. OnePetro.

370

M. Hussain et al.

18. Beckwith, R. (2011). Managing Big Data: Cloud computing and co-location centers. Journal of Petroleum Technology, 63(10), 42–45. 19. Mounir, N., et al. Integrating Big Data: simulation, predictive analytics, real time monitoring, and data warehousing in a single cloud application. in Offshore technology conference. 2018. OnePetro. 20. Handscomb, C., S. Sharabura, and J. Woxholth, The Oil and Gas Organization of the Future. . 2016. 21. Preveral, A., A. Trihoreau, and N. Petit. Geographically-distributed databases: A big data technology for production analysis in the oil & gas industry. in SPE Intelligent Energy International Conference and Exhibition. 2014. SPE. 22. Xie, M., & Tian, Z. (2018). A review on pipeline integrity management utilizing in-line inspection data. Engineering Failure Analysis, 92, 222–239. 23. Nguyen, T., R.G. Gosine, and P. Warrian, A systematic review of big data analytics for oil and gas industry 4.0. IEEE access, 2020. 8: p. 61183–61201. 24. Gu, J., et al. The Application of the Big Data Algorithm for Pipeline Lifetime Analysis. in 2019 Chinese Automation Congress (CAC). 2019. IEEE. 25. Tole, A. A. (2013). Big data challenges. Database systems journal, 4(3), 31–40. 26. Kaisler, S., et al. Big data: Issues and challenges moving forward. in 2013 46th Hawaii international conference on system sciences. 2013. IEEE. 27. Gow, G.A. Privacy and ubiquitous network societies. in ITU Workshop on Ubiquitous Network Societies. 2005. 28. Moreno, J., Serrano, M. A., & Fernández-Medina, E. (2016). Main issues in big data security. Future Internet, 8(3), 44. 29. Neri, P. Big data in the digital oilfield requires data transfer standards to perform. in Offshore Technology Conference. 2018. OnePetro. 30. Mohammadpoor, M., et al., Big Data analytics in oil and gas industry: An emerging trend, Petroleum (2019). 10.1016/j. petlm, 2018. 1. 31. Thiyagalingam, J., et al. (2022). Scientific machine learning benchmarks. Nature Reviews Physics, 4(6), 413–420. 32. Kaisler, S., et al., Big Data and Analytics: Issues and Challenges for the Past and Next Ten Years. 2023. 33. Susto, G. A., et al. (2014). Machine learning for predictive maintenance: A multiple classifier approach. IEEE transactions on industrial informatics, 11(3), 812–820. 34. Lohr, S., GE, the 124-year-old software start-up. The New York Times, 2016. 27. 35. Chakra, N. C., et al. (2013). An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). Journal of Petroleum Science and Engineering, 106, 18–33. 36. Chan, A. Can AI Be Used for Risk Assessments? 2023 [cited 2023 18 August]. 37. Hussain, M., & Zhang, T. (2023). Potential of Big Data Analytics for Energy Pipeline Integrity Management. Corrosion Management, 171, 31. 38. Mordor. Global ai in oil and gas market size & share analysis - growth trends & forecasts (2023 - 2028). [cited 2023 July ]. 39. Wang, X. (2017). Application of artificial intelligence in oil and gas industry. Mod Inf Technol, 3(1), 117–119. 40. Balaji, K., et al. Status of data-driven methods and their applications in oil and gas industry. in SPE Europec featured at 80th EAGE Conference and Exhibition. 2018. OnePetro. 41. Hamzeh, H., Application of big data in petroleum industry. Department of Electronics and Computer Engineering Istanbul Sehir University hamedhamzeh@ std. sehir. edu. tr, 2016.

Application of Artificial Intelligence in the Oil and Gas Industry

371

42. Saputelli, L. (2016). Technology focus: Petroleum data analytics. Journal of Petroleum Technology, 68(10), 66–66. 43. Mohaghegh, S.D. and S.D. Mohaghegh, Shale analytics. 2017: Springer. 44. Feblowitz, J. Analytics in oil and gas: The big deal about big data. in SPE Digital Energy Conference. 2013. OnePetro. 45. Daneeva, Y., et al. Digital transformation of oil and gas companies: energy transition. in Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020). 2020. Atlantis Press. 46. Cowles, D., Oil, gas, and data: high-performance data tools in the production of industrial power. 2015: O’Reilly Media. 47. Shafiee, M., et al. (2019). Decision support methods and applications in the upstream oil and gas sector. Journal of Petroleum Science and Engineering, 173, 1173–1186. 48. Temirchev, P., et al. (2020). Deep neural networks predicting oil movement in a development unit. Journal of Petroleum Science and Engineering, 184, 106513. 49. Gupta, D., & Shah, M. (2022). A comprehensive study on artificial intelligence in oil and gas sector. Environmental Science and Pollution Research, 29(34), 50984–50997. 50. Hussain, M., T. Zhang, and M. Seema, Adoption of big data analytics for energy pipeline condition assessment. International Journal of Pressure Vessels and Piping, 2023: p. 105061. 51. Ahmad, W., et al. (2018). Formal reliability analysis of oil and gas pipelines. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(3), 320–334. 52. Aronu, O.K., Integrity Management In The Energy Sector-An Investigation of Oil & Gas Assets. 2017, NTNU. 53. Ramasamy, J. and M.Y. Sha’ri, A literature review of subsea asset integrity framework for project execution phase. Procedia Manufacturing, 2015. 4: p. 79–88. 54. Hussain, M., et al. (2021). Application of big data analytics to energy pipeline corrosion management. Corrosion management, 2021, 28–29. 55. Mora, R. G., et al. (2016). Pipeline integrity management systems: A practical approach (Vol. 374). ASME Press. 56. Kishawy, H. A., & Gabbar, H. A. (2010). Review of pipeline integrity management practices. International Journal of Pressure Vessels and Piping, 87(7), 373–380. 57. Jiang, T., et al. (2017). Application of FBG based sensor in pipeline safety monitoring. Applied Sciences, 7(6), 540. 58. Palmer-Jones, R., S. Turner, and P. Hopkins. A new approach to risk based pipeline integrity management. in International Pipeline Conference. 2006. 59. Sivarajah, U., et al. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, 263–286. 60. Lu, H., et al. (2019). Oil and Gas 4.0 era: A systematic review and outlook. Computers in Industry, 111, 68–90. 61. Platforms, G. E. I. (2012). The rise of industrial big data 62. Susto, G. A., et al. (2013). A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation. In 2013 IEEE international conference on automation science and engineering (CASE). IEEE. 63. Susto, G. A., et al. (2015). Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach. Computers & Operations Research, 53, 328–337. 64. Köksal, G., Batmaz, I., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert systems with Applications, 38(10), 13448–13467.

372

M. Hussain et al.

65. Afzal, F., et al. (2021). A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects. International Journal of Managing Projects in Business, 14(2), 300–328. 66. Hojageldiyev, D. Artificial Intelligence Opportunities for Environmental Protection. in SPE Gas & Oil Technology Showcase and Conference. 2019. OnePetro. 67. Tariq, Z., et al. (2021). A systematic review of data science and machine learning applications to the oil and gas industry. Journal of Petroleum Exploration and Production Technology, 1–36. 68. Hanga, K. M., & Kovalchuk, Y. (2019). Machine learning and multi-agent systems in oil and gas industry applications: A survey. Computer Science Review, 34, 100191. 69. Sircar, A., et al. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379–391. 70. Mohammadpoor, M., & Torabi, F. (2020). Big Data analytics in oil and gas industry: An emerging trend. Petroleum, 6(4), 321–328. 71. Phuyal, S., Bista, D., & Bista, R. (2020). Challenges, opportunities and future directions of smart manufacturing: A state of art review. Sustainable Futures, 2, 100023. 72. Ghodke, P. K., et al. (2023). Artificial Intelligence in the digital chemical industry, its application and sustainability. Recent Trends and Best Practices in Industry, 4, 1. 73. Olson, J. E. (2003). Data quality: The accuracy dimension. Elsevier. 74. Loshin, D. (2010). The practitioner’s guide to data quality improvement. Elsevier. 75. Hussain, M., & Zhang, D. T. (2020). Pipeline integrity management system (PIMS): An overview. 76. Muhammad Hussain, A. H., Zhang, T., & Nasser, M. (2021). The importance of data quality in energy pipelines condition assessment. In IMA international conference on modelling in industrial maintenance and reliability (MIMAR). 77. Weyrauch, T., & Herstatt, C. (2017). What is frugal innovation? Three defining criteria. Journal of frugal innovation, 2(1), 1–17. 78. Schmidt, J. (2019). Plant security-public awareness and mitigation of third party attacks as a new layer of protection in the safety concept. Chemical Engineering Transactions, 77, 901–906. 79. Toshniwal, R., Dastidar, K. G., & Nath, A. (2015). Big data security issues and challenges. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(2). 80. Padmanabhan, V. (2014). Big data analytics in oil and gas. Bain & Company Report. 81. Morrow, S., Coplen, M. (2017). Safety culture: a significant influence on safety in transportation. 2017, United States. Federal Railroad Administration. Office of Research …. 82. Wadhera, S., et al. (2021). A systematic Review of Big data tools and application for developments. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). 2021. IEEE. 83. Elijah, O., et al. (2021). A survey on Industry 4.0 for the oil and gas industry: Upstream sector. IEEE Access, 9, 144438–144468. 84. Oguntimilehin, A., & Ademola, E.-O. (2014). A review of big data management, benefits and challenges. A Review of Big Data Management, Benefits and Challenges, 5(6), 1–7. 85. Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041. 86. Kuang, L., et al. (2021). Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development, 48(1), 1–14. 87. Hassan, O. (2020). Artificial intelligence, neom and Saudi Arabia’s economic diversification from oil and gas. The Political Quarterly, 91(1), 222–227. 88. Persson, R., & Wernersson, J. (2023). Exploring the potential impact of AI on the role of graphic content creators: Benefits, challenges, and collaborative opportunities. 89. Canals, J., & Heukamp, F. (2020). The future of management in an AI world. Springer.

Application of Artificial Intelligence in the Oil and Gas Industry

373

90. Keskinbora, K. H. (2019). Medical ethics considerations on artificial intelligence. Journal of clinical neuroscience, 64, 277–282.

Duplicated Tasks Elimination for Cloud Data Center Using Modified Grey Wolf Optimization Algorithm for Energy Minimization Arif Ullah, Aziza Chakir, Irshad Ahmed Abbasi, Muhammad Zubair Rehman, and Tanweer Alam

Abstract

Task load balancing is a significant challenge in the cloud environment due to the network’s performance in relation to the workload of the cloud machines. It also has a direct impact on how much energy is consumed and, consequently, how much money the cloud provider makes. Customer wants and an application’s functionalities are A. Ullah (B) Department of Computer Science Faculty of Computing and Artificial Intelligent Air University, Islamabad, Pakistan e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Marocco e-mail: [email protected] I. A. Abbasi Department of Computer Science, Faculty of Science and Arts at Belgarn, University of Bisha, Bisha, Saudi Arabia e-mail: [email protected] M. Z. Rehman Faculty of Computing and Information Technology, Sohar University, Al-Batinah, Sohar, Oman e-mail: [email protected] T. Alam Faculty of Computer and Information Systems, Islamic University of Madinah, Medina, Saudi Arabia e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_20

375

376

A. Ullah et al.

both dynamic. In normal situation tasks allocation make issues like under loaded or overloaded but in some point tasks duplication occur which effect the data center performance. Dynamic tasks allocation in cloud computing improve the tasks allocation and reduce tasks duplication. For that purpose, modified Grey wolf optimization algorithm was design that improve tasks allocation and reduce tasks duplication. Thus, it is necessary to simultaneously achieve several competing goals. The proposed resource allocation technique for cloud computing that is adaptive, multi-objective, teaching– learning based. This method strikes a balance between goals like decreasing makespan, lowering total cost, and improving resource utilization. Additionally, the suggested technique reduces tasks duplication and system imbalance. Comparing the suggested algorithm to different algorithms demonstrated its effectiveness. The simulation results showed that the suggested algorithm may greatly improve resource utilization while reducing the user’s total cost and makespan. Future research the proposed approach will examine additional methods that enhance the predication capabilities for cloud data centers. Keywords

Duplicated tasks • Load balancing • Datacenter • Virtual machines • Virtualization

1

Introduction

Due to the recent maturity of cloud technologies and their viability, the availability of cloud services has increased significantly. The services of various cloud service providers emphasize various performance factors. Many cloud customers are unable to determine which cloud services are most appropriate for their purposes, leading them to select an unsuitable and incurring financial losses and time delays. In order to do this, researcher present a three-layered, efficient system for analyzing and ranking IaaS, PaaS, and SaaS cloud services in this study [1]. Cloud computing has significantly impacted human lives by providing various services and resources through virtualization and internet connectivity. It is a rapidly growing technology used in fields such as education, engineering, healthcare, and more. Cloud data centers play a crucial role in storing and accessing resources and applications, requiring efficient management and load balancing techniques [2]. Virtualization, specifically through virtual machines (VMs), is a key component of cloud computing, enabling the creation and allocation of virtual resources. Load balancing techniques are employed to optimize VM performance, enhance user satisfaction, and improve resource utilization. Load balancing distributes traffic among servers, minimizing response times and ensuring maximum throughput [3]. Virtualization also allows for dynamic provisioning of resources, optimizing utilization and reducing energy consumption. Different load balancing methods, including static and dynamic approaches, are used for task assignment and load balancing. Meta-heuristic algorithms are employed to

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

377

Fig. 1 Cloud datacenter structure [6]

solve complex load balancing problems and optimize resource allocation in cloud environments. Different machine learning algorithms help minimize costs and reduce execution time when resources are limited [4]. Different approaches also used for data distribution between different VM and physicals device using load balancing approaches for cloud different data centers. For data distribution different level of services are used but some situation these data become duplicated which effect the performance of data center and make unload or overloaded situation. For the improvement of duplicated data situation different researcher used different approaches [5]. Figure 1 present cloud data centers structure where different. VM or physical machine (PM) are connected with the help of different rules and regulation. For the improvement of proper data distribution and removing of duplicated in tasks this paper presents a hybrid approach.

2

Related Work

Through analysis of grey wolf optimizer in each iteration in exactly locating the required individuals with a predetermined simulation budget. The integration of OCBA-Mb with GWO has been shown to considerably intensification the exploration effectiveness of GWO in explaining stochastic optimization issues. Additionally, the suggested economical division decree cylinder be used with all current population-based meta-heuristics that need the best m explanations out of a possible m. By making the most use of the available computational resources, it provides a novel approach for improving the performance of population-based meta-heuristic searches. Utilizing the recommended meta-heuristics with OCBA-Mb algorithm framework, we analyze practical stochastic optimization problems from industrial and service systems. Additionally, taking a few real-world needs into account might help create allocation rules that are more successful. This project is

378

A. Ullah et al.

Table 1 Summary of related work Ref

Existed methods

Datasets use

Parameter

Results

Limitations

[10]

Scheduling of applications

Cloud computing

Make span and Improved response time data distribution

Less efficient in long tasks

[11]

Sand Cat swarm optimization algorithm

Global optimization problems

Makespan, Improved processing rate distribution

Costly

[12]

RCSO-based clustering method

SVM and MLP networks

Classification system

Improved distribution

Poor in determination of parameters’ values, and duplicated data for VM

[13]

Cat swarm optimization (CSO)-based algorithm

Urban transit routing problem (UTRP)

Optimization, scheduling workflow

Improved distribution

Algorithm’s optimization capability

fascinating [7, 8]. A management system called cloud armor was designed and put into use for reputation-based trust, according to [9] a number of functionalities are offered by this framework as well as benefits that trust-as-a-service (TaaS) offers: It is essential to use resource scheduling methods to allocate the assignment and conserve an anticipated equal of obtainability concluded the distribution of the faith controlling facilities (FCF) in order to protect users’ privacy. This is done in order to: (i) employ anonymization techniques; (ii) suggest a number of metrics for identifying response complicity and Sybil bouts; and (iii) identify feedback collusion and Sybil attacks. Table 1 displays some current research regarding the use of various strategies to eliminate duplicate activities for cloud data centers.

3

Grey Wolf Optimization Algorithm

A brand-new meta-heuristics method is called the Grey Wolf Optimization method (GWO). The engineering profession uses it most frequently to solve optimization problems. It takes its cues from the social and hunting habits of grey wolves. It mimics the grey wolves’ innate leadership structure and hunting style. Grey wolves typically live in groupings known as packs. Alpha, beta, delta, and omega grey wolves stand cast-off toward signify the four stages of the leadership hierarchy. The alpha wolf, the dominant member of the pack, serves as a leader to direct the pack during the hunting phase [14, 15]. The beta wolves, or subordinate wolves, are at the second rung of this hierarchy and assist the alpha in setting policies regarding the hunting process. Delta is the third level.

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

379

Wolf. They stand for the alpha wolf’s level of dominance in terms of making choices. Omega wolves, who serve as scapegoat wolves, are at the bottom of this hierarchy. When the dominant alpha wolf is missing from the grey wolf pack’s social structure, decisionmaking ability suffers. This social ability is the main source of inspiration for the grey wolf optimizer. Grey wolves have a deliberate strategy in their three repetitive phases of hunting. Searching, encircling, and attacking are these phases. Tracking or searching is the initial stage. This phase is often referred to as exploration. The grey wolves conduct a global search for prey during this time. Calculating the separation between the grey wolf and its prey is described in the mathematical model. It is determined use formula (1) [16]. Dist = abs(coeff × LP (t) − LGW (t))

(1)

Here coeff provides the coefficient vector; LGW (t) signifies the position of the grey wolf by a particular time intermission t; LP shows the locality of prey on a certain time intermission t. The worth of coeff is calculated by means of equivalence (2). Coeff = 2 × Ran(0, 1)

(2)

Here Ran (0, 1) is a purpose that revenues a haphazard figure amongst 0 and 1 [17, 18]. The additional stage is the surrounding the target by grey wolves. This stage is likewise recognized as the manipulation phase, which is cast-off to discover the optimum answer in a local exploration space. This stage includes altering the position of dissimilar grey wolves concerning the prey to enclose it usually. Location is patterned from unalike location and mark them. Mathematically, a modification in location of nth grey wolves at a time interval of t + 1 in this segment can be replicated as per reckoning (3) which is specified under. LGW −n (t + 1) = LP (t) − Mcoeff n × Dist n

(3)

where Dist n the remoteness of nth grey is wolf since the prey and calculated as per balance (4). Dist n = abs(coeff n × LP (t) − LGW (t))

(4)

Mcoeff n Provided the malicious coefficient vector for grey wolves and, calculated as per reckoning (5). Mcoeff n = 2 × X × ran(0, 1) × X

(5)

X is a variable that gets diminished from 2 to 0 in the iterative progression. The recent value X in an iteration t in tmax as the all-out number of rehearsals is calculated as per reckoning (6) [19, 20].

380

A. Ullah et al.

X =2−2×

t tmax

(6)

Equations 1 through 6 simulate the movements made by grey wolves in response to changes in the location of their prey. Grey wolves follow their social structure to attack the prey. Alpha, beta, and delta, the top three dominant wolves in the hierarchy, are seen to be the finest choices. It is thought that wolves in the alpha, beta, and delta positions have a better understanding of where their prey is after they have been encircled. The dominant wolf then attacks its prey as a result. The prey’s location is updated in accordance with the simulation of the alpha, beta, and delta wolves provided by Eq. (7). Equation (7) states that Omega wolves periodically update where they are in relation to the prey [17]. Distalpha= abs(coeff1 × Lalpha × (t) − L(t)) Distbeta= abs(coeff2 × Lbeta × (t) − L(t)) Distdelta= abs(coeff3 × Ldelta × (t) − L(t)) L1 = Lalpha (t) − coeff1 × Distalpha

(7)

L2 = Lbeta (t) − coeff2 × Distbeta L3 = Ldelta (t) − coeff3 × Distdelta (L1 + L2 + L3 ) L= 3 A group of random solutions are used by the grey wolf optimizer as grey wolves. Multiple objective functions are used to evaluate a set of random solutions. Each solution’s quality is represented by the values of a fitness function composed of various functions. Alpha, beta, and delta wolves represent the best three quality solutions. The grey wolf optimizer constantly adjusts the wolf population’s position throughout each cycle. If any answer improves during an iteration to the point that it is superior to alpha, beta, and delta wolves, then the related solution is replaced in favor of the new solution. When a set of stopping conditions is met, the grey wolf optimizer stops iterating the solutions [21–23].

3.1

Duplicated Tasks Assignment Elimination

This action is castoff to control whether an explanation comprises an only VM allocated to further than single PM in direction to alteration it so that individually VM is presented through a solitary PM in agreement. Nearby stand numerous attitudes to statement the delinquent of a VM presence presented through additional than single PM. Individual technique is to injection an accidental number, xij = 1, and set the values of the other entries to 0. The second tactic is to continue having the initial PM host the VM while designating it as not being allocated to the other PMs. In cloud computing, tasks are often started by cloud users in million instructions (MIs), which can be of varied lengths and

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

381

are processed later in the cloud datacenter. To ensure higher utilization, we want to make sure tasks are scheduled properly on VMs [24]. Thus, the scheduling decision depends on the VM’s estimated time to compute (ETC) for each task, as shown in Eq. 8. T represents the tasks V represents the VM. ⎤ T1 V1 T1 V2 ...T1 Vm ⎥ ⎢ T2 V1 .... ⎥ ⎢ ⎥ ⎢ ETC = ⎢ ⎥ ..... ⎥ ⎢ ⎦ ⎣ ..... Tn V1 ...Tn Vm ⎡

(8)

Sometime the process of VM and PM working properly but the data distribution become issue like duplication of tasks are performed within VM which can affect the proper data distribution in cloud environments. Due to the duplication of tasks VM become under loaded or overloaded which affect the energy level and cloud performance. The given issue can be solved by different research using VM and PM selection rule and load balancing approaches [25]. In this paper the selection rule used to solve data distribution issue for that purpose grey wolf optimizer algorithm is selected.

3.2

Proposed Modified Grey Wolf Optimizer Algorithm

Performance of cloud datacenters can be significantly decreased by lowering the amount of dynamic PMs and redundant activities. In order to address the VMP delinquent besides produce an answer that maps the VMs to the fewest active PMs and minimizes duplicate jobs, this study alters the GWO algorithm. The most practical option, “S,” is selected because it has the fewest active PMs. As the mapping from VMs to PMs is unidentified at the early formal, we flinch through the superlative consequence that takes remained originate, which is represented with SB in the chief state. Each VM is allocated to a single PM, and the n PMs randomly distribute the m VMs among one another. Consequently, there are mn. The wolves are persistent. Updating their locations is the best option. The wolves regularly update their locations (an optimal solution) to search for prey in different ways that m can be scattered over n. The best solutions, marked as, and, are used to guide the wolves throughout the exploration in order to enclose the prey. The wolves alter their locations in accordance with the positions of the, and wolves [26]. So, it is possible to create the VMP solution as depicted in Fig. 2. The allocation of five VMs to the bare minimum of the four PMs currently in situ by four wolves is shown in Fig. 2 with n = 4 and m = 5. The set below can be used to symbolize the four wolves’ solutions: S = {Sα, Sβ, Sδ, Sω}. The amount of lively PMs is hand-me-down to sort the solutions into ascending order. The primary, additional, and third-best responses remain designated by the letters S, S, and S, respectively. A single

382

A. Ullah et al.

Fig. 2 Selection criteria of the VM& PMs

solution is represented by each individual solution. These solutions are represented in Fig. 2 as (×1, × 2, × 3, × 4), (×6, × 7, × 8, × 9, × 10), and (×11, × 12, × 13, × 14, × 15), respectively. S, which stands for the remaining solution(s), is (×16, × 17, × 18, × 19, × 20). All solutions are represented by S, with the exception of the top three responses. Consequently, it is a collection of answers. The system selects the options in a diverse environment based on power consumption, with lower power consumption indicating a better option, assuming two wolves present the similar amount of vigorous PMs. The solution, which was deemed the greatest possibility at the most recent iteration, epitomizes the perfect assignment of the VMs on the PMs as assessed by GWO. Toward decrease the amount of dynamic PMs, each PM must host as many VMs as is practical, cumulative the reserve use of respectively PM. The location of the group of VMs has to be updated to the same PM in order to lower the amount of dynamic PMs. Any available PM in individually VM is rationalized with its present location. PM: 1 iteration (1, 1, n). The PM that is readily available, or Pi, is defined as [27–29].  m xij .V cpuj + V cpui ≤ Pcpui and PI = j=1 m j=1 xij .V ramj + V rami ≤ Prami where, respectively, Vcpuj and Vramj represent the totals of the CPU and memory capacity of the previously surrender to VMs on Pi. The unscheduled VM’s CPU and memory capacities are Vcpul and Vraml, respectively. The PM has the following capabilities: Pcpui and Prami. This equation describes the capabilities’ limitations, making it easier

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

383

Fig. 3 Flowchat of proposed algorithm

to choose a proper PM from the available PMs. During the course of each segment, the methods used to inform the discrete and binary positions of VMs on PMs also verify the task distribution at each VM and PM to assist prevent duplicate job distribution and to ensure correct load balancing techniques. The proposed structure contains the position update (Dupdate ), initialization (Dini ), and fitness evaluations (Deval ) for the population. The computational difficulty of the suggested context may be determined using an N-wolf of container and a D-dimensional optimization difficult as follows: D = Dini +(Dupdate + Deval ) × Fevaluations = N + (N × D + N ) × Fevaluations = N × (1 + (D + 1) × Fevaluations Thus, the proposed framework’s time complexity is given in education. 0(N × D × Fevaluations ) [30, 31]. Figure 3 present the flowchart of proposed algorithm where the modification section how it works is mention.

4

Evaluation Parameter

Make span is the quantity of time needed to process all task in the specified order. We can calculate make span according to this formula:

384

A. Ullah et al.

Makespan =

Given time of network Complete time of network

 × 100

The amount of data that may be transferred and received in a certain amount of period is mentioned to as throughput. The average rate at which communications successfully reach the desired destination is called throughput. We can calculate throughput by using the given formula: Throughput =

No. of Tasks makespan

The degree of imbalance measures how randomly cloud workloads are distributed among virtual machines according to their capacities. Task execution times on virtual machines are typically used to calculate it. We can calculate the degree of imbalance by using the given formula [32]. Imbalance_Degree =

Max_CTimei − Min_CTimei Avg_CTimei

Here, Max_CTimei denotes the determined achievement period of jobs on all VM. Min_CTimei Denotes the smallest conclusion time of jobs on all VM. Avg_CTimei Denotes the lowest achievement time of tasks on all VM. Imbalance Degree is a measure of how well load balancing is working in the cloud. Lower values show that Workload on the cloud is properly balanced. While higher values demonstrate inefficient load balancing. The amount of time a network is able to carry out its specified job is known as the total network’s lifetime. It means it’s the period of time when the network starts its defined function until it finishes the function. The network life time reduce due to the reduction of duplicate tasks [33].

4.1

Experimental Setup

In this study, the modified Grey wolf optimization algorithm was implemented using a customized CloudSim on test datasets in the standard workload format (SWF) with file sizes ranging from 200 to 400 Kilobytes. The performance of the planned procedure was assessed using dissimilar numbers of jobs (ranging from 200 to 2000) and varying numbers of VM(s) and PM across different data centers in the cloud computing environment. The outcomes of the suggested process stayed associated with the ABC, M. Bat, HHO-ACO, and PSO algorithms, focusing on parameters such as makespan, throughput, turnaround time, and degree of imbalance as indicators of load balancing effectiveness. The above all parameters improved due to the removed of duplicated tasks in PM and VM. Table 2 present the simulation parameter which are used during the testing process.

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

385

Table 2 Simulation parameter Type

Setting

Value

Type

Stetting

Value

RZ

1 to 4

5

Task

No. of Data

100/1000

DC

VM

5

No. of task center

5

Length of task

100/200/400 byte

No. of hosts

100/1000

No. of processor per requirement

250 KB

Type of manager

Time and space

Bandwidth

1000

Total no. of VM

30/20/20

VM image size

1000

Memory cloudlets

Type of manager

Time and space

Total memory

204,800 MB

No. of processor

4 per VM

Total no. of data

400/600

Result and Discussion

The suggested algorithm, which has been used to address the problem statement, has been developed. The proposed. Due to this process different parameters result are improved which are mention in this section. Table 3 present the complete of tasks during the testing process. According to the Table 3 and Fig. 4 the algorithm with the best performance in the given table is the “Proposed Modified Grey Wolf Optimization algorithm. It consistently achieves the highest scores across different numbers of tasks. Its throughput is 791 when number of tasks are 800. On the other hand, the algorithm with the worst performance in the given table is the “PSO” algorithm. It consistently achieves the lowest scores across different numbers of tasks. Its throughput is 750 when number of tasks are 800.Therefore, based on the provided data, the proposed algorithm exhibits the best performance, while the PSO algorithm demonstrates the worst performance among the listed algorithms during the testing process. Table 4 present the result of makespan. Based on the provided data in Table 4 Fig. 5 the best algorithm is the “Proposed M.GW” as it consistently achieves the lowest makespan values across different numbers of virtual machines. The worst algorithm is the “PSO” algorithm, which consistently has Table 3 Throughput Complete no. of task of different algorithm based on No. of task Given no. of task

PSO

ABC

PSO-CALBA

M.BAT

HHO-ACO

Proposed algorithm

200

160

165

170

175

186

193

400

355

365

372

378

385

390

600

560

570

575

580

588

592

800

750

770

775

780

786

791

386

A. Ullah et al.

Fig. 4 Result of through put

Table 4 Result of makespan (In millisecond) Makespan (in millisecond) of different algorithm based on no. of virtual machine No. of VM

PSO

ABC

PSO-CALBA

M-Bat

HHO-ACO

Proposed HGWCA

10

48

48

47

46

46

45

20

100

98

97

97

97

96

30

198

196

195

194

194

192

40

398

395

393

390

390

388

higher makespan values compared to other algorithms. This indicates that the Proposed performs the finest in relations of reducing finishing time, while the “PSO” algorithm performs the worst. Figure 6 present the result of VM performance. Based on the experiment in Table 4 and Fig. 6 the best algorithm is the “Proposed algorithm as it consistently achieves the lowest makespan values across different numbers of virtual machines. The worst algorithm is the “PSO” algorithm, which consistently has Fig. 5 Result of makespan

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

387

Fig. 6 Result of different VM

higher makespan values compared to other algorithms. This indicates that the “Proposed algorithm performs the superlative in relations of lessening execution time, while the “PSO” algorithm performs the worst. The Table 5 and Fig. 6 presents data on the number of complete tasks and their corresponding makespan (in milliseconds) for different algorithms. The “Proposed algorithm consistently achieves the lowest makespan values across all sets of complete tasks, indicating efficient execution time. Conversely, the “PSO” algorithm consistently exhibits higher makespan values, indicating relatively slower execution time. This data suggests that the “Proposed algorithm performs the greatest in relations of minimalizing completing time, while the “PSO” algorithm performs comparatively worse. Table 6 present the Average turnaround time. The provided Table 6 and Fig. 7 illustrate the average turnaround time of different algorithms for different numbers of tasks. Consistently, the proposed algorithm demonstrates the lowest average turnaround time across all task sets, indicating efficient completion. In contrast, the “PSO” algorithm generally exhibits higher average turnaround time values, indicating relatively slower task completion. These results indicate that the proposed algorithm excels in minimizing average turnaround time, while the “PSO” algorithm tends to lag behind in this aspect. Analyzing the Table 7 and Fig. 8 we observe variations in the throughput time among the algorithms for different task quantities. The “HHO-ACO” algorithm consistently exhibits the lowest throughput time values across all task sets, indicating its efficiency in processing cloudlets quickly. Conversely, the “PSO-CALBA” algorithm generally shows

50

50

HHO-ACO

Proposed algorithm

47

50

50

PSO-CALBA

M-Bat

49

50

ABC

43

45

47

50

50

PSO

Makespan (in millisecond)

Complete no. of tasks

Algorithm Name

Table 5 Overall result

100

100

100

100

100

100

Complete no. of Tasks

90

92

92

94

98

100

Makespam (in millisecond)

150

150

150

150

150

150

Complete no. of Tasks

142

144

146

147

148

150

Makespan (in millisecond)

180

180

180

180

180

180

Complete no. of tasks

152

155

157

162

165

170

Makespan (in millisecond)

388 A. Ullah et al.

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

389

Table 6 Result of Average turnaround time Given No of Task

Average Turnaround Time of different algorithm PSO

ABC

PSO-CALBA

M-Bat

HHO-ACO

Proposed algorithm

200

100

98

94

92

92

90

400

150

148

147

146

144

142

600

170

165

162

157

155

152

800

200

195

194

190

188

185

1200

250

250

247

245

240

230

1600

280

278

275

270

265

262

Fig. 7 Result of different VM turnaround time base

higher throughput times compared to other algorithms, suggesting slower processing. The “ABC” and “M-Bat” algorithms demonstrate competitive performance, with relatively lower throughput times across various task quantities. The “Proposed M.GW” algorithm performs relatively well, although its throughput time values fluctuate depending on the number of tasks. Table 7 Result of cloudlets Turnaround time of cloudlets No. of Task

PSO

ABC

PSO-CALBA

M-Bat

HHO-ACO

Proposed algorithm

200

3.2

3.6

2.9

2.4

2.1

1.5

400

3.4

2.9

2.3

2.5

3.1

1.3

800

3

1.2

1.5

2.2

1.8

1.2

1200

2.5

1.6

1.7

1.3

2.1

1.3

1600

3.1

2.2

2.5

0.8

1.23

1.6

390

A. Ullah et al.

Fig. 8 Result of degree of imbalance

Table 8. Result of degree of imbalance Degree of imbalance No. of Task

PSO

ABC

PSO-CALBA

M-Bat

HHO-ACO

Proposed HGWCA

200

2.8

2.6

2.9

2.4

0.66

0.87

400

3.4

2.9

2.3

2.5

1.2

0.8

800

2.7

1.2

1.5

2.2

0.9

0.5

1200

3

1.8

0.7

1.3

0.31

0.39

1600

3.1

2.2

2.5

0.8

0.21

0.19

Based on these values in Table 8 and Fig. 8 it appears that the “Proposed algorithm performs well in terms of achieving a lower degree of imbalance across multiple task quantities.

6

Conclusion

The suggested algorithm chooses the right host and distributes the task after receiving requests in the form of tasks and user interface requests. After assessing the number of hosts in the datacenter with resources available and their productivity at the time of the

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

391

request, it routes the user request to it. However, this method prevents job duplication since if a user submits numerous requests, each one is listed and handled separately. One of the major difficulties in this setting are makespan, throughput, system balance and reduction of duplicated tasks in VM and PM in cloud datacenter. CloudSim emulators were used to evaluate the outcomes. Actually, the load balance algorithm was the main focus of this simulator’s creation. That expansion in the direction of the suggested method was furthered by the effect by using different simulation parameter based on the result the proposed algorithm improved the tasks distribution and improve reduction of duplicated tasks. For the improvement of the above issue cloud data center improve throughput, makespan, degree of imbalance and network stability. In future study the proposed algorithm will implement for predication and real environments situation.

References 1. Lee, S. P., Kim, K., & Park, S. (2023). Investigating the market success of software-as-a-service providers: The multivariate latent growth curve model approach. Information Systems Frontiers, 25(2), 639–658. 2. Oke, A. E., Kineber, A. F., Alsolami, B., & Kingsley, C. (2023). Adoption of cloud computing tools for sustainable construction: A structural equation modelling approach. Journal of Facilities Management, 21(3), 334–351. 3. Naseri, N. K., Sundararajan, E., & Ayob, M. (2023). Smart Root Search (SRS) in solving service time-cost optimization in cloud computing service composition (STCOCCSC) Problems. Symmetry, 15(2), 272. 4. Fofana, S. (2023). Cloud computing for servitization: A case study and future trends, Master’s thesis. Universitat Politècnica de Catalunya. 5. Jahromi, G. S., & Ghazinoory, S. (2023). How to use bits for beats: the future strategies of music companies for using Industry 4.0 technologies in their value chain. Information Systems and e-Business Management, 1–21. 6. Bansal, J. C., & Singh, S. (2021). A better exploration strategy in Grey Wolf Optimizer. Journal of Ambient Intelligence and Humanized Computing, 12, 1099–1118. 7. Rodríguez, L., Castillo, O., Soria, J., Melin, P., Valdez, F., Gonzalez, C. I., ... & Soto, J. (2017). A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Applied Soft Computing, 57, 315–328. 8. Wong, L. I., Sulaiman, M. H., Mohamed, M. R., & Hong, M. S. (2014, December). Grey Wolf Optimizer for solving economic dispatch problems. In 2014 IEEE International Conference on Power and Energy (PECon) (pp. 150–154). IEEE. 9. Dida, H., Charif, F., & Benchabane, A. (2020, October). Grey wolf optimizer for multimodal medical image registration. In 2020 Fourth International Conference on Intelligent Computing In Data Sciences (ICDS) (pp. 1–5). IEEE. 10. Dudani, K., & Chudasama, A. R. (2016). Partial discharge detection in transformer using adaptive grey wolf optimizer based acoustic emission technique. Cogent Engineering, 3(1), 1256083. 11. Fahad, M., Aadil, F., Khan, S., Shah, P. A., Muhammad, K., Lloret, J., ... & Mehmood, I. (2018). Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Computers & Electrical Engineering, 70, 853–870.

392

A. Ullah et al.

12. Tawhid, M. A., & Ali, A. F. (2018). Multidirectional grey wolf optimizer algorithm for solving global optimization problems. International Journal of Computational Intelligence and Applications, 17(04), 1850022. 13. Kohli, S., Kaushik, M., Chugh, K., & Pandey, A. C. (2019, November). Levy inspired enhanced grey wolf optimizer. In 2019 Fifth International Conference on Image Information Processing (ICIIP) (pp. 338–342). IEEE. 14. Cui, J., Liu, T., Zhu, M., & Xu, Z. (2023). Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems. The Journal of Supercomputing, 79(10), 10864– 10914. 15. Alzaqebah, A., Al-Sayyed, R., & Masadeh, R. (2019, October). Task scheduling based on modified grey wolf optimizer in cloud computing environment. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS) (pp. 1–6). IEEE. 16. Gholizadeh, S. (2015). Optimal design of double layer grids considering nonlinear behaviour by sequential grey wolf algorithm. Journal of Optimization in Civil Engineering, 5(4), 511–523. 17. Maravelias, C. T., & Grossmann, I. E. (2003). Minimization of the Makespan with a DiscreteTime State−Task Network Formulation. Industrial & engineering chemistry research, 42(24), 6252–6257. 18. Melouk, S., Damodaran, P., & Chang, P. Y. (2004). Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing. International journal of production economics, 87(2), 141–147. 19. Lee, C. Y. (1999). Minimizing makespan on a single batch processing machine with dynamic job arrivals. International journal of production research, 37(1), 219–236. 20. Lian, Z., Jiao, B., & Gu, X. (2006). A similar particle swarm optimization algorithm for jobshop scheduling to minimize makespan. Applied mathematics and computation, 183(2), 1008– 1017. 21. Friedman, N. P., & Miyake, A. (2004). The reading span test and its predictive power for reading comprehension ability. Journal of memory and language, 51(1), 136–158. 22. Kashan, A. H., Karimi, B., & Jolai, F. (2006). Effective hybrid genetic algorithm for minimizing makespan on a single-batch-processing machine with non-identical job sizes. International Journal of Production Research, 44(12), 2337–2360. 23. Muthiah, A., & Rajkumar, R. (2014). A comparison of artificial bee colony algorithm and genetic algorithm to minimize the makespan for job shop scheduling. Procedia Engineering, 97, 1745–1754. 24. Ullah, A., Yasin, S., & Alam, T. (2023). Latency aware smart health care system using edge and fog computing. Multimedia Tools and Applications, 1–27. 25. Rai, S. Kuan, W. L., & Mustafa, R. (2023). An Enhanced Compression Method for Medical Images Using SPIHT Encoder for Fog Computing. International Journal of Image and Graphics, 2550025. 26. Sebai, D., & Shah, A. U. (2023). Semantic-oriented learning-based image compression by OnlyTrain-Once quantized autoencoders. Signal, Image and Video Processing, 17(1), 285–293. 27. Hanane, A., Ullah, A., & Raghay, S. (2023). Enhanced GAF protocol based on graph theory to optimize energy efficiency and lifetime in WSN technology. International Journal of Intelligent Unmanned Systems, 11(2), 214–225. 28. Aznaoui, H., Raghay, , & Khan, M. H. (2021). Energy efficient strategy for WSN technology using modified HGAF technique. iJOE, 17(06), 5. 29. Ouhame, S., & Hadi, Y. (2020). A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology. International Journal of Online & Biomedical Engineering, 16(14).

Duplicated Tasks Elimination for Cloud Data Center Using Modified …

393

30. Baseer, S., & Umar, S. (2016, August). Role of cooperation in energy minimization in visual sensor network. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH) (pp. 447–452). IEEE. 31. Ullah, A., Nawi, N. M., Arifianto, A., Ahmed, I., Aamir, M., & Khan, S. N. (2019). Real-time wheat classification system for selective herbicides using broad wheat estimation in deep neural network. International Journal on Advanced Science, Engineering and Information Technology, 9(1), 153. 32. Ouhame, S., & Hadi, Y. (2020). A hybrid grey wolf optimizer and artificial bee colony algorithm used for improvement in resource allocation system for cloud technology. International Journal of Online & Biomedical Engineering, 16(14). 33. Alam, T., Ullah, A., & Benaida, M. (2023). Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. Journal of Ambient Intelligence and Humanized Computing, 14(8), 9959–9972.

Enhancing Deep Learning-Based Semantic Segmentation Approaches for Smart Agriculture Imade Abourabia, Soumaya Ounacer, Mohamed Yassine Ellghomari, and Mohamed Azzouazi

Abstract

Human civilization relies on agriculture as an essential component and has been the foundation of human society for thousands of years. However, agriculture faces several problems that affect its sustainability, productivity and profitability. Indeed, plant diseases, soil degradation and others as well as the development of artificial intelligence techniques are pushing human beings to think about smart agriculture and develop models of machine learning and deep learning to solve the problem. In this paper, we will present the process of preparing data using satellite images and the different models of machine learning and deep learning. Data preparation from satellite images and the various libraries implemented on python in order to apply different semantic segmentation approaches based on deep learning, which helps in decision-making and improving the overall performance and efficiency of the agricultural industry. Keywords

Artificial Intelligence (AI machine learning) • Deep learning • Smart agriculture Models • Satellite images • Semantic segmentation approaches

•

I. Abourabia (B) · S. Ounacer · M. Y. Ellghomari · M. Azzouazi Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_21

395

396

1

I. Abourabia et al.

Introduction

Smart agriculture is an emerging field focused on enhancing agricultural efficiency and sustainability by harnessing cutting-edge technologies like deep learning. In this domain, a pivotal task involves semantic segmentation, which entails labeling individual pixels in an image with semantic identifiers to gain insights into the composition and layout of agricultural landscapes. Semantic segmentation approaches based on deep learning have revolutionized the way we approach this challenge. Using deep convolutional neural networks, it is possible to capture spatial and contextual information at different scales, allowing for accurate and robust segmentation of agricultural images. These approaches exploit massive and diverse data sets, including satellite imagery, aerial photographs, and data captured by drones. Through sophisticated deep network architectures such as deep convolution networks (DCNN) and recurrent convolutional neural networks (CRNNs), deep learning models can extract discriminating and Learn about the complex relationships between pixels and labels.

2

Problematic

Smart agriculture is a growing field that uses cutting-edge technologies, such as deep learning, to optimize farming practices. Semantic segmentation is one of the key tasks of smart agriculture, allowing an agricultural image to be divided into different semantic regions, such as crops, weeds, soils, etc. This segmentation information is essential for making accurate and effective decisions in agriculture. Nevertheless, even with the notable progress made in deep learning-based semantic segmentation methods, a series of issues and hurdles remain unresolved. These encompass issues like the scarcity of annotated data, the wide-ranging variability of agricultural settings, susceptibility to temporal fluctuations, and the need for improved interpretability of outcomes.

3

Objectives

Goals of deep learning-based semantic segmentation approaches for smart agriculture may include: – – – –

Accurate detection of agricultural objects. Improved operational efficiency. Crop health surveillance (plant disease) … Optimize resource management.

Enhancing Deep Learning-Based Semantic Segmentation Approaches …

397

– Prediction of agricultural yields.

4

Technologies Used

Here are some common deep learning-based semantic segmentation approaches in the context of smart agriculture, according to articles and research: 1. U-Net: The U-Net network is widely used for semantic segmentation in many applications, including smart agriculture. It is recognized for its ability to capture contextual information at different scales through a U-shaped architecture, which is particularly useful for segmenting agricultural images with complex structures such as crops and weeds. 2. FCN (Fully Convolutional Network): Fully convolutional networks were among the first architectures to be specifically designed for semantic segmentation. They operate by performing convolutions on the entire image and predicting labels for each pixel. This makes them suitable for agricultural images where spatial structure is important. 3. SegNet: SegNet is another convolutional neural network architecture designed for semantic segmentation. It uses an encoder to extract functionality from the image and a decoder to reconstruct the segmentation map. SegNet can be adapted to smart farming tasks using multispectral images or other agriculture-specific data. 4. Set Models: Some articles explore the use of model sets for semantic segmentation. By combining predictions from multiple models, it is possible to improve the robustness and accuracy of segmentation, which is crucial for smart agriculture applications that require accurate segmentation of crops, weeds, and other elements.

5

Related Work

This section explores prior research in the area of Enhancing Deep learning-based semantic segmentation approaches for smart agriculture, outlining various methodologies employed (Table 1). This table presents a thorough summary of recent progress in the utilization of computer vision, deep learning, and machine learning to address significant agricultural challenges. The articles under consideration delve into various agricultural issues, including pest control, weed detection, and efficient water management. The solutions put forward harness cutting-edge technologies to automate tasks, enhance crop quality, improve surveillance, and optimize resource utilization. By leveraging sophisticated algorithms and methodologies, these studies offer promising avenues for transforming

398

I. Abourabia et al.

Table 1 State of art Article Problematic

Proposed solutions

[1]

Traditional methods in agriculture, such as visual inspection by experts or biological examination, are time-consuming and often impractical for identifying and addressing issues on farms, leading to production problems

The application of computer vision technology, enabling machines to analyze, process, and comprehend images from data acquisition systems. This facilitates automatic object counting, localization, and recognition, significantly enhancing the quality of agricultural operations

[2]

With growing interest in utilizing vine varieties to adapt to global warming, ensuring the authenticity of plants is essential to avoid planting subpar vines, which can result in substantial losses for winegrowers

The implementation of deep learning techniques, encompassing supervised and unsupervised learning approaches, to semantically segment images of grape leaves. This approach aims to develop an automated object detection system through segmentation for leaf phenotyping, offering insights into leaf structure and function

[3]

The global population continues to expand, leading to a pressing need for accessible organic food production. Farmers must control various critical factors, including crop health, water and fertilizer usage, and the management of harmful diseases in fields. However, monitoring these agricultural activities can be challenging

The adoption of precision agriculture, a crucial decision support system for food production and decision-making. This involves the integration of hyperspectral imaging technology and deep learning and machine learning algorithms to effectively monitor and manage these agricultural factors

[4]

Accurate monitoring of land, especially rice fields, is crucial for food security and support measures in agriculture. Traditional methods involve expensive, slow, and less detailed fieldwork or expert investigations

We propose a novel multi-temporal classification method with high spatial resolution utilizing an advanced spatio-temporal deep neural network. This approach allows us to pinpoint rice paddies at the pixel level throughout the entire year and across different temporal instances

[5]

Agricultural production generates significant amounts of waste, and composting is an effective recycling method. Assessing compost maturity is vital, but conventional biochemical tests are time- consuming

We introduce the use of convolutional neural networks (CNNs) for rapid compost maturity assessment by analyzing images at different composting stages. Experimental results demonstrate that our CNN-based prediction model produces cutting-edge results and can predict compost maturity during the composting process (continued)

Enhancing Deep Learning-Based Semantic Segmentation Approaches …

399

Table 1 (continued) Article Problematic

Proposed solutions

[6]

Agriculture is the primary occupation in India and a major economic contributor, but weed growth affects overall yield

By utilizing machine learning and deep learning techniques and combining images from the Virudhachalam exploration site with soil datasets, we aim to enhance soil type classification accuracy

[7]

Proper application of machine learning and deep learning in agriculture is crucial, as they can lead to incorrect or misleading results if not applied correctly

We introduce key deep learning concepts, advocate best practices for data preprocessing, emphasize the importance of choosing appropriate metrics, clarify the process of cross- validation, and provide recommendations for data preprocessing and metrics

[8]

Effective water management requires understanding agricultural irrigation systems and their responses to various factors, including segmentation of different irrigation types

Our approach involves leveraging remote sensing for segmentation of various irrigation types, employing a U-Net architecture with a Resnet-34 backbone, applying transfer learning for improved model performance, and considering four irrigation systems, urban, and background areas in land use/cover classes, using a dataset of 8,600 high-resolution images labeled with field observations

[9]

Pest control in greenhouses necessitates precise pest detection, traditionally relying on labor-intensive human observation of sticky traps

To address this issue, we have developed a detection model called “TPest-RNNN” based on the Faster Regional Convolution Neural Network (R-CNN) to enhance the accuracy of detecting small pests like whiteflies and thrips in greenhouse conditions

[10]

The Service System for Supervision of the Operation of Agricultural Machinery contains images of various machine types, often with complex backgrounds and low image quality

To improve clarity, we have assembled a dataset of 125,000 images, constructed a network system for machine recognition, and utilized AMTNet for addressing image illumination, environmental changes, and small area occlusions (continued)

400

I. Abourabia et al.

Table 1 (continued) Article Problematic

Proposed solutions

[11]

The IoT-based agricultural ecosystem presents an avenue for leveraging various technologies to support the agricultural sector in monitoring and preventing fruit diseases. Therefore, the question arises: How can we advance the development of learning and image processing methods to detect and prevent the spread of diseases in agricultural products?

In this research, the focus was on apples, involving the classification of apple images into two distinct categories: infected and uninfected. This process involved the creation of a convolutional neural network designed to extract crucial features from the apple images, which were subsequently employed for the classification task

[12]

Typically, weeds are perceived as a persistent challenge within greenhouses, and their presence can diminish the overall quality of crops. Thus, it becomes essential to explore methods for weed detection, recognizing plant diseases, and implementing water and soil conservation techniques that can enhance crop productivity

The development of the HLBODL- WDSA model enables IoT devices to capture farm images and subsequently transmit them to a cloud server for evaluation. Following this step, the HLBODL-WDSA model employs a weed detection process based on YOLO-v5

[13]

Video detection poses challenges such as defocus, motion blur, and partial occlusion. The objective is to design a real-time agricultural monitoring system using deep learning techniques to process, extract, and classify video frames

Video data was initially gathered from an agricultural surveillance camera and transmitted via an IoT module. These data underwent a transformation into video frames. Initially, the input video frame was subjected to preprocessing and segmentation, which involved noise removal, frame resizing, and image smoothing. Subsequently, characteristic features were extracted from the frames using a probability-based lasso network regression technique. The extracted characteristics were then subjected to classification using a dynamic radial functional neural network (Dy_Rad_ FuNN) that operated on a deep learning architecture (continued)

Enhancing Deep Learning-Based Semantic Segmentation Approaches …

401

Table 1 (continued) Article Problematic

Proposed solutions

[14]

Insufficient agricultural practices represent a substantial obstacle to boosting food production. This issue arises from suboptimal soil management, excessive reliance on pesticides and fertilizers, and the adoption of monoculture methods. These factors can contribute to soil degradation, reduced fertility, and ultimately, diminished crop yields, thereby endangering food security

Examination of software sensor data in agriculture involved the utilization of IoT module-based software sensors to gather historical cultivation data. The collected data underwent preprocessing, which entailed removing missing values and cleaning the data, particularly for images obtained via the IoT module. To represent the characteristics of the processed data, a neural network optimized for weight and maximum likelihood (WONN_ML) was employed. The deep features, once represented, were subjected to analysis using maximum likelihood techniques. Subsequently, the analyzed and represented characteristics were classified using a comprehensive architecture that incorporated both a stacked autoencoder and a kernel convolution network (SAE_ KCN)

[15]

In the context of greenhouse crops, factors such as environmental instability and plant diseases can negatively impact plant growth and lead to reduced yields during the growth process. This prompts the question: How can we harness machine learning in agriculture to enhance both crop yield and quality?

The data under observation was obtained through IoT modules, incorporating historical images from the crop farm. Following data collection, a resizing process was applied to the images. Characteristic features were extracted from the processed data using convolutional learning, specifically through the utilization of the deep attention layer (DAL_CL), which facilitated the extraction of data characteristics. These extracted data underwent classification using a neural network- based recursive architecture (RNN). The proposed system is capable of utilizing data categorization and deep learning to harness the acquired data effectively, enabling it to accurately predict whether a plant will be affected by a disease or not (continued)

402

I. Abourabia et al.

Table 1 (continued) Article Problematic

Proposed solutions

[16]

Aphid infestations have the potential to inflict substantial harm on Brassica crops, with adverse effects on both crop yield and quality

The establishment of a novel dataset specifically tailored for aphid colonies was initiated. A novel technique was introduced for merging boundary boxes, resulting in the generation of potential colony regions. Subsequently, a binary classification algorithm based on a convolutional neural network (CNN) was applied to the dataset images. The obtained results revealed that the models achieved an average accuracy (mAP) of 56.9% for Faster R-CNN, 53.4% for SSD, 53.1% for YOLOv3, and 48.7% for EfficientNet. Additionally, in terms of computational time, SSD and EfficientNet outperformed Faster R- CNN and YOLOv3. These findings suggest that machine learning algorithms show promise in detecting aphid colonies within images, although there is room for further improvement

[17]

The widespread adoption of plastic agricultural greenhouses (PWS) plays a pivotal role in ensuring an ample supply of food, including vegetables and fruits, for the population. Nevertheless, concerns have arisen due to pollution stemming from the use of plastic materials in these structures. Consequently, there is a pressing need to determine the spatial distribution of SGA (plastic agricultural greenhouses) through various methods. The question arises: How can we employ deep learning techniques to effectively recognize and quantify greenhouses?

This research was conducted within the greenhouses located in the Shouguang region of China. The approach involved utilizing high- resolution Google images in conjunction with the U-Net semantic segmentation network to extract information about these greenhouses. The findings revealed that the total greenhouse area in Shouguang spanned 185.37 square kilometers, with an estimated count of approximately 170,000 individual plastic agricultural greenhouses (APG)

(continued)

Enhancing Deep Learning-Based Semantic Segmentation Approaches …

403

Table 1 (continued) Article Problematic

Proposed solutions

[18]

To address this challenge, they implemented the Random Image Cropping and Patching (RICAP) technique, which introduces enhancements to boost the performance of deep neural networks. This method was put to the test on two distinct datasets obtained from different farms (Narrabri and Bonn). The outcomes demonstrated its effectiveness in improving segmentation accuracy. Specifically, for the Narrabri dataset, there was an increase in mean accuracy from 91.01 to 94.02, and a boost in mean intersection over union (IOU) from 63.59 to 70.77. Meanwhile, for the Bonn dataset, it led to an increase in mean accuracy from 97.99 to 98.51 and elevated the average IOU from 74.26 to 77.09

Deep learning techniques like convolutional neural networks (CNNs) have gained popularity for addressing challenges related to crop and weed classification in agricultural robotics. Nevertheless, achieving desirable performance and mitigating the need for a vast number of labeled images can be challenging when training deep neural networks. Thus, the question arises: What strategies and methods can be employed for data augmentation in the context of deep learning-based semantic segmentation and weed classification within agricultural robotics?

conventional agricultural methods and ensuring the sustainability of food production in response to increasing global needs.

6

Results

The performance of deep learning algorithms in the context of smart farming varies based on several factors, including the quality of training data, task complexity, available computational resources, and the desired level of accuracy. Nevertheless, deep learning algorithms have demonstrated promising outcomes in various areas of smart agriculture: 1. Crop Detection and Field Mapping: Convolutional Neural Networks (CNNs) have been effectively employed to detect and map different crop types using satellite or aerial images. High accuracy can be achieved, particularly when the training dataset is diverse and abundant. 2. Pest and Disease Detection: CNNs are also utilized for automated detection of pests and diseases in crops through the analysis of plant images. This approach can yield high-performance results, enabling prompt intervention to mitigate crop losses. 3. Irrigation Optimization: Deep learning models can estimate soil moisture levels using data such as thermal infrared images or sensors. Generally, these models perform well, facilitating more efficient irrigation management.

404

I. Abourabia et al.

Fig. 1 Results obtained

4. Yield Forecasting: Recurrent Neural Network (RNN) models or deep neural networks can predict crop yields by considering factors like weather conditions, soil data, and agricultural practices. Model performance is contingent on data availability and the desired level of accuracy. 5. Weed Identification: CNNs are employed for the detection and classification of weeds within crops, aiding in weed control efforts. Model performance depends on factors such as weed variety and lighting conditions. 6. Site Selection for Planting: Models can recommend optimal planting locations based on data such as topography, soil quality, and water availability. The accuracy of these recommendations relies on the precision of input data. 7. Supply Chain Management: Deep learning models enable the monitoring and tracking of the food supply chain, verifying product quality at different stages. Model performance hinges on data quality and sensor accuracy. It’s important to emphasize that the effective implementation of deep learning in smart agriculture necessitates efficient data collection, annotation, and management, often involving substantial amounts of data. Furthermore, close collaboration between agricultural experts and machine learning specialists is crucial to tailor models to the unique requirements of agriculture. Additionally, model interpretability presents a significant challenge, as decisions made in agriculture often require explanations for farmers and stakeholders. For instance, using a dataset containing 224 images of various plant diseases to predict disease presence in plants yields the following results and discussion (Fig. 1).

Enhancing Deep Learning-Based Semantic Segmentation Approaches …

7

405

Conclusion

In conclusion, deep learning-based semantic segmentation approaches offer immense potential for smart agriculture. Thanks to these advanced techniques, it is now possible to segment and understand the different entities present in agricultural images, such as crops, weeds, pests and many others. The application of these approaches allows farmers to make more informed decisions and optimize their farming practices. Semantic segmentation provides accurate information on the spatial distribution of different entities, making it possible to target the actions needed, whether for early detection of diseases, effective weed management or crop health assessment. However, it is important to note that these approaches still require efforts to address some challenges. The availability of high-quality and representative labeled data remains a major challenge, as does the generalization of deep learning models to different geographic regions and cultures. In addition, the energy consumption and computing resources required to train and deploy these models remain important considerations. Despite these challenges, deep learning-based semantic segmentation approaches have already shown their value in smart agriculture, and they will continue to play a crucial role in improving agricultural productivity, efficiency and sustainability.

References 1. Alrowais, F., Asiri, M. M., Alabdan, R., Marzouk, R., Hilal, A. M., Alkhayyat, A., & Gupta, D. (2022). Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment. Computers and Electrical Engineering, 104, 108411. https://doi.org/10.1016/j.compeleceng.2022.108411. 2. Amrani, A., Sohel, F., Diepeveen, D., Murray, D., & Jones, M. G. K. (2023). Deep learningbased detection of aphid colonies on plants from a reconstructed brassica image dataset. Computers and Electronics in Agriculture, 205, 107587. https://doi.org/10.1016/j.compag.2022. 107587. 3. Chen, W., Xu, Y., Zhang, Z., Yang, L., Pan, X., & Jia, Z. (2021). Mapping agricultural plastic greenhouses using google earth images and deep learning. Computers and Electronics in Agriculture, 191, 106552. https://doi.org/10.1016/j.compag.2021.106552. 4. Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2022). Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications, 16, 200102. https://doi.org/10.1016/j.iswa.2022.200102. 5. Padmapriya, J., & Sasilatha, T. (2023). Deep learning based multi-labelled soil classification and empirical estimation toward sustainable agriculture. Engineering Applications of Artificial Intelligence, 119, 105690. https://doi.org/10.1016/j.engappai.2022.105690. 6. Jiang, He, Xiaoru Li, et Fatemeh Safara. « WITHDRAWN: IoT-Based Agriculture: Deep Learning in Detecting Apple Fruit Diseases ». Microprocessors and Microsystems, août 2021, 104321. https://doi.org/10.1016/j.micpro.2021.104321.

406

I. Abourabia et al.

7. Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022). A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecological Informatics, 69, 101678. https://doi.org/10.1016/j.ecoinf.2022.101678. 8. Khan, S., & AlSuwaidan, L. (2022). Agricultural monitoring system in video surveillance object detection using feature extraction and classification by deep learning techniques. Computers and Electrical Engineering, 102, 108201. https://doi.org/10.1016/j.compeleceng.2022.108201. 9. Li, W., Wang, D., Li, M., Gao, Y., Wu, J., & Yang, X. (2021). Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Computers and Electronics in Agriculture, 183, 106048. https://doi.org/10.1016/j.compag.2021.106048. 10. Nguyen, T. T., Hoang, T. D., Pham, M. T., Trinh Vu, T., Nguyen, T. H., Huynh, Q.-T., & Jo, J. (2020). Monitoring agriculture areas with satellite images and deep learning. Applied Soft Computing, 95, 106565. https://doi.org/10.1016/j.asoc.2020.106565. 11. Raei, E., Asanjan, A. A., Nikoo, M. R., Sadegh, M., Pourshahabi, S., & Adamowski, J. F. (2022). A deep learning image segmentation model for agricultural irrigation system classification. Computers and Electronics in Agriculture, 198, 106977. https://doi.org/10.1016/j.com pag.2022.106977. 12. Richetti, J., Diakogianis, F. I., Bender, A., Colaço, A. F., & Lawes, R. A. (2023). A methods guideline for deep learning for tabular data in agriculture with a case study to forecast cereal yield. Computers and Electronics in Agriculture, 205, 107642. https://doi.org/10.1016/j.compag. 2023.107642. 13. Su, D., Kong, H., Qiao, Y., & Sukkarieh, S. (2021). Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics. Computers and Electronics in Agriculture, 190, 106418. https://doi.org/10.1016/j.compag.2021.106418. 14. Tamvakis, P. N., Kiourt, C., Solomou, A. D., Ioannakis, G., & Tsirliganis, N. C. (2022). Semantic image segmentation with deep learning for vine leaf phenotyping. IFAC-Papers On Line, 55(32), 83–88. https://doi.org/10.1016/j.ifacol.2022.11.119. 15. Wongchai, Anupong, Durga Rao Jenjeti, A. Indira Priyadarsini, Nabamita Deb, Arpit Bhardwaj, et Pradeep Tomar. « Farm Monitoring and Disease Prediction by Classification Based on Deep Learning Architectures in Sustainable Agriculture ». Ecological Modelling 474(décembre 2022): 110167.https://doi.org/10.1016/j.ecolmodel.2022.110167. 16. Wongchai, A., Shukla, S. K., Ahmed, M. A., Sakthi, U., Jagdish, M., & Kumar, R. (2022). Artificial intelligence—Enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture. Computers and Electrical Engineering, 102, 108128. https://doi.org/10.1016/j.compeleceng.2022.108128. 17. Xue, W., Xuejiao, H., Zhong, W., Xinlan, M., Xingjian, C., & Yangchun, X. (2019). A Fast and Easy Method for Predicting Agricultural Waste Compost Maturity by Image-Based Deep Learning. Bioresource Technology, 290 (October), 121761. https://doi.org/10.1016/j.biortech. 2019.121761. 18. Zhang, Z., Hui, L., Zhijun M., & Jingping, C. (2019). Deep learning-based automatic recognition network of agricultural machinery images. Computers and Electronics in Agriculture, 166(November), 104978. https://doi.org/10.1016/j.compag.2019.104978.

Applications of Artificial Intelligence in Management, in Supply Chain, and in Finance

Role of Artificial Intelligence in Sustainable Finance Monika Rani and Ram Singh

Abstract

Sustainability has gotten more attention in recent years because of environmental activists who are pressuring businesses to incorporate sustainable practices into their daily operations. Businesses must now demonstrate their ability to operate with the greatest efficiency and effectiveness possible in order to succeed. This document’s main goal is to look into how financial management affects sustainability, how it affects overall performance, and how it relates to sustainable artificial intelligence (AI). Many businesses all around the world are now mandating that using sustainable business practices is an integral part of their operations. Financial management has proven to play a key role in promoting sustainability, which positively impacts business success. Businesses are using artificial intelligence (AI) more and more to recognize environmental and climate change-related problems, evaluate how they affect daily operations, and develop solutions. Investigating many facets of environmentally friendly development, ESG (Environmental, Social, and Governance) investment, and utilizing artificial intelligence to assist creditors, investors, and corporate leaders in making educated decisions are the main areas of attention. In the long run, this will help to ensure financial stability. In this perspective, it is important to think about the challenges and potential benefits that AI presents when offering answers to sustainability-related problems. One societal issue that artificial intelligence (AI) has the potential to solve is sustainability—his editorial deterioration and the urgent climate crisis present complex problems that call for cutting-edge, innovative solutions. According to a survey of M. Rani (B) · R. Singh MM Institute of Management, Maharishi Markandeshwar (Deemed to Be University), Mullana-Ambala, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_22

409

410

M. Rani and R. Singh

the literature, corporations and governments have embraced sustainability in their own circumstances and are actively pursuing it. In addition to summarising the papers in this edition, this Editorial offers novel viewpoints gleaned from the literature. It also explores the wider significance of AI beyond its use as a problem-solving tool in financial management and the crucial role AI applications and models play in sustainable finance. Keywords

Sustainable finance • Financial performance • Financial management

1

Introduction

Environmental activists have increased their pressure on businesses to adopt sustainable practices in recent years, which has increased the attention on sustainability. The ability of companies to demonstrate their extraordinary productivity and efficacy has evolved into a critical component in the long-term success of global corporations. This paper’s main goal is to study how financial management contributes to sustainability promotion, how it affects performance, and how it integrates sustainable artificial intelligence (AI). Numerous businesses now require participation in sustainable business practices as a component of their operations on a worldwide basis. Sustainable development has had a significant impact on financial management, which has benefited business performance. These societal problems, such as sustainability, can be solved through artificial intelligence (AI). The difficulties brought on by the climate catastrophe and the deterioration of the ecosystem are complex, necessitating innovative and cutting-edge solutions[1]. goes beyond its ability to reduce hardship, environmental pollution, and resource depletion. it also resides in its capacity to assist social and environmental governance [2]. As a result, many countries have launched programs designed to increase investment in renewable resources, promote economic growth, and considerably advance environmental goals. Achieving these sustainability goals in modern society depends on the application of enormous amounts of data, networking sites, knowledge management, and data science in the age of artificial intelligence [3]. It is anticipated that the rapid development of intelligent systems will result in a spike in the production of financial data, forcing the development of accounting and financial solutions to deal with new problems and an increase in the demand for highly qualified accountants who are adept at using AI-based financial systems. In this context, sustainable investing has gained increased significance in recent years, drawing greater attention from scholars, researchers, and policymakers [4]. However, institutional investors, who commonly handle assets on behalf of individuals, as well as the academic community might benefit from understanding the drivers driving sustainable investing. This is especially important in view of the growing significance of AI in dealing with sustainability concerns. Investments in sustainable development are increasingly being made, according to a thorough analysis of the relevant literature. The

Role of Artificial Intelligence in Sustainable Finance

411

impact of AI on financial outcomes, its critical role in assisting stakeholders and investors in making informed decisions, and its support for organizational initiatives aimed at protecting user and consumer information by thwarting cyberattacks are all questions that arise in such an environment [3]. It’s critical to first consider the effect of AI on financial reporting since it enables investors to gather, analyze, and comprehend a wider range of data for assessing environmental, social, and governance (ESG) aspects affecting both firms and investment portfolios. In addition, AI empowers sustainable investors to quickly manage enormous datasets, including crucial ESG-related data, among the massive sea of data. Similar to this, sentiment analysis is currently automating jobs that, even a few years ago, people would not have been able to do, leading to novel ethical problems in addition to psychological and societal implications [5]. this increase in demand highlights the need for educated, skilled accountants who can manage AI-driven financial systems. sustainable investing has significantly increased in popularity recently. However, institutional investors, who frequently make investments on behalf of individuals, as well as scholars must fully understand the driving forces behind sustainable finance [6].

2

Literature Review

2.1

Sustainable Finance

The broad idea of sustainable finance serves as the basis for financial management. It is a component of a firm’s overall management [7]. Although the terms business finance and corporate finance are sometimes used synonymously, it’s crucial to remember that business finance covers a wider range of topics, including things like company ownership, operations, and partnerships [8]. Corporate finance is the foundation of financial management and focuses solely on a company’s financial elements. Financial management essentially entails making choices about the acquisition of assets, financing, and increasing shareholder value [9]. Another widely held belief is that good financial management is essential because it makes business strategies more efficient. Responsible management and monitoring of cash flows are essential components of adequate financial management, which in turn foretells increases in productivity [10] Concerns about sustainability cover a wide range of aspects relating to economic, social, and environmental problems. several companies have modified their innovation, technology, operational procedures, and business frameworks in order to protect and grow their operations. To achieve this, businesses must carefully evaluate the effects of economic decisions, such as the purchase of assets, investments, raw materials, waste management, and environmental degradation. This is essential to preventing long-term damage to the environment and the communities on whom they depend while maintaining their output [8]. In order to encourage sustainability and growth inside businesses, financial management is essential. Its main responsibility is to provide internal controls and checks, which are essential given how quickly things

412

M. Rani and R. Singh

are progressing. stressed the need for improved decision-making based on accounting data that is both historical and predictive. The incorporation of cutting-edge technological tools like artificial intelligence has shown its crucial significance in establishing a consistent information foundation that fosters confidence and ensures compliance with the requirements necessary for achieving long-term corporate goals and sustainability. Whether a corporation is in its infancy or getting ready for financial liquidity, sound financial management is always important. As a result, firms need to constantly adapt to new conditions. The pursuit of sustainability should center on actions that ensure its continuation. Innovative strategies that strike a balance between maximizing profits and environmental protection are needed for sustainable financial management models. Concerns about sustainability and financial decision-making are frequent themes in financial institutions. Due to the fact that many businesses are not actively pursuing economic growth, these problems are represented in sustainability reports. Organizations frequently experience systemic financial crises over time. It is essential to overcome management challenges by improving financial performance in order to improve sustainability in every area of organizational expansion and progress. Models for sustainable financial management must incorporate cutting-edge strategies that seek to find a balance between profit maximization and environmental protection. Concerns with financial decision-making and sustainability are widespread and include problems with financial institutions’ consideration of sustainability themes. Because so many businesses are reluctant to actively participate in economic growth programs, sustainability reports continue to face difficulties. Companies regularly experience systemic financial crises over time. Improving financial performance is necessary to address management concerns and improve sustainability across all facets of organizational growth and development. In order to maximize benefits and give environmental considerations top priority within financial institutions, businesses should also expand their investments in financial innovation. For many different economic sectors, integrating environmentally friendly practices into business operations should be a top focus [11]. The majority of businesses have responded creatively to the elements that contribute to unfavorable climate effects as a result of the increased worldwide awareness of climate change in recent decades [12]. In order to achieve sustainable development, technology adoption, particularly artificial intelligence, has gained prominence across all corporate processes. External pressure from communities to embrace environmental responsibility has led to an increased emphasis on company sustainability and economic sustainability. Large-scale global problems like oil spills by businesses have sparked discussions among environmental activists about the value of ecological sustainability. The public’s expectations of organizations’ environmental responsibility have increased as a result of these problems and related ones. Corporate sustainability has been implemented by many businesses, albeit occasionally at a hefty cost. In order to advance future sustainability, good financial management practices are crucial [13]. Numerous studies highlight how important financial management is to corporate success. The idea of sustainable

Role of Artificial Intelligence in Sustainable Finance

413

development first surfaced more than 50 years ago, but it wasn’t until 33 African countries, as members of the International.

2.2

Artificial Intelligence

In several sectors, including healthcare, education, and research, AI is steadily gaining ground. The financial management department must accept and improve the expanding use of AI in its operations in order to support community sustainability and meet social needs. It’s crucial to recognize that AI has its own set of restrictions, making it necessary for all stakeholders to work together to address the problems resulting from its use. The technology known as AI is being used more frequently as a result of the growing worries and potential threats related to climate change. Numerous businesses are reallocating their financial resources to initiatives that support sustainable growth in response to the effects of climate change [14]. Numerous businesses have included sustainable practices in their operations as a result of the worldwide push for sustainability [15]. The introduction of AI has significantly altered a number of industries, including productivity, healthcare, and the environment. AI has a mixed record of effects on long-term sustainability. It has transformed how business is conducted in the finance sector, maximizing factors including time effectiveness, cost savings, and value creation [16]. Leading international companies that supply improved services have emerged as a result of AI’s application in technical developments. AI is used in finance to spot irregularities and departures from the norm. For instance, it is capable of spotting early warning signals of security problems, and financial fraud, and providing shrewd remedies and sustainable investment prospects. Through the use of proprietary algorithms, AI can gather and incorporate data on market changes, enabling automatic replies. The ability of AI to expedite international efforts in resource conservation and environmental preservation is what is driving its acceptance in commercial operations [14]. Additionally, AI is essential for monitoring deforestation, building eco-friendly supply chains, detecting energy emissions reduction, lowering atmospheric CO2 levels, and finding efficient remedies for health emergencies. In order to analyze massive data and develop activities for environmental preservation, ‘s wide use of data and information is essential. It may be able to comprehend how climate change may affect a firm’s financial management in the future. The use of low-carbon energy sources, which is crucial in the fight against climate change, can be facilitated by AI. Planning for resources and financing for sustainable development may be impacted by the use of AI in financial management [17]. Artificial intelligence (AI) can swiftly spot patterns and trends in desertification across broad areas, giving useful data for environmental planning, important decisions, and resource allocation to halt or reverse these trends. Machine learning and computer vision, for example, work together to improve business efficiency and cut down on energy use. Similarly, the use of AI has streamlined processes.

414

2.3

M. Rani and R. Singh

Sustainable Finance and Financial Performance

The majority of nations today support the idea that businesses should be accountable to their stakeholders. These businesses must balance economic, environmental, societal, and governance factors in order to succeed [18]. Organizations are under pressure from environmental activists to adopt sustainability principles and show how they integrate ecological, socioeconomic, and governance factors into their daily operations. In order for businesses to gain the public’s trust and confidence, it is essential to communicate sustainability strategies [19]. Numerous disciplines of study have looked into how sustainability reporting affects business performance [20]. Consumers’ and investors’ interest in corporate sustainability has prompted several businesses to emphasize and promote it more. Businesses with a poor track record of disclosing their environmental performance frequently receive criticism from the political and social spheres, which can damage their reputations in the marketplace. Some businesses excel in this area by taking an active role in sustainability activities, such as supporting neighborhood projects and funding the access of disadvantaged people to healthcare and education [21] another contributing element is their involvement with external sources of finance like the stock market, which encourages companies to reveal information about their environmental sustainability initiatives in order to raise money from external stakeholders. Most businesses have increased their attempts to publish environmental information in recent years to improve their chances of obtaining outside finance for their projects. The importance of thorough research on how crucial sustainability factors affect corporate performance is well acknowledged. Research has consistently shown that companies that actively promote environmental sustainability perform better as a whole. Implementing sustainability plans, including technology and artificial intelligence also improves business performance [22]. This beneficial relationship between sustainability and several facets of financial management gives businesses strong justification for integrating sustainability into their operations. Indicators including earnings yields (EY), return on assets (ROA), and return on equity, or ROE, significantly improve when sustainable growth rates are higher, according to the research having a high return on capital employed (ROCE) is significantly aided by both enhanced sustainable growth rate values and the disclosure of corporate social responsibility [20]. According to metrics like return on assets (ROA) and other indicators, companies that adopted sustainable practices in their operations saw significant improvements in their financial performance, according to a 2008 study that looked at 100 companies globally [23]. Companies who did not adopt similar practices, on the other hand, did not see equivalent benefits.

Role of Artificial Intelligence in Sustainable Finance

3

415

Research Method and Objectives

The researcher used a rigorous search technique utilizing numerous internet databases, including Research Gate, Google Scholar, Web of Science, and Scopus in our effort to conduct a complete analysis of sustainable finance. The researcher selected a collection of terms that were frequently used in earlier works on green finance in order to identify the research that was the most pertinent. These terms include ‘green banking. Green bond, sustainable finance carbon emission, etc.’. The research concentrated on articles appearing in all reputable journals. The researcher focuses on a total of 59 articles. Several papers provided an in-depth analysis of sustainable finance. The researcher continued to note the significant elements of sustainable finance in these papers as the study developed the positioning of the paper. The study is based on the ‘Domain Based Review’ which further focuses on a Structured review of the literature. The paper intends to investigate the following aspects: (a) How financial management affects sustainability. (b) Evaluating the impact of sustainability on financial performance. (c) Examining how financial management, sustainability, and AI technology are related.

4

Result and Discussion

It is critical to evaluate the capabilities of AI systems in the field of financial management. The goal of this review is to use AI to successfully address finance performance and business difficulties. It also entails determining the knowledge and abilities that financial managers will need in order to effectively manage intelligent systems. By becoming experts at using these AI technologies, accountants can improve their skills and provide investors with financial reports of the highest caliber. To promote sustainable finance, AI has the ability to combine algorithms, fuzzy models, predictive models, and data analytics. Investor organizations are putting more and more pressure on businesses and accountants to provide more thorough financial reporting. Investment management is referred to as ESG (Environmental, Social, and Governance) data integration. However, this definition, which largely focuses on ESG considerations, is regarded as being extremely limited by Kumar et al. in 2022. Therefore, it is recommended in this Special Issue (SI) that the word “sustainable finance” be used to refer to all actions and elements that contribute to both financial and overall sustainability, including the use of AI models, applications, and systems.

416

5

M. Rani and R. Singh

Findings and Suggestions

Effective financial management techniques are essential for raising production and reducing financial risks, it is clear across many industries. Research has also shown important developments in artificial intelligence, which have made it possible to create powerful financial tools that are upending the financial industry. Nowadays, businesses are crucial to maximizing earnings while embracing an adaptable approach that takes into account both social and environmental concerns. The globe has struggled with climate-related issues during the past few decades, such as forest fires, pandemic disasters, and rising temperatures, underscoring the severity of climate change. As proposed by Ahamed in 2018, many stakeholders, including activists, regulatory agencies, corporations, and governments globally, are urged to adopt sustainable methodologies and technology to address issues with economic, social, and environmental sustainability. Companies are required to develop and implement sustainable strategies that have a good social impact and protect the environment as the globe struggles to cope with the effects of climate change. Although it is impossible to deny the existence of climate change, some businesses have neglected to implement sustainable practices into their daily operations, which has resulted in environmental damage (Al Breiki & Nobanee, 2019). The implementation of carbon emission reduction strategies by businesses has been pushed by external demands from regulatory agencies and environmental activists, particularly in carbonintensive sectors (Al-Blooshi & Nobanee, 2020). Many organisations and governments are utilising technology to address the growing concerns related to climate change. Companies are prioritizing technical advancements like artificial intelligence (AI) for identifying and resolving environmental challenges as a result of the increasing demand to address climate change. Today, AI is used to recognize and respond to challenges such as extreme weather, emissions of carbon dioxide, deforestation, wildfires, and more (Al Breiki & Nobanee, 2019). Therefore, it is increasingly important for firms to set up a coordinated and effectively designed financial management system that complies with both public and environmental criteria.

6

Implication of the Study

Various facets of finance and sustainability are affected significantly by artificial intelligence (AI), including sustainable finance. The following major themes emphasize the importance of AI for environmentally friendly finance: (a) Improvements in Risk Management: For the purpose of evaluating environmental, social, and governance (ESG) hazards, AI can analyze enormous amounts of data from several sources. This lessens the possibility

Role of Artificial Intelligence in Sustainable Finance

417

that financial institutions or investors would make investments in activities that are socially or environmentally unpopular or damaging. (b) Changing Investment Techniques: Investment possibilities that are in line with sustainability objectives can be found using AI-driven algorithms. Identifying businesses with outstanding ESG performance, green bonds, renewable energy initiatives, and other sustainable assets are all part of this process. (c) Climate Risk Evaluation: By simulating the possible effects of climate change on portfolios and assets, AI can help in the evaluation of climate risk. Thus, proactive risk mitigation and adaptation measures are possible. (d) Data ESG Analysis: Large amounts of ESG data may be processed and analyzed by AI more quickly than by using conventional techniques. It aids in locating pertinent ESG elements and determining how they affect financial performance. (e) Automated Reporting and Compliance: Financial institutions can save time and money by automating compliance with sustainability rules and reporting obligations. This improves sustainability standards compliance and lowers the danger of sustainable finance. (f) Enhancing Asset Management: Retail investors may have access to sustainable investment options through AI-driven robo-advisors and portfolio management tools. These tools can offer individualized investment guidance that considers a person’s ESG preferences. (g) Decrease Greenwashing: AI can assist in spotting and preventing “greenwashing,” in which businesses make misleading claims about being sustainable. AI can spot inconsistencies by examining a company’s actual practices and contrasting them with its sustainability promises.

418

M. Rani and R. Singh

(h) Additional Due Diligence: By automatically evaluating the ESG performance of possible investments, AI-powered solutions can help with due diligence procedures, making it easier for asset managers and investors to examine sustainability criteria. (i) Cost-Saving Measures: By automating regular operations like data collection, processing, and reporting, artificial intelligence can save operating costs in sustainable finance. Financial firms can distribute resources because of this.

7

Conclusion

This study looked at the relationship between financial management and the advancement of AI and sustainable growth. A thorough analysis of the most recent literature demonstrates that many businesses have been forced to incorporate sustainability into their operations due to mounting external pressures and the impending problems associated with climate change. Forging public trust, luring potential investors, and increasing profitability depends on adopting sustainability components. In order to embrace and put sustainable practices into practice, financial management plays a crucial role. The assessment has also emphasized the benefits that adopting sustainable practices can have for businesses, including improved financial performance, as mentioned by Ahamed in 2018. To support their efforts in the field, many businesses have made significant investments in artificial intelligence technologies. One of the most significant advances in science has been the development of artificial intelligence, which has gone from theoretical study to actual use in a variety of sectors. Currently, AI is a major driver of economic growth in the world’s main countries, particularly in the area of financial management. Artificial intelligence (AI) has developed over the past few decades into an essential instrument for financial management that actively promotes sustainable development.

References 1. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda. International Journal of Information Management, 53, 102104. 2. Chayjan, M. R., Bagheri, T., Kianian, A., & Someh, N. G. (2020). Using Data Mining for Prediction of Retail Banking Customer’s Churn Behaviour. International Journal of Electronic Banking, 2(4), 303–320.

Role of Artificial Intelligence in Sustainable Finance

419

3. Musleh Al-Sartawi, A. M. A., ed. 2022. Artificial Intelligence for Sustainable Finance and Sustainable Technology. ICGER 2021. Lecture Notes in Networks and Systems, Vol. 423. 4. Memdani, L. (2020). Demonetisation: A Move Towards Cashless Economy in India. International Journal of Electronic Banking, 2(3), 205–211. 5. Al-Sartawi, A. (2021). The Big Data-Driven Digital Economy: Artificial and Computational Intelligence. In Studies in Computational Intelligence, 974, 1–20. 6. Krüger, P., Sautner, Z., & Starks, L. T. (2020). The Importance of Climate Risks for Institutional Investors. Review of Financial Studies, 33, 1067–1111. 7. Ahamed, N. (2018). Does working capital determine firm performance? Empirical research of the emerging economy. Corporate Governance and Sustainability Review, 2(1), 14–33. 8. Bagaeva, A. (2020). Financial management: Managing the finances of an enterprise. Trends in The Development Of Science And Education. 9. Ardillah, K. (2020). The impact of environmental performance and financing decisions to sustainable financial development. Proceedings of the 3rd Asia Pacific Management Research Conference (APMRC 2019). 10. Migliorelli, M., & Marini, V. (2020). undefined. Palgrave Studies in Impact Finance, 93- 118. 11. Diez-Cañamero, B., Bishara, T., Otegi-Olaso, J. R., Minguez, R., & Fernández, J. M. (2020). Measurement of corporate social responsibility: A review of corporate sustainability indexes, rankings. 12. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and the future of artificial intelligence. California Management Review, 61(4), 5–14. 13. Mhlanga, D. (2021). Artificial intelligence in Industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lesson from emerging economies? Sustainability, 13(11), 5788. 14. Savina, S., & Kuzmina-Merlino, I. (2015). Improving financial management system for multibusiness companies. Procedia - Social and Behavioral Sciences, 210, 136–145. 15. Stein Smith, S. (2019). Artificial intelligence. Blockchain, Artificial Intelligence and Financial Services, 83–99. 16. Zhang, H., Song, M., & He, H. (2020). Achieving the success of sustainability development projects through big data analytics and artificial intelligence capability. Sustainability, 12(3), 949. 17. Joseph, O. A., & Falana, A. (2021). Artificial intelligence and firm performance: A robotic taxation perspective. The Fourth Industrial Revolution: Implementation of ArtificiaI Intelligence for Growing Business Success, 23–56. 18. Migliorelli, M., & Marini, V. (2020). undefined. Palgrave Studies in Impact Finance, 93–118. 19. Weber, O. (2017). Corporate sustainability and financial performance of Chinese banks. Sustainability Accounting, Management and Policy Journal, 8(3), 358–385. 20. Pham, D. C., Do, T. N., Doan, T. N., Nguyen, T. X., & Pham, T. K. (2021). The impact of sustainability practices on financial performance: Empirical evidence from Sweden. Cogent Business & Management, 8(1), 1912526. 21. Pikus, R., Prykaziuk, N., & Balytska, M. (2018). Financial sustainability management of the insurance company: Case of Ukraine. Investment Management and Financial Innovations, 15(4), 219–228. 22. Rodriguez-Fernandez, M. (2016). Social responsibility and financial performance: The role of good corporate governance. BRQ Business Research Quarterly, 19(2), 137–151. 23. Ameer, R., & Othman, R. (2011). Sustainability practices and corporate financial performance: A study based on the top global corporations. Journal of Business Ethics, 108(1), 61–79.

Optimizing Processes in Digital Supply Chain Management Through Artificial Intelligence: A Systematic Literature Review Zaher Najwa, Ghazouani Mohamed, Aziza Chakir, and Chafiq Nadia

Abstract

Digital supply chain management practices have a significant impact on the success of contemporary businesses. With the advancement of digital technology and artificial intelligence (AI), the application of AI-based solutions to improve these processes has assumed growing significance. In recognition of the importance of digital supply chain management based on AI, a thorough literature analysis was conducted to examine the benefits and challenges of doing so. This review intended to provide a full overview of the current state of research by highlighting the potential advantages of adding AI into supply chain management operations, such as greater forecasting accuracy, increased operational efficiency, and better decision-making skills. The assessment also made clear the hurdles and problems related to the use of AI in supply chain management, including change management, integration concerns, and data quality. The systematic literature review underlined the need of utilizing AI in digital supply chain management in order to increase efficiency and achieve competitive advantages in today’s rapidly changing corporate environment. Z. Najwa (B) · G. Mohamed · C. Nadia Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco e-mail: [email protected] A. Chakir Faculty of Law, Economic and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco e-mail: [email protected] Z. Najwa · Z. Najwa · G. Mohamed · A. Chakir · C. Nadia Laboratory of Sciences and Technologies of Information and Education LASTIE Faculty of Sciences, BEN M’SIK, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_23

421

422

Z. Najwa et al.

Keywords

Supply chain management • Digitalization • Purchase logistics • Artificial intelligence • Process optimization

1

Introduction

1.1

Rationale

Since over 20 years ago, supply chain management has occupied a prominent position in the minds of many businesses as a complete approach to gaining competitive advantages by boosting the efficacy and efficiency of the value chain. In order for a supply chain to be effective and profitable, all participants must strive for global optimization by identifying a goal and allocating as little resources as possible to it, as well as by integrating the information system with the overall strategy of the supply chain. In the age of digitization, all companies whose digital innovations have an impact on their supply chain management difficulties must be flexible and adaptable. Numerous research on digital technology in supply chain management have been conducted over the years. There have been analyses that have focused on the notion of the digitization of supply chain management without addressing how to optimize the operations.

2

Objectives

This article will discuss the pertinent studies in order to understand the significance of digitalization in supply chain management. Many definitions of “supply chain management,” “modeling,” and “digitalization” will be looked at, categorized, and summarized. Then, we will present the various supply chain modeling approaches as well as the various configurations and definitions of digitalization and supply chain management in order to establish a coherent framework for understanding the phenomenon, its dimensions, and the primary digital advancements required to achieve process optimization. The primary focus of the review will be on the following research questions: The primary focus of the review will be the following research questions: 1. How does digitization affect supply chain management? 2. What measurements do digital technologies have? 3. What types of restrictions are there on research into supply management using digital technologies? 4. How can supply chain management processes be made more efficient?

Optimizing Processes in Digital Supply Chain Management Through …

423

These research questions serve as the framework for the subsequent sections of this essay. In the following section, we’ll go over the methodology that was employed for this review and how the papers were picked. We next present the results and address the research questions based on the publications selected for this literature review. After pointing out potential shortcomings in this study, we provide the findings and recommendations for future research on digital supply chain management to improve the procedures.

3

Method

We prepared, conducted, and reported the review in accordance with [1]’s recommendations for the SLR procedure. Four research questions were asked in order to produce research on digital supply chain management that would enhance the processes and offer a fair summary and interpretation of the data. At the commencement of the review, the research questions should be clearly established as a target to be attained. The databases utilized for the search are then provided along with the search criteria and criteria for evaluating and choosing studies. Finally, the publications that were included at the end of the process are introduced. Selecting, identifying, and synthesizing are the three steps that make up the process [2].

3.1

Search Strategy

Below is a more thorough list of the literature sources and search terms that were used: Six electronic databases (IEEE Xplore, ACM Digital Library, ScienceDirect, Web of Science, EI Compendex, and Google Scholar) were used in our search for primary research in the literature. Using the previously developed search criteria, conference papers and journal articles were looked up in the six electronic data-bases. The search phrases were changed to fit different databases because the search engines of different databases use different syntaxes for search strings. On the first five databases, searches were conducted using the title, abstract, and keywords. Just the titles were searched because a full text search on Google Scholar would turn up millions of irrelevant items. We restricted the search to the period beginning on January 1, 2012, because as of this day, it adequately included contemporary literature.

424

3.2

Z. Najwa et al.

Study Selection

The process of choosing a study comprises a number of steps, including sorting through the citations that the search has produced and obtaining full reports of those that might be relevant. The most recent advances and research findings on the topic of digital supply chain management was sought after due to the rapid development of digital technologies. 600 objects were discovered during the initial search. Research questions influenced our study in a significant way. The works that had only been published in English in the previous ten years, from 2012 to 2022, were also included in this reevaluation. It included an acceptable amount of recent content. There were no articles in the review that did not respond to the stated research question(s). The following inclusion and exclusion criteria were devised and were enhanced by pilot selection. We chose the articles for our research by reading their titles, abstracts, or full contents. 1. Inclusion criteria and exclusion criteria a. Inclusion Criteria; The two main topics of this study are medialization and digital supply chain management. In the study, current best practices for controlling the digital supply chain are discussed. Research papers are published between 2012 and 2012. The pieces of research are written in English. Before being published, the research pieces were subjected to peer review. The study corresponds to the appropriate research framework in accordance with the research methodology. b. Exclusion Criteria: In the context of higher education, the study project has nothing to do with instructors’ or students’ degrees of digital competency. The article does not cover the most recent advancements in digital supply chain management. There are no research article publications between 2012 and 2022. Research publications are not written in English. Research articles have been published in the past without going through a peer review process. By applying the selection criteria, we discovered 100 relevant papers. After applying the quality rating criteria to the selection process, we ultimately decided on 14 papers to be used as the studies for which data would be extracted. This made it possible for us to guarantee the quality of the works selected to answer the study objectives (Fig. 1).

Optimizing Processes in Digital Supply Chain Management Through …

425

Fig. 1. Data extraction procedure

4

Results and Discussion

In this section, we delve into the findings of the review, offering insights and addressing the research questions by thoroughly analyzing the selected papers. The organization of this section mirrors the questions we set out to investigate, allowing us to present the outcomes of the systematic literature review (SLR).

4.1

What’s the Role of Digitalization in Supply Chain Management?

In order to clarify the roles that each performs in supply chain management, seven of the 14 papers that were selected employed research to define the terms “digitalization” and “supply chain management” separately. Research on supply chain management and logistics is expanding. However, the introduction of innovations may have an impact on this tendency [3].

426

Z. Najwa et al.

In a society that is becoming more digital, the rise in innovations that are driven by technology could lead to a lack of knowledge about significant research issues and the innovation strategies employed by businesses. In the digital age, where innovations are aplenty [4] and where the economic climate necessitates assessing the opportunity to invest in one innovation or another, it is impossible to predict how innovation will improve supply chain management logics. According to [5], digitalization is a transition that needs to be handled with great caution. Every link in the supply chain must cooperate as a result of digitization because without everyone’s help and cooperation, the supply chain cannot succeed. The digital model therefore entails integrating all of the supply chain’s connections, activities, functions, processes, and locations. Conventionally, it is believed that a supply chain’s partners perform better the more integrated they are. The organizational change of various supply chain tasks, such as planning, scheduling, and execution tasks, is helping to radically change the conventional way of performing the same tasks in the past and is creating the opportunity for competition and the creation of new business models. Digital technologies are having an impact on the development of supply chain processes. The majority of these selected works offered general definitions of supply chain management digitalization.

4.2

What Are the Dimensions of Digital Technologies?

The 14 selected publications employed diverse tools and measures, each with its unique set of dimensions. This diversity is due to the fact that a digital supply chain (DSC) represents an intelligent, value-centric process aimed at creating novel revenue streams and enhancing organizational value by harnessing innovative techniques powered by digital and analytical technologies. The DSC approach uses a variety of cutting-edge technology to manage supply chain operations. The use of digital technology has positive effects on the economy, conserving resources like energy and materials, thanks to the rationalization of production and consumption as well as the perspective of environmental rehabilitation and restoration.

4.3

What Kinds of Limitations Exist in Research on Digital Technologies in Supply Chain Management?

Forms of limits on the investigations. The use of the data collection approach was discovered to be the most prevalent constraint in the 14 research that were selected (n = 14). Then, it was shown that the sample size was a limitation on the study that frequently appeared in papers (n = 12), and the researchers also found that they could not disregard the lack of reliable and accessible data (n = 7). The remaining seven publications were

Optimizing Processes in Digital Supply Chain Management Through …

427

chosen because they did not disclose their research limits, although seven of them had numerous research restrictions.

5

Conclusion

This systematic study shows how the concept of digitalization is defined and used in supply chains in addition to giving an overview of recent research with the use of digital technology and artificial intelligence. As supply chains become more global in scope and involve a larger number of partners and suppliers, their ability to adapt and innovate will become more and more important to their success. The potential for “end-to-end” digitalization is the driving force behind this innovation. The adoption of new technology has advantages and disadvantages for the company. However, new technologies take into account user demands and expectations. In fact, digitization makes the supply chain more visible and makes it possible to standardize processes and products. In order to gain distinct competitive advantages and better meet customer expectations with the help of these new technological breakthroughs, such as Big Data, the Internet of Things (IoT), and Cloud Computing, businesses need be aware of the significance of new business models. Realizing that these technologies enable improved supply chain management and control throughout the whole product lifecycle, resulting in decreased production lead times, improved product quality, increased operational efficiency, and increased customer satisfaction [6]. The most crucial component of supply chain design has been deemed innovation in research studies and conversations. Companies run the risk of losing money and going out of business if they don’t adopt innovations and technological advancements right away. This study has some limitations that outline numerous potential research possibilities and give advice on how to use digital technologies to optimize supply chain operations. In order to manage and optimize the process, we will work on the formulation and verification of the consultation file for tender projects in the case of purchase logistics in the public sector with the use of AI.

References 1. Junge, A. L., & Straube, F. (2020). Sustainable supply chains – Digital transformation technologies’ impact on the social and environmental dimension. Procedia Manufacturing., 43, 736–742. https://doi.org/10.1016/j.promfg.2020.02.110 2. Cardona, A., Carusi, C., & Bell, M. M. (2021). Engaged intermediaries to bridge the gap between scientists, educational practitioners and farmers to develop sustainable agri-food innovation systems: A US case study. Sustainability, 13, 11886. https://doi.org/10.3390/su132111886

428

Z. Najwa et al.

3. Piancastelli, C., & Tucci, M. (2020). The role of digital twins in the fulfilment logistics chain. IFAC-PapersOnLine., 53, 10574–10578. https://doi.org/10.1016/j.ifacol.2020.12.2807 4. Lam, W. S., Lam, W. H., & Lee, P. F. (2023). A Bibliometric analysis of digital twin in the supply chain. Mathematics, 11, 3350. https://doi.org/10.3390/math11153350 5. Bowersox, D., Closs, D., & Drayer, R. (2005). The digital transformation: Technology and beyond. Supply Chain Management Review, 9. 6. Boyd, D. & Crawford, K. (2011). Six provocations for big data. Computer (Long. Beach. Calif), 123. https://doi.org/10.2139/ssrn.1926431.

Enhancing Hotel Services Through Sentiment Analysis Soumaya Ounacer, Abderrahmane Daif, Mohamed El Ghazouani, and Mohamed Azzouazi

Abstract

Sentiment analysis in hotel reviews has become a prominent field of research in computer science, closely tied to machine learning and deep learning. The surge of online platforms like TripAdvisor, Booking, and Yelp has led to active sharing of consumer experiences and emotions regarding hotels, generating vast amounts of data. Researchers employ a range of techniques, including traditional machine learning techniques such as SVM, Naive Bayes and Logistic Regression, alongside deep learning models like RNN, LSTM, and BERT, to evaluate their efficacy in extracting customer emotions related to hotel services. The findings consistently demonstrate that BERT often surpasses traditional methods in both precision and contextual understanding. This advancement empowers hotel businesses to better cater to their customers’ needs and significantly enhance overall satisfaction. Keywords

Sentiment analysis • Hotel reviews • Machine learning • Deep learning Transformers • BERT

•

S. Ounacer (B) · A. Daif · M. E. Ghazouani · M. Azzouazi Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sik, Hassan II University, Casablanca, Morocco e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. Chakir et al. (eds.), Engineering Applications of Artificial Intelligence, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-50300-9_24

429

430

1

S. Ounacer et al.

Introduction

The advent of the digital age has fundamentally reshaped interactions between consumers and businesses, paving the way for unprecedented sharing of experiences. Online platforms and social media have created a virtual space where customers can freely express their opinions, evaluate products and services, and share their experiences. This trend is particularly pronounced in the hospitality sector, where travelers, whether casual tourists or business travelers, actively turn to websites and applications to seek information about hotels, read reviews, and make informed decisions for their stays. Online reviews, often posted on specialized platforms such as TripAdvisor, Booking.com, Yelp, and many others, have become an invaluable source of information for consumers seeking memorable experiences. They also play an essential role for hotel managers who aim to assess and improve the quality of their services [1]. Customer reviews, expressed freely and spontaneously, reflect public opinion about hotels and can have a significant impact on their reputation and business success. This is where sentiment analysis comes into play, a branch of artificial intelligence that falls under both machine learning and natural language processing (NLP) [2]. It proves to be a powerful tool for extracting valuable information from this abundance of online reviews. It not only categorizes these reviews into sentiment categories such as “positive,” “negative,” or “neutral” but also extracts more detailed information. For example, it can identify specific aspects of a customer’s experience that contributed to their overall sentiment, such as service quality, cleanliness, food quality, location, amenities, and more. Sentiment analysis thus provides a means to delve into customer reactions and the factors influencing their satisfaction levels. The information obtained can be used to guide hotel improvement efforts, address customer concerns, and create more positive and memorable experiences. BERT, as a pre-trained language model, is an essential component of sentiment analysis in hotel customer comments and reviews [3]. Thanks to its contextual understanding of words, its ability to handle long sentences, and detect linguistic nuances, it enables the extraction of rich and accurate information from these reviews. This technology is crucial for the hotel industry as it enhances service quality, better understands customer needs, and facilitates informed decision-making to ensure positive experiences. The structure of the manuscript is as follows: Sect. 2 provides an overview of various studies that address sentiment classification in hotel customer reviews. Section 3 outlines the suggested methodology for tackling the sentiment analysis problem. Section 4 showcases the achieved results, and lastly, Sect. 5 summarizes our key findings and conclusions.

Enhancing Hotel Services Through Sentiment Analysis

2

431

Related Works

In the landscape of recent research focused on sentiment analysis in the context of hotel customer reviews, several studies have emerged, exploring innovative methodologies aimed at extracting meaningful insights from online comments. Among these studies, one has delved into the application of advanced natural language processing techniques. In this study [4], the emphasis was placed on conducting sentiment analysis and summarizing hotel reviews from the TripAdvisor website. The primary goal of this research was to extract essential information from customer comments, going beyond the often-available raw numerical ratings. To achieve this, researchers implemented advanced natural language processing techniques, including Topic Modeling (LDA) and sentiment analysis. These methods enabled the categorization of reviews into predefined categories corresponding to different aspects while revealing valuable insights not immediately evident. This study offers several significant advantages, including enlightening analysis that goes beyond raw ratings, the utilization of advanced NLP techniques such as sentiment analysis and topic modeling, and the development of a custom Python web scraper for large-scale data collection. Furthermore, researchers devised predefined aspect categorization for targeted analysis, utilized topic modeling to uncover latent information, performed sentiment analysis, and provided review summaries for each aspect, facilitating customer comment understanding. However, this study does come with certain limitations, including a limited dataset based on TripAdvisor, incomplete metadata availability, dependence on user-provided aspects, overlapping sentences grouped under a single category, concerns related to review credibility, and user interaction requirements for customization. Additionally, handling user-generated, unstructured data presents various challenges related to varying writing styles and user-specific requirements. In this work [5], the authors focus on sentiment analysis of texts and sentences using a machine learning-based sentiment classification technique. The main objective of this research is to extend the limits of traditional sentiment analysis by adopting a multi-class approach, meaning categorizing texts or sentences into multiple distinct categories such as “happy,” “sad,” “hungry,” “love,” and many others, instead of being limited to binary or ternary classification (positive/negative/neutral). This innovative approach provides greater granularity in understanding expressed opinions in texts, allowing for a more nuanced analysis of sentiments. One of the major advantages of the article is its adaptability to social media, where massive data generation occurs daily. Understanding opinions expressed on social media platforms is crucial for businesses seeking to evaluate the public’s perception of their products or services. Additionally, the article pays particular attention to data preprocessing, emphasizing the importance of cleaning and properly preparing data to obtain high-quality sentiment analysis results. The authors implement techniques such as tokenization, stemming, and the removal of unnecessary words to reduce data complexity.

432

S. Ounacer et al.

The use of machine learning, especially the Naïve Bayes theorem as a sentiment classification algorithm, is a well-established and widely used approach in the article. This method allows for relatively quick results with acceptable accuracy, especially when appropriate datasets are used. However, the article has some limitations, including a lack of in-depth comparison with other methods and insufficient details on model performance evaluation measures. Additionally, the use of a single dataset to validate the approach could limit the generalization of results to other domains or languages. Finally, the article does not specifically address noisy data handling, which can impact the quality of sentiment analysis results. In [6], the focus is on the application of machine learning algorithms, particularly the Naïve Bayes algorithm, and Opinion Mining techniques based on Natural Language Processing (NLP) to perform sentiment analysis. The primary goal of this study is to predict the overall rating assigned to a hotel derived from customer feedback and ratings. The advantages of this approach lie in improving the understanding of customer sentiments towards hotels, which can assist hotel businesses in making informed decisions to enhance their services based on customer feedback. Moreover, the use of machine learning algorithms, such as the Naïve Bayes algorithm, allows for efficient processing of large volumes of review data, leading to accurate and rapid results. This method also offers potential applications in other business domains to understand customer sentiments towards various products and services, while helping companies guide their marketing decisions and enhance their online reputation. However, the article has certain limitations, including dependence on the quality of collected review data, the use of a lexicon-based approach for sentiment analysis, which may be less precise than other machine learning-based methods, and potential bias in online reviews, which can influence sentiment analysis results. Additionally, binary classification of sentiments into positive and negative categories may not always capture all the nuances expressed by customers in their reviews. The modeling techniques used in this article primarily include machine learning, sentiment classification, and the utilization of an NLP analyzer [7]. These techniques are applied to extract features from customer reviews and then perform sentiment classification into negative and positive categories, using the Naïve Bayes algorithm. In [8], the main objective is to leverage multi-aspect sentiment analysis to facilitate hotel selection by travelers based on their needs and preferences. This study proposes an innovative approach that combines Random Forest, SVM, and Naïve Bayes methods, demonstrating improved performance compared to using a single classification algorithm. This research contribution opens up avenues for new methodologies and could significantly enhance the relevance of recommendations to travelers. By focusing on specific aspects such as room quality, location, cleanliness, check-in process, and service, this approach aims to streamline the decision-making process for tourists. Furthermore, the use of real-world data from TripAdvisor enhances the credibility of the results, making them more relevant and applicable in real-world situations. Nevertheless, this study has

Enhancing Hotel Services Through Sentiment Analysis

433

some limitations, including the restriction to analyzing only five aspects, potential nongeneralizability of results, and dependence on subjective user reviews. Additionally, the lack of in-depth qualitative analysis and detailed comparison with other sentiment analysis approaches are points to consider. Considering these contributions, it is evident that sentiment analysis in the realm of hotel customer reviews is continually evolving. Each study brings new perspectives and underscores the importance of innovation in this expanding field. This research lays the foundation for future work aimed at improving the understanding of customer sentiments and assisting businesses in better catering to their needs and preferences.

3

Research Methodology

3.1

Proposed Workflow

For a clear understanding of our sentiment analysis experiment on hotel customer reviews, we have created a detailed workflow that outlines each step of the process. This workflow, depicted in the diagram below, serves as a visual guide to comprehend how we collected, preprocessed, analyzed, and evaluated the data. Data Collection: The first essential step in this is to collect data from online sources, specifically, hotel customer reviews on TripAdvisor. To accomplish this, we utilized the Selenium library, a web scraping tool, to extract relevant information from TripAdvisor web pages. Data Preprocessing: To ensure data quality and enhance usability, we perform a preprocessing step on the entire set of reviews. This step involves removing white spaces, special characters, punctuation marks, and other irrelevant elements, as well as normalizing the text format. Tokenization: Tokenization is a fundamental step in transforming raw text into structured data ready for analysis. It involves breaking the text into discrete units, typically words or phrases, called ‘tokens.’ In our analysis of hotel reviews, we utilized a BERT tokenizer to divide the text into tokens. BERT requires specific tokenization that considers subword structures. Embedding Creation: In this step, we convert each word in a text into a vector representation using a pre-trained model. These vectors, assigned to each subtoken by BERT, capture rich and contextual semantic information. This approach significantly enhances the accuracy of sentiment analysis by considering language context and nuances (see Fig. 1). Model Training: The actual training involves using the training dataset (reviews with BERT embeddings) to adjust the model’s parameters. The goal is for the model to learn

434

S. Ounacer et al.

Fig. 1 Workflow for sentiment analysis of hotel reviews

to predict sentiments accurately. During training, a loss function, such as cross-entropy, is minimized. Model Evaluation: In this phase, we assess the model’s performance by applying it to the embeddings of the test set. The model’s task is to predict the sentiment label for each review within the test set. To achieve this, we input the test set embeddings into the trained model. Subsequently, the model uses its learned patterns and analyses the embeddings to assign a sentiment label (positive, negative, neutral) to each review. The aim of this evaluation is to measure how effectively the model performs on new and unseen data. By comparing its predictions to the true sentiment labels in the test set, we can measure the model’s accuracy and its ability to correctly classify sentiments in hotel reviews. This evaluation helps us understand the model’s performance and whether it can effectively analyze sentiment in hotel reviews beyond the data it was trained on.

3.2

BERT

In 2018, Google AI research team unveiled BERT, short for Bidirectional Encoder Representations from Transformers [9]. This remarkable advancement has now become the standard approach for various natural language processing tasks, such as machine translation, text generation, and automatic summarization. BERTis a model for representing texts written in natural language. BERT’s representation is contextual. In other words, a word is not represented statically, as in conventional embedding, but according to the meaning of the word in the context of the text. What’s more, BERT’s context is bidirectional, i.e. the representation of a word involves both the

Enhancing Hotel Services Through Sentiment Analysis

435

words that precede it and the words that follow it in a sentence. The principle of using BERT is quite simple: it is “already” pre-trained on a large amount of data, we modify it for a specific task and then (re)train it with our own data. Bert is a Transformer model. A transformer is a model that operates efficiently, executing a consistent and limited number of steps. During each step, it utilizes an attention mechanism to comprehend the connections among words in a sentence, without being constrained by their specific positions. BERT [9] is therefore based on the architecture of transformers, i.e. consisting of an encoder to read the text and a decoder to make a prediction. BERT is limited to an encoder, as its aim is to create a language representation model that can then be used for NLP tasks. The BERT model consists of two crucial steps [10]: pre-training and fine-tuning. Pretraining revolves around two main unsupervised tasks: the Masked Language Model (MLM) and Next Sentence Prediction (NSP). In the fine-tuning phase, BERT utilizes the self-attention mechanism of the Transformer to tailor its inputs and outputs to specific tasks. During fine-tuning, we begin by initializing the BERT model with pre-trained parameters and then employ all parameters for comprehensive end-to-end fine-tuning (see Fig. 2). Fig. 2 BERT architecture

436

S. Ounacer et al.

3.3

Comparison Between Traditional Machine Learning Techniques and BERT

3.3.1

Logistic Regression

Logistic regression [11] is a machine learning technique used to model and predict the polarity or sentiment (positive, negative, neutral) associated with customer reviews of hotels. It is a statistical model that models the probability that an observation (in this case, a customer review) belongs to a particular class (e.g., positive review or negative review). In our context, it typically takes input features extracted from customer reviews, such as keywords, phrases, or text embeddings, and seeks to learn a model that can predict the probability that each review belongs to a specific sentiment category. A decision function is then used to assign a sentiment class (e.g., positive, negative, neutral) to each review based on the calculated probability. Logistic regression has both advantages and disadvantages in the context of sentiment classification in hotel customer reviews [12]: Advantages: • Ease of Interpretation: Logistic regression provides coefficients for each feature, allowing for an understanding of the impact of each variable on sentiment prediction. This can be useful for result interpretation. • Good Performance with Linear Data: When data is linearly separable, logistic regression can offer good performance in terms of sentiment classification. • Suitable for Small to Medium-Sized Datasets: For small to medium-sized datasets, logistic regression can be efficient and does not require substantial computational power. • Lower Risk of Overfitting: Logistic regression is less prone to overfitting compared to more complex models, which can be beneficial when training data is limited. Disadvantages: • Linearity Assumption: Logistic regression assumes that the relationships between features and the target variable are linear. If the data is highly nonlinear, logistic regression may not be suitable. • Inability to Capture Complex Interactions: Logistic regression cannot capture complex interactions between features, which can be important for sentiment analysis when specific combinations of words or phrases influence sentiment. • Unsuitability for Large Textual Datasets: For very large textual datasets, logistic regression may lack the representation capacity needed to capture the complexity of natural language. • Sensitivity to Outliers: Logistic regression can be sensitive to outliers in the data, which can negatively impact its performance.

Enhancing Hotel Services Through Sentiment Analysis

3.3.2

437

SVM

The training algorithm of a Support Vector Machine [13] builds a model to classify new instances into one of two classes, acting as a non-probabilistic binary linear classifier. The SVM model depicts instances as points in a space, adeptly mapping them to maximize the separation between instances from different categories [14]. Consequently, new instances are projected onto the same space and classified into a category based on their relative position with respect to the separation gap. This approach aims to maximize the margin between different categories, making SVMs effective for tasks like classification and regression. SVM is another commonly used technique for sentiment classification in hotel customer reviews. Here are its advantages and disadvantages in this context [15]: Advantages: • Effective in high-dimensional spaces: SVMs are efficient in handling datasets with a large number of features, which is common in sentiment analysis where each word or n-gram can be considered a feature. • Good generalization: SVMs are known for their ability to generalize well from a limited training dataset, which can be useful when training data is scarce. • Handling non-linear data: By using non-linear kernels, SVMs can handle non-linear data and capture complex relationships between features. • Robust to outliers: SVMs are generally robust to outliers in the data, which can be beneficial when customer reviews contain exceptional comments. Disadvantages: • Hyperparameter tuning complexity: SVMs have hyperparameters such as kernel choice and regularization parameter C that require careful tuning to achieve good performance. Finding optimal hyperparameter values can be a complex task. • Interpretability challenge: SVMs do not provide easily interpretable coefficients like logistic regression, making it difficult to understand the relative importance of features. • Sensitivity to feature scaling: SVMs are sensitive to feature scaling, meaning you often need to normalize or scale your data before using it with an SVM. • Need for labeled data: Like most supervised learning algorithms, SVMs require a labeled dataset for training, which can be costly to obtain. Overall, logistic regression can be a reliable option for sentiment classification in hotel customer reviews, particularly when dealing with linear data and requiring model interpretability. However, it may face limitations when attempting to model nonlinear relationships or handling extensive textual datasets. SVMs, on the other hand, provide several advantages in sentiment analysis but present challenges related to hyperparameter tuning and feature interpretability.

438

S. Ounacer et al.

Bidirectional Encoder Representations from Transformers offers several advantages over traditional machine learning techniques, especially when it comes to analyzing sentiment in hotel customer reviews [16, 17]: • Contextual Understanding: BERT is a pre-trained natural language processing (NLP) model that comprehends the context of words within a sentence. Unlike traditional models that treat words in isolation, BERT considers relationships and context, leading to a better understanding of sentence and review meanings. • Handling Polysemy: BERT can differentiate between multiple meanings of the same word based on context. This is crucial for understanding the nuances of human language, especially in customer reviews where words can have different meanings depending on context. • Managing Long Sequences: Customer reviews can be lengthy, and BERT can effectively process longer text sequences, unlike some traditional techniques that may struggle with longer texts. • Outstanding Performance: BERT has demonstrated exceptional performance in numerous NLP tasks, including sentiment analysis. It often outperforms traditional methods, leading to more accurate results. • Versatility: BERT finds utility in numerous NLP tasks like text classification, sentiment analysis, question answering, and more. Its remarkable adaptability allows for finetuning tailored to specific tasks, rendering it versatile. • Reduced Feature Engineering: Unlike traditional methods that often require extensive feature engineering, BERT can learn directly from raw data, reducing the workload associated with data preparation. • Multilingual Capability: BERT is available in multiple languages, making it a strong choice for sentiment analysis in reviews written in different languages. • Massive Pre-training: BERT is pre-trained on vast text corpora, allowing it to capture rich contextual information. This massive pre-training helps it generalize effectively to specific tasks. BERT stands out as the preferred option for sentiment analysis and various other NLP tasks. This preference is attributed to its strengths in contextual comprehension, polysemy management, performance, and adaptability.

Enhancing Hotel Services Through Sentiment Analysis

4

Experimentation

4.1

Datasets Used

439

The dataset includes 14,356 English reviews gathered from the TripAdvisor website for 10 different hotels located in Marrakech. An example of this dataset is illustrated in Fig. 3. This dataset encompasses the following fields (see Table 1). In this research, we have opted to center our investigation on the city of Marrakech. Consequently, we have employed the BERT model, a renowned Natural Language Processing (NLP) model celebrated for its exceptional performance [18]. Moreover, the

Fig. 3 Example of datasets

Table 1 Description of a review for a hotel on tripadvisor Variable name

Description

Hotel_ Name

Indicates the hotel’s name

Title_review Refers to the title written by the customer to provide a general summary of their review or feedback about the hotel Reviews_ hotel

Comprises textual reviews that are composed in English

Score_ rating

Represents the evaluation given by each customer, ranging from 10 to 50

Rating_date Indicates the date when a review was posted

440

S. Ounacer et al.

BERT model demonstrates its advantage in delving into the nuanced context of review text, leading to improved predictions of attribute ratings based on user preferences. With its comprehensive pre-training on a large corpus, we have confidence in BERT’s capability to effectively address the emotion detection challenge.

4.2

Performance Measure

To assess the models’ performance on the test data, various performance metrics will be employed [19]. The subsequent (Table 2) illustrates the frequently employed performance metrics. Accuracy: It calculates the number of accurate predictions in relation to the total sample size, a frequently used metric to assess the overall effectiveness of a classification model. Precision: is a performance metric commonly used in classification tasks, particularly in machine learning and information retrieval. It measures the accuracy of the positive predictions made by a model. is a crucial metric when the cost of false positive errors is high, and you want to ensure that the positive predictions made by your model are highly reliable. It is often used in conjunction with other metrics like recall and F1-Score to provide a more comprehensive assessment of a model’s performance in classification tasks. Recall: measures a model’s ability to identify all positive examples. It is important when the detection of false negatives is costly or critical. F1-Score: When a combination of both precision and recall needs to be considered in a single metric, the F1-Score is used, which is the harmonic mean of these two measures. Specifity: It measures the ability of a model to correctly identify negative instances or the “true negatives” in a dataset. Specificity is also known as the True Negative Rate (TNR). is particularly important when the cost of false negatives, such as failing to identify actual Table 2 Evaluation metrics

Measure Accuracy Precision F1-score Recall Specificity

Formula VP +VN V P + FP + V N + FN VP V P + FP 2 ∗ recall ∗ precision recall + precision VP V P + FN TN TN + FP

Enhancing Hotel Services Through Sentiment Analysis

441

negatives, is high, and there is a need to ensure that the model effectively distinguishes between positive and negative cases.

4.3

Results

BERT has exhibited higher precision scores compared to the other two models, indicating its ability to accurately predict sentiments expressed in hotel reviews. Logistic Regression achieved a slightly higher precision score of 0.80, suggesting that it likely leveraged the inherent linearity of features for precise predictions. SVM achieved a precision score of 0.84, indicating its effectiveness in capturing underlying patterns and relationships in the data, resulting in more accurate sentiment predictions. The accuracy of the BERT model is reported at 0.91, signifying that approximately 91% of the reviews in the evaluation dataset were correctly classified by the model. The classification report (Table 3) provides valuable insights into the performance of the sentiment analysis model based on BERT’s application. When examining precision values, it is evident that the model excels in classifying the positive sentiment class with a precision score of 0.96. The precision for the negative sentiment class is also relatively high, reaching 0.81, surpassing both Logistic Regression and SVM, which achieve 0.79 and 0.81, respectively. This suggests that the model possesses a strong ability to identify negative sentiments. However, the precision score for the neutral sentiment class is relatively lower, at 0.50. This implies that the model tends to misclassify some neutral reviews as either positive or negative. Further analysis may be necessary to understand the reasons for this misclassification and identify potential areas for improvement. The classification report highlights the strengths and weaknesses of the sentiment analysis model based on BERT’s application. While the model demonstrates outstanding Table 3 Comparison of BERT with traditional machine learning techniques

Metrics

Logistic regression

SVM

BERT

Accuracy

0.80

0.84

0.91

Precision (Negative)

0.79

0.81

0.81

Precision (Positive)

0.87

0.89

0.96

Precision (Neutral)

0.39

0.41

0.50

Recall (Negative)

0.70

0.78

0.87

Recall (Positive)

0.95

0.93

0.92

Recall (Neutral)

0.12

0.23

0.47

F1-score (Negative)

0.74

0.79

0.83

F1-score (Positive)

0.91

0.91

0.94

F1-score (Neutral)

0.18

0.29

0.48

442

S. Ounacer et al.

performance in classifying positive sentiments, it faces challenges in accurately identifying neutral sentiments. These findings offer valuable insights for further analysis and potential enhancements to the sentiment analysis model.

5

Conclusion

Sentiment analysis in hotel reviews is emerging as a dynamic and highly relevant research field within computer science. The proliferation of online platforms has opened new avenues for understanding customer experiences and emotions, particularly in the context of hotel services. The consistent pattern that emerges from our analysis is the outstanding performance of BERT compared to traditional methods. BERT’s superior precision and contextual understanding make it a powerful tool for extracting nuanced sentiments from hotel reviews. This advancement has significant implications for the hotel industry, enabling businesses to better grasp customer preferences and sentiments. By leveraging sentiment analysis, hotel establishments can tailor their services to align with customer expectations, ultimately resulting in increased satisfaction and customer loyalty. As technology continues to evolve, further research in this field promises even more sophisticated methods for understanding and enhancing the hotel guest experience. However, while BERT has demonstrated excellent performance, opportunities for improvement remain. This may involve finer hyperparameter tuning, exploring new model architectures specifically tailored for sentiment analysis, or the use of custom BERT variants designed for this task. These efforts aim to enhance model precision and versatility. Future research should also focus on extending these models to support multiple languages and effectively analyze multilingual reviews. This will enable a more comprehensive analysis of customer experiences worldwide. Furthermore, another important perspective is Aspect-Based Sentiment Analysis (ABSA), which involves analyzing sentiments based on various aspects of an experience. This approach helps in understanding how customers perceive different aspects such as food quality, service, cleanliness, and more. Therefore, it is crucial to further explore this approach to gain finer insights into customer preferences.

References 1. Ounacer, S., Mhamdi, D., Ardchir, S., Daif, A., & Azzouazi, M. (2023). Customer sentiment analysis in hotel reviews through natural language processing techniques. International Journal of Advanced Computer Science and Applications, 14(1), 569–579. https://doi.org/10.14569/IJA CSA.2023.0140162 2. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4

Enhancing Hotel Services Through Sentiment Analysis

443

3. Alaparthi, S., & Mishra, M. (2020). Bidirectional encoder representations from transformers (BERT): A sentiment analysis odyssey (No. 1, pp. 1–15). https://doi.org/10.17762/turcomat. v12i7.3055 4. Akhtar, N., Zubair, N., Kumar, A., & Ahmad, T. (2017). Aspect based sentiment oriented summarization of hotel reviews. Procedia Computer Science, 115(May 2020), 563–571. https://doi. org/10.1016/j.procs.2017.09.115 5. Sharad, D. A., Ashok, D. S., Dattatray, D. P., & Bhagwat, M. P. (2018). Sentiment analysis of hotel review. International Journal of Advanced Research in Computer and Communication Engineering, 7(11), 174–176. https://doi.org/10.17148/IJARCCE.2018.71139 6. Bhargav, P. S., Reddy, G. N., Chand, R. V. R., Pujitha, K., & Mathur, A. (2019). Sentiment analysis for hotel rating using machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering (IJITEE), (6), 1225–1228. https://doi.org/10.1007/ 978-3-030-58669-0_21 7. Kibble, R. (2013). Introduction to natural language processing Undergraduate study in Computing and related programmes. 8. Ananda, I. P., Utama, M., Prasetyowati, S. S., & Sibaroni, Y. (2021). Multi-aspect sentiment analysis hotel review using RF SVM, and Naïve Bayes based Hybrid Classifier. Jurnal Media Informatika Budidarma, 5(April), 630–639. https://doi.org/10.30865/mib.v5i2.2959 9. Thomassey, S., Sleiman, R., & Tran, K. P. (2022). Natural language processing for fashion trends detection. In International conference on electrical, computer and energy technologies (ICECET). https://doi.org/10.1109/ICECET55527.2022.9872832 10. Jawahar, G., Sagot, B., & Seddah, D. (2019). What does BERT learn about the structure of language? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3651–3657). https://doi.org/10.18653/v1/P19-1356 11. Aliman, G., et al. (2022). Sentiment analysis using logistic regression. Journal of Computational Innovations and Engineering Applications, (July), 35–40. https://doi.org/10.9790/9622-110702 3640 12. Kaur, S., & Mohana, R. (2016). Prediction of sentiment from textual data using logistic regression based on stop word filtration and volume of data. International Journal of Control Theory and Applications, 9(45), 1–8. https://doi.org/10.3390/app13074550 13. Pinem, F. J. (2018). Sentiment analysis to measure celebrity endorsement’s effect using support vector machine algorithm (pp. 16–18). https://doi.org/10.1109/EECSI.2018.8752687 14. Cheng, H. (2019). Mitotic cell detection in H&E stained meningioma histopathology slides (No. December). 15. Patil, M. G., Galande, M. V., Kekan, V., & Dange, M. K. (2014). Sentiment analysis using support vector machine (pp. 2607–2612). https://doi.org/10.1109/IC3I46837.2019.9055645 16. Sun, C., Huang, L., & Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence (pp. 380–385). https://doi.org/10.18653/v1/N19-1035 17. Samir, A., Elkaffas, S. M., Madbouly, M. M. (2021). Twitter sentiment analysis using BERT. In 31st international conference on computer theory and applications (ICCTA) (pp. 11–13). https:// doi.org/10.1109/ICCTA54562.2021.9916614 18. Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X.: Pre-trained models for natural language processing: A survey. https://doi.org/10.1007/s11431-020-1647-3 19. Sowell, T. (2021). Magician’s corner: 9. Performance metrics for machine learning models. Radiology: Artificial Intelligence, 55905, 1–7. https://doi.org/10.1148/ryai.2021200126