Recent Advances in Computer Based Systems, Processes and Applications: Proceedings of National Conference on Recent Advances in Computer based Systems, Processes and Applications (RACSPA-2019), Vellore Institute of Technology, Amaravati, India, 22–23 October 2019 9781003043980, 1003043984

Table of contents :
Cover
Title Page
Copyright Page
Table of Contents
Foreword
Committees
About the Editors
Computer Based Systems
01: A Short Survey of Dimensionality Reduction Techniques
02: A Brief Visit to the Landscape of Cloud DDoS Attacks
03: Comparative Study of Image Colorization Neural Network Models
Computer Based Processes
04: Range Doppler ISAR Imaging using Chirp Pulse
05: Trust-Based Hybrid Ids for Rushing Attacks in Wireless Mesh Networks
06: Text Mining from Internet Resources using Information Retrieval Techniques
07: Image Based Centroid Selection for Clustering
Computer Based Applications
08: Single Valued Triangular Neutrosophic Fuzzy C-Means for Mr Brain Image Segmentation
09: Real-Time 2D Avatar Lip Syncing for the on Demand Interactive Chatbots
10: DCNN-SVM: A New Approach for Lung Cancer Detection
11: Multimodal Medical Image Fusion Based on Gradient Domain Guided Image Filter
12: A Brief Analysis on the Top Performing Companies Across the Globe
13: Smart Home Security System
14: Brain Tumour Classification using Convolution Neural Networks
15: Prediction of Crop Yield using Deep Learning Techniques: A Concise Review
16: Design of Mems Model to Study the Radiation Effects on Brain Tumour
17: An Improved Telecommunication Churn Prediction System by PPFCM Clustering Hybrid Model
18: Grammar Expert an Automated Essay Scoring Application
Index

Citation preview

RECENT ADVANCES IN COMPUTER BASED SYSTEMS, RECENT PROCESSES ADVANCES IN AND APPLICATIONS

COMPUTER BASED SYSTEMS, PROCESSES AND APPLICATIONS Edited by

Anupama Namburu

Soubhagya SankarOF Barpanda PROCEEDINGS RECENT ADVANCES IN COMPUTER BASED SYSTEMS, PROCESSES AND APPLICATIONS (RACSPA-2019), OCTOBER21-22, 2019

Edited by Anupama Namburu and Soubhagya Sankar Barpanda

PROCEEDINGS OF NATIONAL CONFERENCE ON RECENT ADVANCES IN COMPUTER BASED SYSTEMS, PROCESSES AND APPLICATIONS (RACSPA-2019), VELLORE INSTITUTE OF TECHNOLOGY, AMARAVATI, INDIA, 22–23 OCTOBER 2019

RECENT ADVANCES IN COMPUTER BASED SYSTEMS, PROCESSES AND APPLICATIONS Edited by Anupama Namburu Vellore Institute of Technology - Andhra Pradesh

Soubhagya Sankar Barpanda Vellore Institute of Technology - Andhra Pradesh

First published 2020 by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN and by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 © 2020 selection and editorial matter, Anupama Namburu and Soubhagya Sankar Barpanda; individual chapters, the contributors CRC Press is an imprint of Informa UK Limited The right of Anupama Namburu and Soubhagya Sankar Barpanda to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan). British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data ISBN: 978-1-003-04398-0 (ebk)

TABLE OF CONTENTS

Foreword Committees About the Editors

vii ix xi

Computer Based Systems

1

01. A Short Survey of Dimensionality Reduction Techniques V. Lakshmi Chetana, Soma Sekhar Kolisetty and K. Amogh

3

02. A Brief Visit to the Landscape of Cloud DDoS Attacks B. Raj kumar and Amogh Katti

15

03. Comparative Study of Image Colorization Neural Network Models Sudhakar Putheti and Kurmala Gowri Raghavendra Narayan

27

Computer Based Processes

33

04. Range Doppler ISAR Imaging using Chirp Pulse G. V. Sai Swetha, P. Anjali Reddy and Dr. A. Naga Jyothi

35

05. Trust-Based Hybrid Ids for Rushing Attacks in Wireless Mesh Networks K. Ganesh Reddy and P. Santhi Thilagam

49

06. Text Mining from Internet Resources using Information Retrieval Techniques Z. Sunitha Bai1, Dr. M. Sreelatha 07. Image Based Centroid Selection for Clustering Gagan Kumar Koduru, Prof. Nageswara Rao Kuda and Anupama Namburu Computer Based Applications

59 73

79

08. Single Valued Triangular Neutrosophic Fuzzy C-Means for Mr Brain Image Segmentation Anupama Namburu, Sibi chakkaravarthy, Meenalosini Vimal Cruz and Hari Seetha 09. Real-Time 2D Avatar Lip Syncing for the on Demand Interactive Chatbots Venkata Susmitha Lalam, Abhinav Dayal, Sajid Vali Rehman Sheik and Vinay Kumar Adabala

v

81

89

Table of contents

10. DCNN-SVM: A New Approach for Lung Cancer Detection Bibhuprasad Sahu, Amrutanshu Panigrahi and Dr. Saroj Kumar Rout 11. Multimodal Medical Image Fusion Based on Gradient Domain Guided Image Filter K. Vanitha, D. Satyanarayana and M. N. Giri Prasad

97

107

12. A Brief Analysis on the Top Performing Companies Across the Globe Kalluri Lakshmi Prathyush and Hari Kishan Kondaveeti

117

13. Smart Home Security System Vemireddy Sai sindhu reddy, P. V. K. Sai and Anupama Namburu

127

14. Brain Tumour Classification using Convolution Neural Networks Prathibha Goriparthi, Srinu Madhav V. and Narendra M.

135

15. Prediction of Crop Yield using Deep Learning Techniques: A Concise Review Geetha Pratyusha Miriyala and Arun Kumar Sinha 16. Design of Mems Model to Study the Radiation Effects on Brain Tumour Shameem syed, Arunmetha S., Sri Haritha C.H., Chandana Sindhu G. and Sambasivarao K. 17. An Improved Telecommunication Churn Prediction System by PPFCM Clustering Hybrid Model Vijaya J., Srimathi S., Karthikeyan S. and Siddarth S. 18. Grammar Expert an Automated Essay Scoring Application Vikash Varma Sayyaparaju, Ramya Bhargavi Pichukala, Vidyadhari Tummalapalli and Abhinav Dayal, Index

145

161

169 177

185

FOREWORD On behalf of organising committee, we welcome you to the first annual conference on Recent Advances in computer based systems, processes and Applications (RACSPA 2019), held on 21st–22nd October 2019. This is the first conference organized by the school of Computer Science Engineering in VIT-AP University campus with the cumulative efforts of all the faculty members. We are very ambitious with this conference as in coming years, we are planning for organising this conference at international level, with paper indexing in reputed publishing house. The RACSPA-2019 shall create an environment to discuss recent advancements and novel ideas in areas of interest. During the conference participants will have opportunities to discuss issues, ideas and work that focus on a topic of mutual concern. Presentations can cover topics such as advances in computer based systems, processes and Applications. With the valuable contribution of renowned personalities in the field, five keynotes will be included in the conference of the RACSPA-2019. We strongly say that the participants will revive with new ideas of research. We have selected 23 papers for the two days sessions with high quality with thorough plagiarism check and reviews. 18 papers will be published in Taylor and Francis proceedings; selected and extended papers will publish in Scopus indexed journal. We thank our management for supporting us in conducting the conference. We are very grateful to Vice-Chancellor VIT-AP, Prof. D. Subhakar and Management of VITAP, for their support and encouragement to RACSPA 2019; we extend our heartfelt thanks to the advisory board for their suggestion and support. We extend our deep sense of gratitude to the keynote speakers for sharing and motivating the participants with the keynote address. We wish all a productive, stimulating conference and a memorable stay at the conference.

Organizing committee School of computer science engineering Vellore Institute of Technology-Andhra Pradesh, University Amaravati, Andhra Pradesh, India

vii

Recent Advances in Computer based Systems, Processes And Applications © 2020 by Taylor & Francis Group, London, ISBN 978-1-003-04398-0

COMMITTEES

CHAIR

•

Dr. Anupama Namburu, VIT-AP University, Andhra Pradesh

CO-CHAIR

•

Dr. Soubhagya Sankar Barpanda, VIT-AP University, Andhra Pradesh

PROGRAM CHAIR

•

Dr. Amogh katti, VIT-AP University, Andhra Pradesh

TECHNICAL COMMITTEE

• • • • • • • •

Dr. Hari Seetha, VIT-AP University, Andhra Pradesh Dr. Pradeep Reddy, VIT-AP University, Andhra Pradesh Dr. Srinivas battula, VIT-AP University, Andhra Pradesh Dr. D. Nagaraju, VIT-AP University, Andhra Pradesh Dr. D. Sumati, VIT-AP University, Andhra Pradesh Dr. Ravi Sankar Sangam, VIT-AP University, Andhra Pradesh Dr. Vijaya J., VIT-AP University, Andhra Pradesh Dr. Sibi Chakkaravarthy S., VIT-AP University, Andhra Pradesh

ORGANIZING COMMITTEE

• • • • • • • • • • • • • •

Dr. Nandh Kumar R., VIT-AP University, Andhra Pradesh Ms. Garima Singh, VIT-AP University, Andhra Pradesh Ms. Ashwini Umakant Rahangdale, VIT-AP University, Andhra Pradesh Dr. Shruti Mishra, VIT-AP University, Andhra Pradesh Dr. Dr. Aravapalli Rama Satish, VIT-AP University, Andhra Pradesh Mr. Ravi Sankar Barpanda, VIT-AP University, Andhra Pradesh Mr. Asish Kumar Dalai, VIT-AP University, Andhra Pradesh Dr. Saroj Kumar Panigrahy, VIT-AP University, Andhra Pradesh Dr. Sudhakar Ilango S., VIT-AP University, Andhra Pradesh Dr. Prabha Selvaraj, VIT-AP University, Andhra Pradesh Mr. Balu Laxman Parne, VIT-AP University, Andhra Pradesh Dr. BKSP Kumarraju Alluri, VIT-AP University, Andhra Pradesh Dr. Mallikharjuna Rao, VIT-AP University, Andhra Pradesh Dr. Ganesh Reddy Karri, VIT-AP University, Andhra Pradesh

ix

Committees

• • • • • • • • • • • •

Dr. K. Gokulnath, VIT-AP University, Andhra Pradesh Mr. Hari Kishan Kondaveeti, VIT-AP University, Andhra Pradesh Dr. Jonnadula Harikiran, VIT-AP University, Andhra Pradesh Dr. Abhijit Adhikhari, VIT-AP University, Andhra Pradesh Dr. Sibi Chakkaravarthy S, VIT-AP University, Andhra Pradesh Ms. Deepasikha Mishra, VIT-AP University, Andhra Pradesh Mr. Sandipan Maiti, VIT-AP University, Andhra Pradesh Dr. Sunil kumar singh, VIT-AP University, Andhra Pradesh Mr. Nitesh Asaramji Funde, VIT-AP University, Andhra Pradesh Mr. Karthikeyan S., VIT-AP University, Andhra Pradesh Mr. Ajith Jubilson E., VIT-AP University, Andhra Pradesh Mr. Hussain Sayed, VIT-AP University, Andhra Pradesh

WEBSITE AND DIGITAL MEDIA

• •

R. Balaji Programmer, VIT-AP University, Andhra Pradesh Ravi Kumar Thota, VIT-AP University, Andhra Pradesh

ABOUT THE EDITORS Anupama Namburu has received her doctoral degree from the Department of Computer Science & Engineering, Jawaharlal Nehru technical university, kakinada, India. She has completed her M. Tech degree from the Andhra University. She has worked as Sr. Software Engineer in Wipro Technologies, Hyderabad for 4 years. Her research interests include Image processing, soft computing and fuzzy systems. She has published research articles in many journals and conferences of international repute. Currently, she is working as an associate professor in the School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh India. Soubhagya Sankar Barpanda has received his doctoral degree from the Department of Computer Science & Engineering, National Institute of Technology Rourkela, India. He has completed his M. Tech degree from the same institute. His research interests include biometric security and classical image processing. He has published research articles in many journals and conferences of international repute. Currently, he is working as an associate professor in the School of Computer Science and Engineering, VITAP University Amaravati, Andhra Pradesh India.

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Recent Advances in Computer based Systems, Processes And Applications © 2020 by Taylor & Francis Group, London, ISBN 978-1-003-04398-0

Computer Based Systems A Short Survey of Dimensionality Reduction Techniques V. Lakshmi Chetana, Soma Sekhar Kolisetty and K. Amogh

Brief Visit to the Landscape of Cloud DDoS Attacks Rajkumar Batchu and Amogh Katti

Comparative Study of Image Colorization Neural Network Models. Sudhakar Putheti and Kurmala Gowri Raghavendra Narayan

1

A SHORT SURVEY OF DIMENSIONALITY REDUCTION TECHNIQUES V. Lakshmi Chetana1, Soma Sekhar Kolisetty1 and K. Amogh2 1,2

School of Computer Science and Engineering, VIT-AP University, Amaravati - 522 237, Andhra Pradesh, India

ABSTRACT: Advancement in data collection has increased the availability of highdimensional data. High dimensional data results in data overload which makes the storage and processing complex. Most of the data mining and machine learning algorithms use dimensionality reduction techniques. Dimensionality reduction techniques convert the high -dimensional feature space to low-dimensional feature space to ease the storage and processing of the data. It further enhances the scalability of the machine learning algorithms. In this paper, we discuss various dimensionality reduction techniques used to reduce the feature space. Keywords: Dimensionality reduction, Data reduction, Feature relection, Feature extraction, Scalability

I.

INTRODUCTION

High-Dimensional data leads to data overload and plays an important role in many scientific and research applications. It considerably escalates the computational time and storage space requirements of data processing [1]. Many data mining and machine learning algorithms use dimensionality reduction to transform high dimensional space to low-dimensional space. Text categorization, image classification, intrusion detection, genome analysis, etc., are some of the applications where dimensionality reduction techniques are most commonly used. The dimensions of the pre-processed data usually are columns (features), rows (samples) and values for a feature. So, the three basic operations of dimensionality reduction, in general, would be deleting a row or a column and reducing the number of values for a column (by smoothing or binning). In this paper, we focus on column-based dimensionality reduction techniques. A feature is an individual and significant property of the input data that is considered for data analysis. It can also be called as column, attribute, characteristic or variable of a data set. The cardinality of features in a dataset is called its dimensionality. Most of the real-world applications do not require all the features of the high-dimensional data space because it may contain redundant, irrelevant and noisy data. Dimensionality reduction is effective in compressing the features by eliminating irrelevant and redundant data, which in turn improves the efficiency and performance of the model [2]. According to Bellman, as the dimensionality of the

3

Recent Advances in Computer based Systems, Processes and Applications

input data increases, the number of samples to be considered while training a model increases exponentially. This phenomenon is called “Curse of Dimensionality” which was coined in 1961. Most of the datasets with the so-called “large 𝑝, small 𝑛” problem (where 𝑝 is the number of features and 𝑛 is the number of samples) have more chances of overfitting [3,8]. An overfitted model makes the training model complex and results in poor performance. To address the curse of dimensionality, the pre-processing step introduced in high dimensional data analysis, visualization, and modeling is dimensionality reduction [4]. Apart from relevance, redundancy, and noise in the input data, most of the machine learning algorithms suffer from scalability problems due to the ever-increasing data size. Scalability is the capability of handling large scale high-dimensional data. For example, consider a recommendation engine that helps to personalize items to the users. Applications like Amazon, Netflix, LinkedIn, Facebook, Spotify, etc., personalize items to its users using some machine learning algorithms. But these algorithms may not be scalable when the size of the users and items increase [5]. Similarly, in a fault-tolerant large-scale distributed system, it becomes very critical to find the faulty node from the cluster when the number of nodes in a cluster is extremely large [6]. The scalability problem affects the processing speed, accuracy, and performance of the learning algorithm. Dimensionality reduction techniques can deal with scalability problems and help to produce fast and accurate results. The major goals of dimensionality reduction in machine learning are 1) To remove noisy, missing and redundant data. 2) To reduce the requirement of storage space. 3) To enable the speed of the learning model. 4) To build a model with better accuracy. 5) To decrease the model complexity. 6) To reduce overfitting. 7) To permit the visualization of the data and observe their patterns more clearly. II.

APPROACHES TO DIMENSIONALITY REDUCTION

The two most common types of dimensionality reduction techniques are feature selection and feature extraction, which is shown in Figure.1. Feature selection is a process of reducing the dimensional space by selecting the relevant subset of features for the analysis while feature extraction is a process of generating novel features by combining or transforming the existing ones [3]. A.

Feature Selection

Feature selection can be generally viewed as a search problem and it is defined as the process of selecting an optimal feature subset based on some criteria from the input feature set [7]. An optimal subset consists of a relevant and non-redundant features set that improves the performance of the model. The selection of the feature subset is based on Occam’s Razor concept [8]. It is a problem-solving principle which states that “select the simplest among various competing hypothesis that make the same predictions and with less assumptions”, i.e., the optimal feature set from various

4

A Short Survey of Dimensionality Reduction Techniques

feature subsets should be preferred and it is considered to be the best subset for prediction [9]. Let X = {x1,x2,…xn} be the dimensions of the initial data. Now, the task of feature selection is to find the best subset Y = {y1,y2,…ym} where m