Pandian a computer networks, big data and iot 2021

Lecture Notes on Data Engineering and Communications Technologies 66 A Pasumpon Pandian Xavier Fernando Syed Mohammed Shamsul Islam   Editors Computer Networks, Big Data and IoT Proceedings of ICCBI 2020 Lecture Notes on Data Engineering and Communications Technologies Volume 66 Series Editor Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain The aim of the book series is to present cutting edge engineering approaches to data technologies and communications It will publish latest advances on the engineering task of building and deploying distributed, scalable and reliable data infrastructures and communication systems The series will have a prominent applied focus on data technologies and communications with aim to promote the bridging from fundamental research on data science and networking to data engineering and communications that lead to industry products, business knowledge and standardisation Indexed by SCOPUS, INSPEC, EI Compendex All books published in the series are submitted for consideration in Web of Science More information about this series at http://www.springer.com/series/15362 A Pasumpon Pandian · Xavier Fernando · Syed Mohammed Shamsul Islam Editors Computer Networks, Big Data and IoT Proceedings of ICCBI 2020 Editors A Pasumpon Pandian Department of CSE KGiSL Institute of Technology Coimbatore, India Xavier Fernando Department of Electrical and Computer Engineering Ryerson University Toronto, ON, Canada Syed Mohammed Shamsul Islam Edith Cowan University (ECU) Joondalup, WA, Australia ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-981-16-0964-0 ISBN 978-981-16-0965-7 (eBook) https://doi.org/10.1007/978-981-16-0965-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021 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 Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore We are honored to dedicate the proceedings of ICCBI 2020 to all the participants and editors of ICCBI 2020 Foreword It is with deep satisfaction that I write this Foreword to the proceedings of ICCBI 2020 held in Vaigai College of Engineering, Madurai, Tamil Nadu, on December 15–16, 2020 This conference was bringing together researchers, academics and professionals from all over the world, and experts in computer networks, big data and Internet of things This conference particularly encouraged the interaction of research students and developing academics with the more established academic community in an informal setting to present and to discuss new and current work The papers contributed the most recent scientific knowledge known in the field of computer networks, big data and Internet of things Their contributions helped to make the conference as outstanding as it has been The local organizing committee members and their helpers put much effort into ensuring the success of the day-to-day operation of the meeting We hope that this program will further stimulate research in data communication and computer networks, Internet of things, wireless communication, big data and cloud computing and also provide practitioners with better techniques, algorithms, and tools for deployment We feel honored and privileged to serve the best recent developments to you through this exciting program We thank all the guest editors, authors and participants for their contributions Dr P Sugumaran Conference Chair, ICCBI 2020 vii Preface This conference proceedings volume contains the written versions of most of the contributions presented during the conference of ICCBI 2020 The conference provided a setting for discussing recent developments in a wide variety of topics including computer networks, big data and Internet of things The conference has been a good opportunity for participants coming from various destinations to present and discuss topics in their respective research areas This conference tends to collect the latest research results and applications on computer networks, big data and Internet of things It includes a selection of 74 papers from 248 papers submitted to the conference from universities and industries all over the world All of accepted papers were subjected to strict peer-reviewing by 2–4 expert referees The papers have been selected for this volume because of quality and the relevance to the conference We would like to express our sincere appreciation to all the authors for their contributions to this book We would like to extend our thanks to all the referees for their constructive comments on all papers; especially, we would like to thank the organizing committee for their hard work Finally, we would like to thank the Springer publications for producing this volume Coimbatore, India Toronto, Canada Joondalup, Australia Dr A Pasumpon Pandian Dr Xavier Fernando Dr Syed Mohammed Shamsul Islam ix Acknowledgements ICCBI 2020 would like to acknowledge the excellent work of our conference organizing the committee and keynote speakers for their presentation on December 15– 16, 2020 The organizers also wish to acknowledge publicly the valuable services provided by the reviewers On behalf of the editors, organizers, authors and readers of this conference, we wish to thank the keynote speakers and the reviewers for their time, hard work and dedication to this conference The organizers wish to acknowledge Dr R Saravanan, Dr R Thiruchenthuran, Thiru S Kamalakannan, Thiru S Balasubramanian and Thiru S Singaravelan, for the discussion, suggestion and cooperation to organize the keynote speakers of this conference The organizers also wish to acknowledge speakers and participants who attend this conference Many thanks are given to all persons who help and support this conference ICCBI 2020 would like to acknowledge the contribution made to the organization by its many volunteers Members contribute their time, energy and knowledge at a local, regional and international level We also thank all the chair persons and conference committee members for their support xi Contents Maximizing Network Lifetime in WSN Using Ant Colony Algorıthm M D Saranya, G Pradeepkumar, J L Mazher Iqbal, B Maruthi Shankar, and K S Tamilselvan Deep Ensemble Approach for Question Answer System K P Moholkar and S H Patil 15 Information Sharing Over Social Media Analysis Using Centrality Measure K P Ashvitha, B Akshaya, S Thilagavathi, and M Rajendiran 25 AndroHealthCheck: A Malware Detection System for Android Using Machine Learning Prerna Agrawal and Bhushan Trivedi 35 Use of Machine Learning Services in Cloud Chandrashekhar S Pawar, Amit Ganatra, Amit Nayak, Dipak Ramoliya, and Rajesh Patel 43 An Experimental Analysis on Selfish Node Detection Techniques for MANET Based on MSD and MBD-SNDT V Ramesh and C Suresh Kumar 53 Metaheuristic-Enabled Shortest Path Selection for IoT-Based Wireless Sensor Network Subramonian Krishna Sarma 71 Improved Harris Hawks Optimization Algorithm for Workflow Scheduling Challenge in Cloud–Edge Environment Miodrag Zivkovic, Timea Bezdan, Ivana Strumberger, Nebojsa Bacanin, and K Venkatachalam 87 xiii 976 M B Devi and K Amarendra Table Performance of ML algorithms with LBP features Model Accuracy Precision Recall F1 LR 80.24 80 80 80 Naïve Bayes 67.74 69 68 68 Decision tree 72.58 73 73 73 SVM—linear 81.05 81 81 81 SVM—RBF 81.85 82 82 82 HGB 83.87 84 84 84 Table Performance of ML algorithms with HOG features Model Accuracy Precision Recall F1 LR 73.39 73 73 73 Naïve Bayes 76.21 76 76 76 Decision tree 62.5 62 62 62 SVM—linear 77.98 79 78 79 SVM—RBF 84.27 84 84 84 HGB 85.47 85 85 85 Recall F1 Table Performance of ML algorithms with LBP features Model Accuracy Precision LR 79.44 79 79 79 Naïve Bayes 82.26 82 82 82 Decision tree 77.02 77 77 77 SVM—linear 80.24 80 80 80 SVM—RBF 83.47 83 83 83 HGB 84.27 84 84 84 From previous experiments, it is clear that after applying PCA, there is a significant improvement in the performance of classification models with both, HOG and LBP features HGB classifier followed the same trend and outperformed other classification models with both HOG and LBP features after applying PCA Even after reduced dimension, there is a significant improvement in all measures used to test the efficiency of models 4.3.3 Experiments with Fused Features of HOG and LBP This experiment is conducted to check the performance of ML models with composite representation obtained after blending LBP features with HOG features Machine Learning-Based Application to Detect Pepper Leaf … 977 Table Performance of ML algorithms with fused HOG and LBP features Model Accuracy Precision Recall F1 LR 80.24 80 80 80 Naïve Bayes 75.81 76 76 76 Decision tree 69.35 70 69 69 SVM—linear 81.05 81 81 81 SVM—RBF 88.71 89 89 89 HGB 89.11 89 89 89 From Table 5, it is clear that the HGB classifier trained on fused feature descriptor obtained 89.11% accuracy which is highest when compared with the performance of the same classifier trained on HOG and LBP features before and after applying PCA So, it can be concluded that fused texture representations of pepper leaf images help to identify diseases accurately rather than using conventional usage of LBP and HOG features Conclusion Pepper is the most recently used ingredient in dishes Identifying pepper leaf diseases is a challenge for farmers There is a high requirement to automate the process of detecting pepper leaf diseased for correct usage of pesticides and reduce the loss of yield In this paper, the performance of various classification models along with two different types texture-based features was investigated During our experiments, it can be observed that models can perform well with reduced representations of HOG and LBP features rather than using them directly The fused representation of HOG and LBP features helped the models to perform well, and there is a 4% improvement in accuracy with fused features In our experiments, it can also be observed that the HGB classifier outperforms other ML algorithms in every case References Husin ZB, Aziz AHBA, Shakaff AYBM, Farook RBSM (2012) Feasibility study on plant chili disease detection using image processing techniques In: IEEE 3rd international conference on intelligent system modeling and simulation ISMS, Kota Kinabalu, pp 291–296 Balakrishna G, Moparthi NR (2020) Study report on indian agriculture with IoT Int J Electr Comput Eng 10(3):2322 Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images Biosyst Eng 144:52–60 Mannepalli K, Sastry PN, Suman M (2017) Accent recognition 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Comprehensive survey on sentiment analysis based on workflow foundation J Adv Res Dyn Control Syst 10(9 Special Issue):1189–1120 11 Rajesh Kumar T, Vamsidhar T, Harika B, Madan Kumar T, Nissy R (2019) Students performance prediction using data mining techniques IEEE Explorer (ICISS-2019) 978-1-53867798-8 12 Talasila V, Rajesh Kumar T, Sai CP, Satya Sai S, Ayyappa (2019) Predicting the risk of heart failure with EHR sequential data modelling Int J Recent Technol Eng (IJRTE) 6(7):458–461 2277-3878 13 Asfarian A, Herdiyeni Y, Rauf A, Mutaqin KM (2013) Paddy diseases identification with texture analysis using fractal descriptors based on Fourier spectrum In: IEEE international conference on computer, control, informatics and its applications IC3INA, Jakarta, pp 77–81 14 Bommadevara HSA, Sowmya Y, Pradeepini G (2019) Heart disease prediction using machine learning algorithms Int J Innov Technol Exploring Eng 8(5):270–272 15 Khirade SD, Patil AB (2015) Plant disease detection using image 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nonlinear embedding space for fundus image classification deep convolution features in non-linear embedding space for fundus image classification 22 Bodapati JD, Shaik NS, Naralasetti V, Mundukur NB (2020) Joint training of two-channel deep neural network for brain tumor classification Signal Image Video Process 1–8 23 Bodapati JD, Veeranjaneyulu N, Shaik S (2019) Sentiment analysis from movie reviews using LSTMs Ingénierie Des Systèmes D Inf 24(1):125–129 24 Trivedi J, Shamnani Y, Gajjar R (2020) Plant leaf disease detection using machine learning In: International conference on emerging technology trends in electronics communication and networking Springer, Singapore, pp 267–276 25 Inthiyaz S, Prasad MVD, Lakshmi RUS, Sai NS, Kumar PP, Ahammad SH (2019) Agriculture based plant leaf health assessment tool: a deep learning perspective Int J Emerg Trends Eng Res 7(11):690–694 Machine Learning-Based Application to Detect Pepper Leaf … 979 26 Anila M, Pradeepini G (2017) Study of prediction algorithms for selecting appropriate classifier in machine learning J Adv Res Dyn Control Syst 9(Special Issue 18):257–268 27 Waghmare H, Kokare R, Dandawate Y (2016, February) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system In: 2016 3rd international conference on signal processing and integrated networks (SPIN) IEEE, pp 513–518 28 Shariff MN, Saisambasivarao B, Vishvak T, Rajesh Kumar T (2017) Biometric user identity verification using speech recognition based on ANN/HMM J Adv Res Dyn Control Syst 9(12 Special issue):1739–1748 Efficacy of Indian Government Welfare Schemes Using Aspect-Based Sentimental Analysis Maninder Kaur, Akshay Girdhar, and Inderjeet Singh Abstract One of the simplest methods to understand people’s thoughts using images or text is commonly given as sentiment analysis Sentiment analysis is used mostly in products advertisement and promotion depends on the user’s opinion The process is based on the aspect-based sentiment analysis and it is used to understand and find out what someone is speaking about, and likeness and dislikeness One of the real-world models of the perfect realm of this subject is the huge number of available Indian welfare plans like Swachh Bharat Abhiyan and Jan Dhan Yojna In this paper, labeled data is used on the basis of polarity Tweets are preprocessed and unigram features are then extracted In the initial steps, tokenization process, stop word removal process, and stemming process are performed as preprocessing to remove duplicate data The unigram features and labels trained by support vector machine (SVM), K-nearest neighbor (KNN), and a combination of SVM, KNN, and random forest as a proposed model are used in the presented work Implementation of experimental proposed approach demonstrates that better results in accuracy and precision than SVM and KNN Keywords Sentiment analysis · Aspect · Support vector machine · K-nearest neighbor M Kaur (B) Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, India A Girdhar Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, India e-mail: akshay_girdhar@gndec.ac.in I Singh Information Technology, Govt Polytechnic College, Bathinda, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021 A Pasumpon Pandian et al (eds.), Computer Networks, Big Data and IoT, Lecture Notes on Data Engineering and Communications Technologies 66, https://doi.org/10.1007/978-981-16-0965-7_74 981 982 M Kaur et al Introduction In natural language processing, sentiment analysis is considered as most significant tool because it opens up numerous possibilities to understand people’s sentiments on different topics The purpose of an aspect-based sentiment analysis is to detect the features of the particular entity The positive and negative aspects of a particular topic can be analyzed through aspect-based sentiment analysis [1] In this research paper, aspect-based sentiment analysis (ASBA) is implemented on the tweets of the government welfare schemes This type of analysis is mainly domain specific The government has launched the various welfare schemes for schools, states, as well as center The given outlines progress in collaboration with center and state governments The welfare schemes have been mostly introduced to develop the weaker and minority section of the society [2] Many schemes are launched but in this research work is on Swachh Bharat Abhiyan and Jan Dhan Yojna These schemes empower every Indian by helping them financially and providing the basic facilities [3] A Two Types of Sentiment Analysis Document Level: If the analysis is performed using documents to identify the positive and negative view of the single entity, then it is document level analysis [4] Comparative: In many cases, users express their views by comparing with the similar product or entity The main goal is to identify the opinion from the comparative sentence [4] The research work is structured as follows: Related works are presented in Sect 2, proposed model is presented in Sect 3, results and its discussions are presented in Sect 4, and finally, conclusion is presented in Sect Related Work Various approaches proposed by the researchers for sentiment analysis are presented in this section Shidaganti et al [5] presented a technique that used data mining along with machine learning as a combination process Clustering is done by using k-means clustering and hierarchical clustering approach This approach helps to analyze the data of different organizations which helps in understanding the thoughts and opinions related to the product Rout et al [6] presented the way to deal with unstructured data of social media like blogs, Twitter for sentiment and emotion analysis The supervised and unsupervised approaches are performed on different databases The unsupervised approach was used for automatic identification of sentiments for the tweets The sentiment identification is done by using maximum entropy, multinomial Naïve Bayes, and support vector machine classifier This approach also works well if it will apply on the larger dataset in future Mumtaz et al [7] presented an approach which is a combination of lexical-based approach and machine learning In this proposed work, hybrid approach was used, which gave high accuracy than Efficacy of Indian Government Welfare Schemes Using Aspect … 983 the classical lexical method and provides the enhanced redundancy than learning approach Natural language processing to extract sentiments from texts is performed in the approach Al-Smadi et al [8] worked on aspect-based sentiment analysis to review the hotels in Arabic Long short-term memory (LSTM) neural networks are used in the research model and it was implemented in two levels that are character level and aspect based with random field classifier and polarity classification based Chen et al [9] presented a visualization approach called Tag Net which is used for sentiment analysis This approach combines the improved node-link diagrams with tag clouds to obtain heterogeneous time varying information Large datasets scalability is improved using the proposed algorithm Fouad et al [10] presented a model for Twitter sentiment analysis which describes the tweet is positive or negative by using the concept of machine learning The presented work uses different methods to label the input in the training phase using different datasets The classification is also done by using different classifiers to compare their performances The concept of feature selection and information gain is used in this work Jones et al [11] focused on the implication of PMJDY scheme for the development all over India The main aim of scheme was to make India digitized, even the rural areas are conscious about their bank accounts to obtain government offering benefits directly Demonetization of currency and all these measures lead toward the progress in the structure of Indian economy This scheme ensures the better quality of life in the country and helps improve the living status of the people India is the only nation where 50% of people are in the working age group Khan et al [12] focused on the challenges that Twitter information faces, concentrating on order issues, and afterward consider these streams for supposition mining and sentiment analysis To manage gushing unequal classes, the sliding window Kappa measurement for assessment in time-changing information streams was used Utilizing this measurement an investigation is performed on Twitter information for utilizing learning calculations for information streams Greica et al [13] presented an analytic model which performs aspect classification, separation and polarity classification for analysis The testing of domain aspect and sentiment classification is performed on the multilingual SemEval dataset Pham et al [14] presented the multilayer architecture for representing customer review It extracts the views for product from the sentiment aspects and sentences related to review The multilayer architecture represents the different level of sentiments for input text This model is integrated with neural network for prediction of overall ratings of the product Rathan et al [15] worked on the review of mobile phones by using the tweets In this lexicon-based approach is used for data labeling and this improves the classification process The support vector machine classifier classifies the tweets with efficient accuracy level Kim et al [16] worked on the co-occurrence of data by using supervised and unsupervised methods A framework is used for processing texts reviews This work also finds the aspect categories according to review sentences and provides effective outcomes from the F-score Pannala et al [17] presented the existing work for the opinion mining that was done on the word level, not on the sentence level The work was done on the trained dataset In this author presents the combination of natural language (NL) and machine learning (ML) model to process the dataset which has 1654 aspects in the 984 M Kaur et al training dataset with different category annotations and 845 aspects in the test dataset with different category annotations for analysis The performance of software is measured by SVM and logistics regression algorithm Previously, work was done on the static parameters which reduce the effective learning of tweets according to its label The nonparametric approach uses number of coefficient parameters those increase the over fitting In proposed paper, the hyperparameters are used which reduces the adaptive features of learning The analysis is on the combined performance of parametric (SVM), nonparametric (KNN), and the random forest is used Methodology The data is collected for the experiment from Twitter and stored in a database for preprocessing After the preprocessing on the feature label, the processed data is learned by KNN, SVM, and proposed model which is the combination of (KNN, SVM, random forest) these By these respective models, tweets are predicted and classified, respectively The below-given section describes the proposed methodology of the model and the techniques used in this work in detail The pictorial representation of the proposed model is presented in Fig Step 1: Collection of data The proposed model used the data collected from the Twitter, regarding government welfare schemes as input People get easy access to financial and banking services due to Jan Dhan Yojna of government welfare schemes and the main aim of Swachh Bharat Abhiyan is to keep India clean [18]; tweets regarding both the scheme have been used to determine the public review based on aspect-based sentiment analysis The data that retrieved from the social media is in unstructured form due to intensive information Step 2: Data Storing and fetching The retrieved tweets are put in storage as csv files, and it is fetched using a Python tool PyCharm [19] Around 3000 tweets are stored for the purpose of training and testing the datasets Data mining algorithm (SVM, KNN, and hybrid SVM-KNN) is utilized to train and test the fetched tweets Step 3: Preprocessing The redundant and noise contents are removed in preprocessing step which makes the data suitable for training process [20] Data cleaning is performed based on the following steps (i) (ii) (iii) All the uppercase is converted to lowercase Remove all the Internet slangs from the data Removing all the stopping words from the list Efficacy of Indian Government Welfare Schemes Using Aspect … 985 Fig Proposed framework process flow (iv) (v) (vi) Eliminating all the additional white spaces Compress the duplicate words All the hashtags are removed but the hashtags texts are selected Step 4: Classification using data mining techniques Classification is performed using data mining techniques to categorize the data into various aspects such as First aspect: increase fund/decrease fund Second aspect: improvement in growth/not growth/growth Third aspect: goes really fast/works/hard fix Fourth aspect: incredible/good/not good On the basis of these parameters, the data is trained and tested Machine learning models are trained using the training datasets In the proposed framework, training data is implemented for classification, then testing dataset is prepared that is not before used in the proposed model for training 986 M Kaur et al Step 5: Optimize the classification results The classification results are need to be checked to confirm whether the learning models training datasets follow the defined rules or not This process is performed to obtain accurate and error-free results Python is used to train and test the proposed KNN-SVM random forest model The tweets nature is predicted as optimal value based on the classification results Results and Discussion The proposed model classification performance is evaluated and compared with conventional approaches to validate the better performance The experiment result validation is done by using fivefold and tenfold validation approach The data is divided into k subsets in cross validation which has equal size and the training process and testing process are repeated for k-times Every time one group of subset will be used for testing of data and other k-1 subsets of data will be treated as training data The result analysis is done on SVM, KNN, and the combination of proposed model (SVM, KNN and random forest algorithm) The parameters used in this for analysis process are accuracy, precision, recall, and F-measure The classification accuracy of all the three models is depicted in Fig It is observed that the proposed shows the maximum accuracy in both fold testing process KNN obtains the least accuracy performance of 49.23%, 51.23%, respectively, for both processes The precision analysis for proposed model and conventional SVM and KNN model is depicted in Fig The proposed algorithm shows the maximum precision and KNN obtains minimum precision 50.23, 51.23 in fivefold and tenfold validation process, respectively (Fig 4) The overall performance of all the classifiers is presented in figure It is observed that the proposed model (KNN-SVM-random forest) shows the optimum, enhanced results with 82% in comparison with the SVM and KNN Accuracy (%) Fig Accuracy comparison 80 70 60 50 40 30 20 10 SVM KNN Proposed Mode 5-Fold 10-Fold Folds Efficacy of Indian Government Welfare Schemes Using Aspect … Fig Precision analysis 987 Precision 100 80 SVM 60 KNN 40 Proposed Model 20 5-Fold 10-Fold Folds Values Fig Overall performance comparison 90 80 70 60 50 40 30 20 10 Accuracy Precision Recall F-Measure Classifiers Conclusion A hybrid classifier is presented in this research work as sentiment analysis model The proposed hybrid model obtains better results by utilizing part-of-speech (POS) tags and word dependencies for aspect-based sentiment analysis Efficacy of government welfare schemes using the proposed model is computed 82% under the restricted environment In future work, improvement in the accuracy will be considered by reducing the feature sparsely by divergence and optimization approaches, improve the classifier by deep learning approaches Additionally, other tasks that are Aspect Based Sentiment analysis also tested on the applicability on best of learning and concerns with the integration of POS tags, word dependencies, and possibly other NLP tools 988 M Kaur et al References Perikos I, Hatzily geroudis I (2017) Aspect based sentiment analysis in social media with classifier ensembles In: 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS), Wuhan, China, pp 273–278 Tiwari SK (2014) To study awareness of a national mission: Swachh Bharat: Swachh Vidyalaya in the middle school student of private and public schools Paripex-Indian J Res 3(12):23–24 Barhate GH, Jagtap VR (2014) Pradhan Mantri Jan Dhan Yojana: national mission on financial inclusion Indian J Appl Res 4(12):340–342 Lin Y, Zhang J, Wang X, Zhou A (2012) An information theoretic approach to sentiment polarity classification In: Proceedings of the 2nd joint WICOW/AIRWeb workshop on web quality, Lyon,France, 2012, pp 35–40 Shidaganti G, Hulkund RG, Prakash S (2017) Analysis and exploitation of Twitter data using machine learning techniques In International proceedings on advances in soft computing, intelligent systems and applications, pp 135–146 Rout JK et al (2018) A model for sentiment and emotion analysis of unstructured social media text Electr Commerce Res 18(1):181–199 Mumtaz D, Ahuja B (2017) A lexical and machine learning-based hybrid system for sentiment analysis In: Studies in computational intelligence Springer, Singapore (Chap 11, pp 165–175) Al-Smadi M, Talafha B, Al-Ayyoub M, Yaser J (2018) Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews Int J Mach Learn Cybern 1–13 Chen Y (2018) TagNet: toward tag-based sentiment analysis of large social media data In: 2018 IEEE Pacific visualization symposium (PacificVis), Kobe, Japan, pp 190–194 10 Fouad Mohammed M, Gharib Tarek F, Mashat Abdulfattah S (2018) Efficient Twitter sentiment analysis system with feature selection and classifier ensemble In: International conference on advanced machine learning technologies and applications, vol 723, pp 516–527 11 Mary Jones T, DivyaSri S, Bavani G (2017) A study on the implications of Pradhan Manthri Jan Dhan Yojana on the growth of indian economy IRA-Int J Manag Soc Sci 6(3):461–466 12 Khan FH, Bashir S, Qamar U (2014) TOM: Twitter opinion mining framework using hybrid classification scheme Decis Support Syst 57:245–257 13 García-Pablos A, Cuadros M, Rigau G (2018) W2VLDA: almost unsupervised system for aspect based sentiment analysis 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intervoxel-texture operators in neuroimaging using Anaconda and 3D Slicer environments In: 2017 XLIII Latin American Computer Conference (CLEI), Cordoba, Argentina, pp 1–3 20 Dwivedi SK, Rawat B (2016) A review paper on data preprocessing: a critical phase in web usage mining process In: 2015 international conference on green computing and Internet of Things (ICGCIoT), Noida, India, pp 506–510 Author Index A Abdow, Hassan I., 827 Acharya, Arup A., 497 Advaith, U., 319 Agarwal, Devansh, 407 Agarwal, Piyush, 709 Agarwal, Vartika, 709 Agrawal, Prerna, 35 Agrawal, Srishti, 541 Ahmad, Mobin, 717 Akshaya, B., 25 Alam, Md Raiyan, 753 Alen, S., 319 AlOsail, Deemah, 305 Amarendra, K., 969 Amino, Noora, 305 Amrutha, A., 939 Ansari, Akbar, 369 Anto Praveena, MD, 697 Arappali, Nedumaran, 459 Arefin, Md Taslim, 753 Aruna, S., 785 Ashvitha, K P., 25 B Bacanin, Nebojsa, 87 Balaji, I., 939 Bandi, Ajay, 651 Barani Sundaram, B., 459 Baskaran, C., 177 Bezdan, Timea, 87 C Chandrasekar, M., 103 Charniya, Nadir N., 839 Cherian, Mimi, 673 Cherwoo, Sameer, 907 Chettri, Sarat Kr., 115 Chitra, S., 227 Christy, A., 697 D Daniel, Ravuri, 217 Darshini, P., 251 Debnath, Dipankar, 115 Deshmukh, Anand B., 395 Devi, Matta Bharathi, 969 Dhanalakshmi, R., 193, 205 Dhanya, V G., 193 Dongre, Nilima M., 483 Dubey, Nilesh, 635 Dudul, Sanjay V., 395 Durga Bhavani, S., 161 E Elamaran, V., 103 Evan, Nawshad Ahmad, 753 G Gabhane, Jyotsna P., 329 Ganatra, Amit, 43, 635 Gandhi, Rishi Kumar, 557 Girdhar, Akshay, 981 Gowtham, Veldi Pream Sai, 277 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021 A Pasumpon Pandian et al (eds.), Computer Networks, Big Data and IoT, Lecture Notes on Data Engineering and Communications Technologies 66, https://doi.org/10.1007/978-981-16-0965-7 989 990 Gummadi, Jose Moses, 217 Gupta, Lavanya, 541 H Haldorai, Anandakumar, 261 Herbert Raj, P., 287, 735 Hussain, Aquib, 369 I Ilavarsan, E., 851 Indumathi, V., 919 J Jacob, Minu Susan, 193 Jagadeesha, S N., 251 Janghel, Rekh Ram, 507 Jayalakshmi, V., 227 John, Jisha, 319 Johnson, Joveal K., 319 Joies, Kesia Mary, 319 K Kalra, Nidhi, 541 Kanade, Vijay A., 627 Karthika, P., 459 Karunananda, Asoka, 443 Kaur, Maninder, 981 Khanduja, Namit, 369 Khot, Jayanth, 939 Kiruthiga, T., 887 Krishna, Akhila, 507 Krishna Mohan, A., 217, 469 Krishnan, Shoba, 839 Krishna Raghavendra, Adapa V., 277 Kumar, Amit, 369 Kumar, Pankaj, 143 Kumar, T G Keerthan, 953 L Ladvaiya, Nikunj, 585 Lahari, Garapati Khyathi, 277 Laxmi Lydia, E., 217, 469 Lingamgunta, Sumalatha, 217, 469 M Madhusudhana Rao, T V., 469 Maheshwari, Sumit, 939 Malik, Monica, 379 Author Index Mallaiah, Kurra, 557 Manavalan, R., 351 Mandpura, Anup K., 827 Manikandan, C., 103 Maruthi Shankar, B., Mazher Iqbal, J L., Meena, K., 813 Mehta, Jigar, 585 Mekonnen, Melaku Tamene, 459 Mohammad, Nazeeruddin, 305 Mohammed, Moulana, 277 Mohana Kumar, S., 251 Mohankumar, N., 613 Mohanty, Sanjukta, 497 Mohapatra, Sunil K., 497 Moholkar, K P., 15 Muneeswari, G., 873 N Naaz, Sameena, 379 Nafis, Md Tabrez, 717 Naik, S., 939 Naveen, 369 Nayak, Amit, 43, 635 Nukapeyi, Sharmili, 217 P Padma Sree, L., 161 Pakhare, Piyusha Sanjay, 839 Pandya, Vidhi, 585 Parihar, Anil Singh, 771, 907 Parveen, M., 797 Patel, Gaurang, 635 Patel, Rajesh, 43 Pathak, Sunil, 329 Patil, S H., 15 Paul, Rosebell, 519 Pawar, Chandrashekhar S., 43 Pedipina, Seenaiah, 205 Pimple, Kshitij U., 483 Pitchaipandi, P., 177 Polara, Vishal, 131 Poovammal, E., 423 Prabakeran, S., 919 Pradeepa, K., 797 Pradeepkumar, G., Prasad, Krishna, 251 Praveenkumar, B., 939 R Rai, Gaurav, 369 Author Index Rajagopalan, S., 297, 663 Rajakumar, R., 743 Rajalakshmi, D., 813 Rajendiran, M., 25 Raju, N., 103 Ramachandram, S., 557 Ramesh, J., 351 Ramesh, V., 53 Ram, G Mohan, 851 Ramoliya, Dipak, 43 Ramu, Arulmurugan, 261 Rathod, Jagdish M., 131 Ravikumar, Aswathy, 319 Rejimol Robinson, R R., 685 S Sahu, Laki, 497 Sahu, Satya prakash, 507 Sai Prasanna, A., 613 Sai Siva Satwik, K., 103 Sankar, S., 205 Saranya, M D., Sarma, Subramonian Krishna, 71 Sasanka, J., 785 Sathiya Devi, S., 743 Sebastian, Neenu, 519 Sengupta, Aritro, 143 Seraphim, B Ida, 423 Shanmugasundaram, N., 887 Sharma, Sachin, 709 Sharma, Seemu, 541 Sharma, Vivek, 953 Shekokar, Narendra, 407 Sheth, Richa, 407 Silva, Thushari, 443 Singh, Amit, 143 Singh, Bikesh Kumar, 507 Singh, Deepak, 601 Singh, Inderjeet, 981 Singh, Paramvir, 907 Singh, Yogendra Narain, 239 Sinha, Kunal, 907 Sowmya, T., 873 Srikanth, M S., 953 Srinivas, P V V S., 277 Srivastava, Mayank, 643 991 Srivastava, Sonam, 239 Strumberger, Ivana, 87 Suganya, S., 939 Suji Helen, L., 697 Sunil, Rahul, 319 Suresh Kumar, C., 53 T Tamilselvan, K S., Tefera, Wondatir Teka, 459 Tejeswini, J., 613 Thakare, Nita M., 329 Thilagavathi, S., 25 Thomas, Ciza, 685 Tiwari, Nandana, 771 Trivedi, Bhushan, 35 U Uddin, Md Raihan, 753 Uganya, G., 813 Urooj, Aksa, 717 V Vaithyasubramanian, S., 697 Vanamala, Sunitha, 161 Varma, Neha, 541 Venkatachalam, K., 87 Verma, Satishkumar, 673 Verma, Vipasha, 541 Vidanagama, Dushyanthi Udeshika, 443 Vijaya Kumar, K., 469 Vijayaraj, N., 813 Vinay, D A., 785 Vinod, Parvathy, 519 Y Yadav, Mukesh, 601 Yadukrishnan, P S., 519 Z Zivkovic, Miodrag, 87 ... 459 Nedumaran Arappali, Melaku Tamene Mekonnen, Wondatir Teka Tefera, B Barani Sundaram, and P Karthika Challenging Data Models and Data Confidentiality Through “Pay-As-You-Go” Approach Entity... Information Sharing Over Social Media Analysis Using Centrality Measure K P Ashvitha, B Akshaya, S Thilagavathi, and M Rajendiran Abstract Instagram and Twitter are popular social media in India... 177 C Baskaran and P Pitchaipandi Twitter-Based Disaster Management System Using Data Mining 193 V G Dhanya, Minu Susan Jacob, and R Dhanalakshmi Sentimental Analysis on Twitter Data of