Preface xv List of Contributors xxi List of Figures xxv List of Tables xxix List of Abbreviations xxxi 1 Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm 1 1.1 Introduction 2 1.2 Problem Description 3 1.2.1 Greedy Approach: Bicluster Size Ma...

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Weitere Verfasser:	Pani, Subhendu Kumar 1980- (HerausgeberIn), Tripathy, Somanath (HerausgeberIn), Jandieri, George (HerausgeberIn), Kundu, Sumit (HerausgeberIn), Butt, Talal Ashraf (HerausgeberIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Gistrup River Publishers 2021
Schriftenreihe:	River Publishers Series in Information Science and Technology
Schlagworte:	Big data Machine learning Cloud computing COMPUTERS / Database Management / Data Mining SCIENCE / Energy
Online-Zugang:	UPA01 Volltext
Zusammenfassung:	Preface xv List of Contributors xxi List of Figures xxv List of Tables xxix List of Abbreviations xxxi 1 Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm 1 1.1 Introduction 2 1.2 Problem Description 3 1.2.1 Greedy Approach: Bicluster Size Maximization Based Fitness Function 4 1.2.2 Data Description 5 1.3 Proposed Work: COVID 19 Pattern Identification Using Greedy Biclustering 7 1.4 Results and Discussions 8 1.5 Conclusion 18 1.6 Acknowledgements 18 References 18 2 Artificial Fish Swarm Optimization Algorithm with Hill Climbing Based Clustering Technique for Throughput Maximization in Wireless Multimedia Sensor Network 23 2.1 Introduction 24 2.2 The Proposed AFSA-HC Technique 27 2.2.1 AFSA-HC Based Clustering Phase 28 2.2.2 Deflate-Based Data Aggregation Phase 33 2.2.3 Hybrid Data Transmission Phase 34 2.3 Performance Validation 34 2.4 Conclusion 40 References 40 3 Analysis of Machine Learning Techniques for Spam Detection 43 3.1 Introduction 44 3.1.1 Ham Messages 44 3.1.2 Spam Messages 44 3.2 Types of Spam Attack 45 3.2.1 Email Phishing 45 3.2.2 Spear Phishing 45 3.2.3 Whaling 46 3.3 Spammer Methods 46 3.4 Some Prevention Methods From User End 46 3.4.1 Protect Email Addresses 46 3.4.2 Preventing Spam from Being Sent 47 3.4.3 Block Spam to be Delivered 48 3.4.4 Identify and Separate Spam After Delivery 48 3.4.4.1 Targeted Link Analysis 48 3.4.4.2 Bayesian Filters 48 3.4.5 Report Spam 48 3.5 Machine Learning Algorithms 48 3.5.1 Na̐ve Bayes (NB) 48 3.5.2 Random Forests (RF) 49 3.5.3 Support Vector Machine (SVM) 49 3.5.4 Logistic Regression (LR) 50 3.6 Methodology 51 3.6.1 Database Used 51 3.6.2 Work Flow 51 3.7 Results and Analysis 52 3.7.1 Performance Metric 52 3.7.2 Experimental Results 52 3.7.2.1 Cleaning Data by Removing Punctuations, White Spaces, and Stop Words 54 3.7.2.2 Stemming the Messages 55 3.7.2.3 Analyzing the Common Words from the Spam and Ham Messages 55 3.7.3 Analyses of Machine Learning Algorithms 55 3.7.3.1 Accuracy Score Before Stemming 55 3.7.3.2 Accuracy Score After Stemming 55 3.7.3.3 Splitting Dataset into Train and Test Data 56 3.7.3.4 Mapping Confusion Matrix 58 3.7.3.5 Accuracy 58 3.8 Conclusion and Future Work 59 References 59 4 Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques 63 4.1 Introduction 64 4.2 Literature Survey 65 4.3 Proposed Method 67 4.4 Data Collection in IoT 67 4.4.1 Fetching Data from Sensors 68 4.4.2 K-Nearest Neighbor Classifier 69 4.4.3 Random Forest Classifier 70 4.4.4 Decision Tree Classifier 70 4.4.5 Extreme Gradient Boost Classifier 71 4.5 Results and Discussions 72 4.6 Conclusion 78 4.7 Acknowledgements 78 References 78 5 Assimilate Machine Learning Algorithms in Big Data Analytics: Review 81 5.1 Introduction 82 5.2 Literature
Beschreibung:	1 Online-Ressource (xxxii, 313 Seiten)
ISBN:	9781003337218
DOI:	10.1201/9781003337218