Data, Engineering and Applications : Volume 1

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications. Contents......Page 5 About the Editors......Page 7 On Data Mining and Social Networking......Page 9 1.1 Motivation and Problem Explanation......Page 10 2 Literature Review......Page 11 3 Recommender System Model......Page 12 4.1 For Predict on User Ratings......Page 13 5 Dimensions of Recommender System......Page 14 References......Page 16 1 Introduction......Page 18 2.2 Challenges......Page 20 3.1 Content-Based Filtering System......Page 21 3.2 Collaborative Filtering......Page 22 4 Experimental Set-up and Results......Page 23 4.2 Working Process......Page 24 5.1 RMSE......Page 25 5.3 F-Measure......Page 26 References......Page 27 1 Introduction......Page 29 2 Background......Page 30 3.1 Reduction of Dataset......Page 32 3.2 ELM for Rating Prediction......Page 33 4.2 Evaluation Metrics......Page 34 4.3 Empirical Results......Page 36 4.4 Selection of Parameter β (Beta)......Page 37 References......Page 38 1 Introduction......Page 40 2.1 System Overview......Page 41 2.2 Methodology......Page 42 3.1 Precision......Page 46 3.3 F-measures......Page 47 3.5 Memory Usage......Page 48 4.1 Conclusion......Page 49 References......Page 50 1 Introduction......Page 52 2 Literature Review......Page 53 3 Implementation of Sentiment Analysis Using R Studio......Page 54 4 Result Analysis......Page 58 References......Page 59 1 Introduction......Page 61 1.1 Information Hiding......Page 62 2.1 Survey on Reversible Data-Hiding Technique......Page 66 3 Comparison and Discussion......Page 71 References......Page 73 1 Introduction......Page 75 3.2 Methodology......Page 76 4 Experimental Results and Discussions......Page 79 5 Conclusion......Page 84 References......Page 85 1 Introduction......Page 87 3.1 Overview......Page 88 3.4 Positive Edges Sampling......Page 90 3.7 Proximity Feature......Page 91 3.10 Edges Classification......Page 92 4.3 Adamic-Adar Index......Page 93 6 KNN......Page 94 6.2 Non-linear SVM......Page 95 7 Experimental Results and Analysis......Page 96 References......Page 97 1 Introduction......Page 99 2 Literature Review......Page 101 3.2 Data Preprocessing......Page 102 3.4 Classification Using Machine Learning Algorithms......Page 104 4 Results and Discussion......Page 105 References......Page 108 On Machine Learning......Page 110 1 Introduction......Page 111 2.1 Previously Proposed Model......Page 114 2.3 Experiments......Page 115 3.1 Privacy Factor......Page 117 3.4 Presence of Outliers......Page 118 References......Page 119 1 Introduction......Page 120 2 Background and Review of Related Works......Page 121 2.1 Literature Review......Page 122 3 The Proposed Methodology......Page 123 3.1 Mapping of University Examination Timetable to PSO......Page 124 4 Implementation and Results Obtained......Page 126 4.1 Experimental Results......Page 127 4.2 Results......Page 129 References......Page 130 1 Introduction......Page 132 2 Literature Survey......Page 133 2.1 The Big Five Model......Page 134 3.1 Multi-label Naïve Bayes (MLNB) Algorithm......Page 135 4.2 Comparative Analysis of Proposed Work with Existing Work......Page 136 References......Page 137 1 Introduction......Page 139 2.1 Network Pruning......Page 140 2.3 Parameter Sharing......Page 141 3 Knowledge Distillation......Page 142 4 Implementation Choices......Page 143 5 Experimental Set-up and Evaluation......Page 144 6 Conclusion......Page 146 References......Page 147 1 Introduction......Page 149 2 Related Work......Page 150 3.1 C4.5 Classification......Page 151 3.2 Fuzzy Logic......Page 152 3.3 Research Methodology......Page 153 4 Results and Discussion......Page 155 5 Conclusion......Page 156 References......Page 157 1 Introduction......Page 158 2 Review of Literature......Page 159 3 Proposed Work......Page 160 3.1 Preprocessing......Page 161 3.2 Feature Extraction Techniques......Page 162 4 Experimental Analysis......Page 163 4.2 Classification Rate for Partially Occluded Face Images......Page 165 5 Conclusion......Page 166 References......Page 167 1 Introduction......Page 169 3 The Proposed Method......Page 170 4 Evaluation Result......Page 175 5 Conclusion and Future Work......Page 177 References......Page 178 1 Introduction......Page 179 2 Literature Review......Page 181 3 System Modeling and Design......Page 182 4 Discussions and Recommendations......Page 187 References......Page 188