Название: Data, Engineering and Applications: Volume 1 Автор: Rajesh Kumar Shukla, Jitendra Agrawal Издательство: Springer ISBN: 9811363463 Год: 2019 Страниц: 189 Язык: английский Формат: pdf (true), epub Размер: 32.6 MB
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.
Part I On Data Mining and Social Networking A Review of Recommender System and Related Dimensions Collaborative Filtering Techniques in Recommendation Systems Predicting Users’ Interest Through ELM-Based Collaborative Filtering Application of Community Detection Technique in Text Mining Sentiment Analysis on WhatsApp Group Chat Using R A Recent Survey on Information-Hiding Techniques Investigation of Feature Selection Techniques on Performance of Automatic Text Categorization 71 Identification and Analysis of Future User Interactions Using Some Link Prediction Methods in Social Networks 83 Sentiment Prediction of Facebook Status Updates of Youngsters 95
Part II On Machine Learning Logistic Regression for the Diagnosis of Cervical Cancer Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm 119 Personality Trait Identification for Written Texts Using MLNB 131 Deep Neural Network Compression via Knowledge Distillation for Embedded Vision Applications 139 An Elective Course Decision Support System Using Decision Tree and Fuzzy Logic 149 An Appearance-Based Gender Classification Using Radon Features 159 An Automatic Text Summarization on Naive Bayes Classifier Using Latent Semantic Analysis 171 Preserving Patient Records with Biometrics Identification in e-Health Systems 181
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