Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges [Hardcover]

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including  artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains.- Big Data Analytics and Preprocessing.- Artificial Intelligence-based Plant Diseases Classification.- Artificial Intelligence in Potato Leaf Disease Classification: A Deep Learning Approach.- Granules-Based Rough Set Theory for Circuit Breaker Fault Diagnosis.- SQL Injection Attacks Detection and Prevention based on NeuroFuzzy Technique.- Convolutional Neural Network with Batch Normalization for Classification of Endoscopic Gastrointestinal Diseases.- A Chaotic Search-Enhanced Genetic Algorithm for Bilevel Programming Problems.- Bio-Inspired Machine Learning Mechanism for Detecting Malicious URL through Passive DNS in Big Data Platform.- Target Analytical: A Text Analytics Framework for Ranking Therapeutic Molecules in the Bibliome.- Earthquakes and Thermal Anomalies in a RemoteSensing Perspective.- Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and their Application in Movie Ratings.- Predicting Student Retention Among a Homogeneous Population using Data Mining.- An Approach for Textual Based Clustering Using Word Embedding.- A Survey on Speckle Noise Reduction in SAR Images.-&nbspl3‘