Machine Learning Paradigms

Chapter 1 Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications

Chapter 2 Vision to Language: Methods, Metrics and Datasets

Chapter 3 Deep Learning Techniques for Geospatial Data Analysis

Chapter 4 Deep Learning Approaches in Food Recognition

Chapter 5 Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding

Chapter 6 A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks

Chapter 7 Survey on Deep Learning Techniques for Medical Imaging Application Area

Chapter 8 Deep Learning Methods in Electroencephalography

Chapter 9 The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models–A MIMO Centrifugal Chiller Case Study

Chapter 10 A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System

Chapter 11 Stock Market Forecasting by Using Support Vector Machines

Chapter 12 An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction

Chapter 13 Deep Dense Neural Network for Early Prediction of Failure-Prone Students

Chapter 14 Non-parametric Performance Measurement with Artificial Neural Networks

Chapter 15 A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies

Chapter 16 Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms