Tsihrintzis G.A., Virvou M., Jain L.C. (Eds.) Advances in Machine Learning/Deep Learning-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis. Volume 2

Springer, 2022. — 237 p. — (Learning and Analytics in Intelligent Systems, 23). — ISBN: 978-3-030-76793-8.As the 4th Industrial Revolution is restructuring human societal organization into, the so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim to incorporate cognitive abilities into machines, such as learning and problem-solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology, and society.The book at hand aims to expose its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction.This research book is directed toward professors, researchers, scientists, engineers, and students in Machine Learning/Deep Learning-related disciplines. It is also directed toward readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.Introduction to Advances in Machine Learning/Deep Learning-Based Technologies.
Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Streams Messages.
AI in (and for) Games.
Computer-Human Mutual Training in a Virtual Laboratory Environment.
Exploiting Semi-supervised Learning in the Education Field: A Critical Survey.
Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security.
AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systems.
A Comparison of Contemporary Methods on Univariate Time Series Forecasting.
Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting.
A Formal and Statistical AI Tool for Complex Human Activity Recognition.
A CU Depth Prediction Model Based on Pre-trained Convolutional Neural Network for HEVC Intra Encoding Complexity Reduction.