Textbook in PDF format

Explores advanced integration in Intelligent Systems Modeling, merging AI, math, and stats for simulations.
This book continues the previous edition: Samsul Ariffin Abdul Karim (2022). Intelligent Systems Modeling and Simulation II: Machine Learning, Neural Networks, Efficient Numerical Algorithm and Statistical Methods, Studies in Systems, Decision and Control. After two years, Intelligent Systems Modeling and Simulation have evolved tremendously through the latest and advanced emergence technologies and many highly sophisticated algorithms have been developed by blending Artificial Intelligence (AI) and mathematics, statistics, data modelling and other related research areas. These blends offer many opportunities and further investigations into the overlapand equality between these areas.
The main scope of the book is to develop a new system of modelling and simulations based on Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Modeling and Simulation, Cyber Security and Awareness, Intelligent Statistical Methods, Big Data Analytics, Sentiment Analytics, Intelligent Function Approximation, and Image Processing in medical imaging. This book is highly suitable for postgraduate students, researchers as well as scientists that have an interest in intelligent numerical modeling and simulations.
Machine Learning facilitates adaptive learning in intelligent systems. By continually learning from operational data, Machine Learning models can adjust to new conditions and improve their performance over time. This adaptability is particularly beneficial for quality control in manufacturing, where intelligent systems can detect defects and anomalies with high precision, ensuring that only products meeting the highest standards are delivered. Machine Learning is typically divided into three categories. Supervised learning involves using a dataset that includes both predictors and known outcomes, known as ‘labels’. The model is trained with this labelled dataset allowing it to make predictions on new, unseen data. Typical tasks in supervised learning include classification, where the goal is to predict a discrete category, and regression, where the goal is to predict a continuous value. Popular supervised learning algorithms include k-Nearest Neighbors, Support Vector Machines (SVM), Logistic Regression, Linear Regression, and Neural Networks.
This book is highly suitable for postgraduate students and researchers to get the state-of-the-art current research directions as well as for the scientists that have an interest and working in intelligent numerical modelling and simulations through AI, Machine Learning, Neural Networks, and its related counterparts