Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

Agent technology has generated lots of excitement in the past decade. Currently, multi-agent systems (MAS) composed of autonomous agents representing individuals or organizations and capable of reaching mutually beneficial agreements through negotiation and argumentation are becoming increasingly important and pervasive. Research on both automated negotiation and argumentation in MAS has a vigorous, exciting tradition. However, efforts to integrate both areas have received only selective attention in the academia and the practitioner literature. A symbiotic relationship could significantly strengthen each area’s progress and trigger new R&D challenges and prospects toward the advancement of automated negotiators and argumentation tools. Negotiation and Argumentation in Multi-Agent Systems presents the current state-of-the-art on the theory and practice of automated negotiation and argumentation in MAS. The eBook encourages the interaction between these two areas in data modelling and attempts to converge them toward mutual enhancement and synergism. Equally, the monograph brings together researchers and industry practitioners specialized in these areas to share R&D results and discuss existing and emerging theoretical and applied problems. This book is intended as a textbook for graduate courses and a reference book for researchers, advanced-level students in Computers Science, and IT practitioners.

Intelligent Computational Systems presents current and future developments in intelligent computational systems in a multi-disciplinary context. Readers will learn about the pervasive and ubiquitous roles of artificial intelligence (AI) and gain a perspective about the need for intelligent systems to behave rationally when interacting with humans in complex and realistic domains. This reference covers widespread applications of AI discussed in 11 chapters which cover topics such as AI and behavioral simulations, AI schools, automated negotiation, language analysis and learning, financial prediction, sensor management, Multi-agent systems, and much more. This reference work is will assist researchers, advanced-level students and practitioners in information technology and computer science fields interested in the broad applications of AI.

Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features - Introduces the reader to Python programming and data processing - Introduces the reader to the preliminaries of natural language processing (NLP) - Covers data analysis and visualization using predefined python libraries and datasets - Teaches how to write text mining programs in Python - Includes text classification and clustering techniques - Informs the reader about different types of neural networks for text analysis - Includes advanced analytical techniques such as fuzzy logic and deep learning techniques - Explains concepts in a simplified and structured way that is ideal for learners - Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.

This book informs the reader about applications of Artificial Intelligence (AI) and nature-inspired algorithms in different situations. Each chapter in this book is written by topic experts on AI, nature-inspired algorithms and data science. The basic concepts relevant to these topics are explained, including evolutionary computing (EC), artificial neural networks (ANN), swarm intelligence (SI), and fuzzy systems (FS). Additionally, the book also covers optimization algorithms for data analysis. The contents include algorithms that can be used in systems designed for plant science research, load balancing, environmental analysis and healthcare. The goal of the book is to equip the reader - students and data analysts - with the information needed to apply basic AI algorithms to resolve actual problems encountered in a professional environment.

The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.