Studies in Computational Intelligence

This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.

Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.

We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics. 

This book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. In this book, new horizons on the theoretical developments of fuzzy logic, neural networks and optimization algorithms are envisioned. In addition, the abovementioned methods are discussed in application areas such as control and robotics, pattern recognition, medical diagnosis, decision-making, prediction and optimization of complex problems. There are a group of papers with the main theme of type-1, type-2 and type-3 fuzzy systems, which basically consists of papers that propose new concepts and algorithms based on type-1, type-2 and type-3 fuzzy logic and their applications. There is also a group of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of hybrid intelligent systems in real problems. There are also a group papers that present theory and practice of neural networks in different applications. Finally, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas.

The book provides a timely coverage of the paradigm of knowledge distillation—an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher–student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.

This book is dedicated to all those interested in the application of computational intelligence techniques for decision-making in uncertain environments. The book is organized into four parts. The first part groups together four works related to conversational systems and decision-making using generative artificial intelligence. The second part includes four articles associated with decision-making in project-oriented environments. The third part includes three works related to decision-making in human health environments and decision-making in sports training. The fourth part of the book contains three articles associated with business decision-making.

This book combines different artificial intelligence techniques for solving decision-making problems, among which the following stand out: generative artificial intelligence, linguistic data summarization techniques, neutrosophic theory, computing with words, among other techniques. The techniques proposed in the book aim to simulate human tolerance in decision-making processes in environments with uncertainty and imprecision.

The authors of the book stand out for their extensive experience in the development of basic and applied applications of computational intelligence. The authors Pedro Y. Piñero Pérez, Iliana Pérez Pupo, Janusz Kacprzyk, and Rafael E. Bello Pérez have published several books associated with artificial intelligence and applied computational intelligence. They continue to work on fundamental and applied research on different artificial intelligence techniques to assist decision-making in different areas of knowledge.

The authors thank all the engineers, professors, and researchers without whose efforts this book could not have been written.

This book provides a comprehensive exploration of the transformative impact of AI technologies across diverse fields. From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs). With a focus on both opportunities and challenges, the book examines the architectural challenges of transformer-based models, the ethical implications of AI, and the importance of language-specific adaptations, particularly for low-resource languages like Arabic. It also highlights the role of AI in code development, multimodal applications, and its integration with intellectual property frameworks. This book is an essential resource for researchers, practitioners, and policymakers seeking to understand and harness the potential of AI to drive innovation and global progress.

This book includes the scientific outcome of the 17th Annual International Symposium on Intelligent Distributed Systems (IDC2024) which was hosted by the University of Brighton, United Kingdom, 18-19 September 2024.
This book includes all sixteen full papers that were accepted for presentation after a rigorous peer review process by experts in their respective fields. The papers are presented here grouped in five areas of focus, namely distributed systems, cyber security, predictive modelling, blockchain applications, and large language models. They provide a wide range of areas of application that demonstrate the dynamics and constantly evolving nature of distributed systems research.
The authors work in two continents, six countries, and a total of fourteen different institutions and organizations.
The book spans different levels of progress of research, completed projects at institutional level, outcomes of PhD. student work, and results that relate to large transnational projects funded by the European Union and other national and international funders. 
The authors hope that the readers of this book of proceedings will find the contents informative and an opportunity to engage with new outcomes of innovative research.
The editors are:
Nikolaos Polatidis, Elias Pimenidis, Marcelo Trovati, Mehmet Aydin.

This book focuses on collecting some of the best results with the goal, dealing with the important and efficient combination of mathematics and computational intelligence, with a stress of fuzzy systems, and fuzzy logic.

Artificial intelligence appears nowadays very much in the media, and also in the practice. In addition, every day, thousands of automatic intelligent systems for decision making are designed and applied around the world. In their design, many advanced techniques based on mathematics and computational intelligence are considered and used. Therefore, it is fundamental to continuously work on novel approaches in these areas and research models and algorithms with foundation in the mutual relationship of these two fields. 

Naturally, an important part of this issue focuses on the applications of these both areas, taking a special interest in the development and application of mathematics, artificial intelligence, and automatic reasoning tools applicable to Digital Forensics, also following the main objective of the COST Action DigForASP-CA17124.

This book applies the powerful asymptotic limit theorems of information and control theories to understanding the dynamics of dysfunction in cognitive cultural artifacts encompassing individual minds, small social groupings, institutions, machine systems, and their many critical composites. A particular focus is on attempts to build ‘intelligent machines’ that would supposedly rival or surpass human minds. All such efforts are blindsided by the reality that all such machines are cultural artifacts of those who build them, closely reflecting cultural priorities and blindness, and that intelligent entities lacking the feedback of high-speed embodiment must endure exaggerated levels of failure by fabulation and hallucination. A principal feature is the detailed working-out of many probability models that can serve as the foundation of statistical tools for the analysis of real-time, real-world data on cognitive failure across a broad range of modes, scales, and levels of organization.

This book introduces a novel integration of Federated Learning with the vision of Healthcare 5.0 to enable secure, adaptive, and intelligent health systems. It presents cutting-edge frameworks that support decentralized model training across medical institutions while preserving patient privacy and ensuring compliance with data regulations.

Focusing on real-world use cases, such as predictive diagnostics, edge-based patient monitoring, personalized medicine, and surgical robotics, it bridges theoretical advances with practical implementations. This book provides deep insights into the design of scalable, privacy-preserving artificial intelligence infrastructures suited for cross-institutional collaboration.