This book discusses all the major nature-inspired algorithms with a focus on their application in the context of solving navigation and routing problems. It also reviews the approximation methods and recent nature-inspired approaches for practical navigation, and compares these methods with traditional algorithms to validate the approach for the case studies discussed. Further, it examines the design of alternative solutions using nature-inspired techniques, and explores the challenges of navigation and routing problems and nature-inspired metaheuristic approaches.

As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

This book gathers selected high-quality research papers presented at the Sixth International Congress on Information and Communication Technology, held at Brunel University, London, on February 25–26, 2021. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The book is presented in four volumes.

This book gravitates on the prominent theories and recent developments of swarm intelligence methods, and their application in both synthetic and real-world optimization problems. The special interest will be placed in those algorithmic variants where biological processes observed in nature have underpinned the core operators underlying their search mechanisms. In other words, the book centers its attention on swarm intelligence and nature-inspired methods for efficient optimization and problem solving. The content of this book unleashes a great opportunity for researchers, lecturers and practitioners interested in swarm intelligence, optimization problems and artificial intelligence.

Decision-Making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with autonomous vehicles and decision algorithms. - Provides a complete overview of decision-making and control techniques for autonomous vehicles - Includes technical, physical, and mathematical explanations to provide knowledge for implementation of tools - Features machine learning to improve performance of decision-making algorithms - Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios

The two-volume set LNAI 12468 and 12469 constitutes the proceedings of the 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, held in Mexico City, Mexico, in October 2020. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 186 submissions. The contributions are organized in topical as follows: Part I: machine and deep learning, evolutionary and metaheuristic algorithms, and soft computing. Part II: natural language processing, image processing and pattern recognition, and intelligent applications and robotics.

This book constitutes revised selected and extended papers presented at track 4 of the Conference on Computer Science and Intelligence Systems, FedCSIS 2020, which took place in Sofia, Bulgaria, during September 6–9, 2020. The FedCSIS Information Systems and Technologies Track included AIST 2020, DSH 2020, ISM 2020, and KAM 2020. For this track, a total of 29 submissions was received from which a total of 5 full and 3 short papers was accepted for publication in this volume. The papers were organized in topical sections named: improving project management methods; numerical methods of solving management problems; and technological infrastructure for business excellence.

We live in a digital world, where we use digital tools and smart devices to communicate over the Internet. In turn, an enormous amount of data gets generated. The traditional computing architectures are inefficient in storing and managing this massive amount of data. Unfortunately, the data cannot be ignored as it helps businesses to make better decisions, solve problems, understand performance, improve processes, and understand customers. Therefore, we need modern systems capable of handling and managing data efficiently. In the past few decades, many distributed computing paradigms have emerged, and we have noticed a substantial growth in the applications based on such emerging paradigms. Some well-known emerging computing paradigms include cloud computing, fog computing, and edge computing, which have leveraged the increase in the volume of data being generated every second. However, the distributed computing paradigms face critical challenges, including network management and cyber security. We have witnessed the development of various networking models—IoT, SDN, and ICN—to support modern systems requirements. However, they are undergoing rapid changes and need special attention. The main issue faced by these paradigms is that traditional solutions cannot be directly applied to address the challenges. Therefore, there is a significant need to develop improved network management and cyber security solutions. To this end, this book highlights the challenges faced by emerging paradigms and presents the recent developments made to address the challenges. More specifically, it presents a detailed study on security issues in distributed computing environments and their possible solutions, followed by applications of medical IoT, deep learning, IoV, healthcare, etc.

This 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.* The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: Part I: classification; clustering - unsupervised learning -analytics; image processing; learning algorithms; neural network modeling; object tracking - object detection systems; ontologies - AI; and sentiment analysis - recommender systems. Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language. *The conference was held virtually due to the COVID-19 pandemic.

This book introduces the reader to the hottest topics in current control sciences and robotics, as seen by scientists from Poland and other European countries. Volume 2 comprises 42 chapters, which specifically address topics connected to statistical and stochastic methods in control engineering applications, to optimization and quantum computing, to biological, medical, and ecological systems, to new applications of artificial intelligence and machine learning in automated and connected vehicles, to design and control of autonomous marine, robotics, and vehicles systems, and to other modern topics. The contributions were presented during XXI Polish Control Conference, held in Gliwice, Poland, from June 26 to 29, 2023. This book is extremely useful to all persons who want to know the latest trends in automation and robotics.