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Nature of Computation and Communication: 7th EAI International Conference, ICTCC 2021, Virtual Event, October 28–29, 2021, Proceedings (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering #408)
by Phan Cong Vinh Nguyen Huu NhanThis book constitutes the refereed post-conference proceedings of the 7th International Conference on Nature of Computation and Communication, ICTCC 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 revised full papers presented were carefully selected from 43 submissions. The papers of ICTCC 2021 cover formal methods for self-adaptive systems and discuss natural approaches and techniques for natural computing systems and their applications.
Nature of Computation and Communication: 8th EAI International Conference, ICTCC 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering #473)
by Cong Vinh Phan Thanh Dung NguyenThis book constitutes the refereed post-conference proceedings of the 8th EAI International Conference on Nature of Computation and Communication, ICTCC 2022, held in Vinh Long, Vietnam, in October 27-28 2022. The 11 revised full papers presented were carefully selected from 32 submissions. The papers of ICTCC 2022 cover formal methods for self-adaptive systems and discuss natural approaches and techniques for natural computing systems and their applications.
Nature of Computation and Communication: 9th EAI International Conference, ICTCC 2023, Ho Chi Minh City, Vietnam, October 26-27, 2023, Proceedings (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering #586)
by Phan Cong Vinh Hafiz Mahfooz Ul HaqueThis book constitutes the refereed post-conference proceedings of the 9th International Conference on Nature of Computation and Communication, ICTCC 2023, held in Ho Chi Minh City, Vietnam, in October 2023. The 12 revised full papers presented were carefully selected from 30 submissions. The papers of ICTCC 2023 cover formal methods for self-adaptive systems and discuss natural approaches and techniques for natural computing systems and their applications.
Nature-Inspired Algorithms and Applications
by Dinesh Goyal S. Balamurugan Anupriya Jain Sachin Sharma Sonia Duggal Seema SharmaThe purpose of designing this book is to portray certain practical applications of nature-inspired computation in machine learning for the better understanding of the world around us. The focus is to portray and present recent developments in the areas where nature- inspired algorithms are specifically designed and applied to solve complex real-world problems in data analytics and pattern recognition, by means of domain-specific solutions. Various nature-inspired algorithms and their multidisciplinary applications (in mechanical engineering, electrical engineering, machine learning, image processing, data mining and wireless network domains are detailed, which will make this book a handy reference guide.
Nature-Inspired Computation in Data Mining and Machine Learning (Studies in Computational Intelligence #855)
by Xin-She Yang Xing-Shi HeThis book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
Nature-Inspired Computation in Engineering
by Xin-She YangThis timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.
Nature-Inspired Computation in Navigation and Routing Problems: Algorithms, Methods and Applications (Springer Tracts in Nature-Inspired Computing)
by Xin-She Yang Yu-Xin ZhaoThis 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.
Nature-Inspired Computing for Control Systems
by Hiram Eredín Ponce EspinosaThe book presents recent advances in nature-inspired computing, giving a special emphasis to control systems applications. It reviews different techniques used for simulating physical, chemical, biological or social phenomena at the purpose of designing robust, predictive and adaptive control strategies. The book is a collection of several contributions, covering either more general approaches in control systems, or methodologies for control tuning and adaptive controllers, as well as exciting applications of nature-inspired techniques in robotics. On one side, the book is expected to motivate readers with a background in conventional control systems to try out these powerful techniques inspired by nature. On the other side, the book provides advanced readers with a deeper understanding of the field and a broad spectrum of different methods and techniques. All in all, the book is an outstanding, practice-oriented reference guide to nature-inspired computing addressing graduate students, researchers and practitioners in the field of control engineering.
Nature-Inspired Computing for Smart Application Design (Springer Tracts in Nature-Inspired Computing)
by Santosh Kumar Das Thinagaran Perumal Thanh-Phong DaoThis book focuses primarily on the nature-inspired approach for designing smart applications. It includes several implementation paradigms such as design and path planning of wireless network, security mechanism and implementation for dynamic as well as static nodes, learning method of cloud computing, data exploration and management, data analysis and optimization, decision taking in conflicting environment, etc. The book fundamentally highlights the recent research advancements in the field of engineering and science.
Nature-Inspired Computing: Physics and Chemistry-Based Algorithms
by Hojjat Adeli Nazmul H. SiddiqueNature-Inspired Computing: Physics and Chemistry-Based Algorithms provides a comprehensive introduction to the methodologies and algorithms in nature-inspired computing, with an emphasis on applications to real-life engineering problems. The research interest for Nature-inspired Computing has grown considerably exploring different phenomena observed in nature and basic principles of physics, chemistry, and biology. The discipline has reached a mature stage and the field has been well-established. This endeavour is another attempt at investigation into various computational schemes inspired from nature, which are presented in this book with the development of a suitable framework and industrial applications. Designed for senior undergraduates, postgraduates, research students, and professionals, the book is written at a comprehensible level for students who have some basic knowledge of calculus and differential equations, and some exposure to optimization theory. Due to the focus on search and optimization, the book is also appropriate for electrical, control, civil, industrial and manufacturing engineering, business, and economics students, as well as those in computer and information sciences. With the mathematical and programming references and applications in each chapter, the book is self-contained, and can also serve as a reference for researchers and scientists in the fields of system science, natural computing, and optimization.
Nature-Inspired Design of Hybrid Intelligent Systems
by Oscar Castillo Patricia Melin Janusz KacprzykThis book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book's third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.
Nature-Inspired Intelligent Computing Techniques in Bioinformatics (Studies in Computational Intelligence #1066)
by Khalid RazaThis book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications. This edited volume covers fundamental applications, scope, and future perspectives of NIC techniques in bioinformatics including genomic profiling, gene expression data classification, DNA computation, systems and network biology, solving personalized therapy complications, antimicrobial resistance in bacterial pathogens, and computer-aided drug design, discovery, and therapeutics. It also covers the role of NIC techniques in various diseases and disorders, including cancer detection and diagnosis, breast cancer, lung disorder detection, disease biomarkers, and potential therapeutics identifications.
Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications (Springer Tracts in Nature-Inspired Computing)
by Serdar Carbas Abdurrahim Toktas Deniz UstunThis book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in application contexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.
Nature-Inspired Methods for Smart Healthcare Systems and Medical Data
by Mohamed Elhoseny Ahmed M. Anter Anuradha D. ThakareThis book aims to gather high-quality research papers on developing theories, frameworks, architectures, and algorithms for solving complex challenges in smart healthcare applications for real industry use. It explores the recent theoretical and practical applications of metaheuristics and optimization in various smart healthcare contexts. The book also discusses the capability of optimization techniques to obtain optimal parameters in ML and DL technologies. It provides an open platform for academics and engineers to share their unique ideas and investigate the potential convergence of existing systems and advanced metaheuristic algorithms. The book's outcome will enable decision-makers and practitioners to select suitable optimization approaches for scheduling patients in crowded environments with minimized human errors.The healthcare system aims to improve the lives of disabled, elderly, sick individuals, and children. IoT-based systems simplify decision-making and task automation, offering an automated foundation. Nature-inspired metaheuristics and mining algorithms are crucial for healthcare applications, reducing costs, increasing efficiency, enabling accurate data analysis, and enhancing patient care. Metaheuristics improve algorithm performance and address challenges in data mining and ML, making them essential in healthcare research. Real-time IoT-based healthcare systems can be modeled using an IoT-based metaheuristic approach to generate optimal solutions.Metaheuristics are powerful technologies for optimization problems in healthcare systems. They balance exact methods, which guarantee optimal solutions but require significant computational resources, with fast but low-quality greedy methods. Metaheuristic algorithms find better solutions while minimizing computational time. The scientific community is increasingly interested in metaheuristics, incorporating techniques from AI, operations research, and soft computing. New metaheuristics offer efficient ways to address optimization problems and tackle unsolved challenges. They can be parameterized to control performance and adjust the trade-off between solution quality and resource utilization. Metaheuristics manage the trade-off between performance and solution quality, making them highly applicable to real-time applications with pragmatic objectives.
Nature-Inspired Networking: Theory and Applications
by Phan Cong-Vinh"Nature-inspired" includes, roughly speaking, "bio-inspired"+"physical-inspired"+"social-inspired"+ and so on. This book contains highly original contributions about how nature is going to shape networking systems of the future. Hence, it focuses on rigorous approaches and cutting-edge solutions, which encompass three classes of major methods: 1) Those that take inspiration from nature for the development of novel problem solving techniques; 2) Those that are based on the use of networks to synthesize natural phenomena; and 3) Those that employ natural materials to compute or communicate.
Nature-Inspired Optimization Algorithms
by Vasuki ANature-Inspired Optimization Algorithms, a comprehensive work on the most popular optimization algorithms based on nature, starts with an overview of optimization going from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of optimization techniques, providing us with simple solutions to complex problems in an effective and adaptive manner. The study of the intelligent survival strategies of animals, birds, and insects in a hostile and ever-changing environment has led to the development of techniques emulating their behavior. This book is a lucid description of fifteen important existing optimization algorithms based on swarm intelligence and superior in performance. It is a valuable resource for engineers, researchers, faculty, and students who are devising optimum solutions to any type of problem ranging from computer science to economics and covering diverse areas that require maximizing output and minimizing resources. This is the crux of all optimization algorithms. Features: Detailed description of the algorithms along with pseudocode and flowchart Easy translation to program code that is also readily available in Mathworks website for some of the algorithms Simple examples demonstrating the optimization strategies are provided to enhance understanding Standard applications and benchmark datasets for testing and validating the algorithms are included This book is a reference for undergraduate and post-graduate students. It will be useful to faculty members teaching optimization. It is also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature-inspired optimization algorithms are unconventional, and this makes them more efficient than their traditional counterparts.
Nature-Inspired Optimization Algorithms with Java: A Look at Optimization Techniques
by Shashank JainGain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java.You'll start with an introduction to the hidden algorithms that nature uses and find the approximate solutions to optimization problems. You'll then see how living creatures such as fish and birds are able to perform computation to solve specific optimization tasks. This book also covers various nature-inspired algorithms by reviewing code examples for each one followed by crisp and clear explanations of the algorithm using Java code. You'll examine the use of each algorithm in specific industry scenarios such as fleet scheduling in supply chain management, and shop floor management in industrial and manufacturing applications. Nature-Inspired Optimization Algorithms with Java is your pathway to understanding a variety of optimization problems that exist in various industries and domains and it will develop an ability to apply nature-inspired algorithms to find approximate solutions to them.What You'll LearnStudy optimization and its problemsExamine nature-inspired algorithms such as Particle Swarm, Gray wolf, etc.See how nature-inspired algorithms are being used to solve optimization problemsUse Java for solving the different nature-inspired algorithms with real-world examplesWho This Book Is For Software developers/architects who are looking to hone their skills in area of problem solving related to optimization with Java.
Nature-Inspired Optimizers: Theories, Literature Reviews and Applications (Studies in Computational Intelligence #811)
by Andrew Lewis Jin Song Dong Seyedali MirjaliliThis book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.
Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems
by Aprna Tripathi Sulabh Bansal Shilpa Srivastava Prem Prakash VuppuluriThis comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities.This book: Discusses load balancing and demand response using nature-inspired optimization techniques Presents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithms Covers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniques Explains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniques Illustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniques Nature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis (SpringerBriefs in Applied Sciences and Technology)
by Patricia Melin Ivette Miramontes German Prado ArechigaThis book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine.
Natürliche und künstliche Intelligenz: Ein kritischer Vergleich
by Gerhard Roth Lukas Tuggener Fabian Christoph RothDieses Sachbuch fasst die wissenschaftlichen Grundlagen der natürlichen und künstlichen Intelligenzsysteme zusammen und analysiert ihre Leistungen in einem kritischen Vergleich. Fachkenntnisse sind keine Voraussetzung. Nach einer Einführung in die Intelligenzforschung folgt die Beschreibung menschlicher und tierischer Intelligenz und deren neurobiologischen Grundlagen. Dieser natürlichen Intelligenz wird im Anschluss die künstliche Intelligenz gegenübergestellt, wobei die wichtigsten Grundprinzipien und die Entwicklung hin zu heutigen KI-Systemen betrachtet werden. Dies beinhaltet auch die wichtige Frage, inwiefern KI-Systeme vom Gehirn und dessen Arbeitsweisen lernen können und ob durch das „Nachbauen“ von Nervenzellenverbünden mit den sogenannten neuromorphen Chips vergleichbare Leistungen erreichbar sind oder sein werden. Ein besonderer Fokus liegt auf der kritischen Betrachtung und Einordnung der Fähigkeiten von KI-Systemen in Hinblick auf Denken und Handeln als eine selbstständige Entscheidungsinstanz. Letzteres wirft Fragen hinsichtlich moralischer Entscheidungen und des möglichen Kontrollverlusts über solche Systeme auf, die zurzeit nicht abschließend beantwortet werden können
Navigating Complexity: AI and Systems Thinking for Smarter Decisions
by Hassan Qudrat-UllahThis book, “Navigating Complexity: AI and Systems Thinking for Smarter Decisions” delves into the integration of Artificial Intelligence (AI) and systems thinking to enhance decision-making in complex and dynamic environments. Aimed at professionals, researchers, and academics in fields such as management, healthcare, sustainability, and public policy, it provides a comprehensive exploration of how these two approaches can be synergistically employed. The main topics include the theoretical foundations of complexity science, the practical application of AI and systems thinking tools, and real-world case studies demonstrating their combined use. These topics are crucial as they address the need for advanced methodologies to navigate and manage the increasing complexity in modern decision-making scenarios. The book seeks to solve the problem of effectively managing complexity by offering innovative frameworks and models that integrate AI’s data-driven capabilities with systems thinking’s holistic approach. This integration is essential for improving decision-making processes across various domains, providing readers with actionable insights and tools to tackle contemporary challenges.
Navigating Cyberspace
by Kim EtingoffThe Internet is a great tool for learning. It's also a lot of fun for games, keeping up with friends, or reading about the things you love. But there are also dangers on the Internet. You can't always know for sure to whom you're talking. Information you put online that you think is safe may become a target for people who are up to no good. Pictures you share with one person can end up in the hands of people you'd never have sent them to. The Internet can become a scary place.
Navigating Digital Transformation: Organizational Change, Digital Work, and Individual Behavior (Lecture Notes in Information Systems and Organisation #73)
by Rocco Agrifoglio Stefano Za Alessandra LazazzaraThis book presents a collection of research papers that explore how managers, practitioners and policymakers can address the challenges of the Digital Transformation with particular reference to the topics of organizational change, digital work, and individual behaviors. Each chapter offers insights into how to tackle DT in order to support work within modern organizations and society as a whole. The plurality of views offered makes this book relevant for scholars, companies, and public sector organizations alike. It gathers revised versions of selected papers (original double-blind peer-reviewed contributions) presented at the annual conference of the Italian chapter of AIS, which took place in Turin, Italy, in October 2023.
Navigating Insurtech: Opportunities and Challenges in Digital Insurance
by Janthana KaenprakhamroyNavigating Insurtech demystifies the insurtech ecosystem, providing insurance professionals with a comprehensive understanding of the industry and its key players, components, challenges and opportunities. The insurtech landscape is highly complex and constantly evolving, making it difficult to fully understand its opportunities and challenges. Yet insurance companies that fail to evolve and grasp advancements in insurtech could risk losing market share and suffer reputational damage. This book offers practical guidance for insurance companies looking to implement insurtech solutions, supported throughout by real-life case studies, insights and interviews from industry leaders and experts. It examines key developments, such as customer experience, risk management, distribution channels and transformative technologies such as blockchain, IoT and AI. It also looks at the investment landscape, offering insights into successful insurtech investments, opportunities and challenges of investing in insurtech startups.To succeed in insurtech, organizations must have a deep understanding of the industry and the technologies involved, as well as the ability to build strong partnerships with other players in the ecosystem. Navigating Insurtech is an essential read for insurance and insurtech professionals, investors and anyone else interested in the developments of insurtech.