Books in the Studies in Computational Intelligence series

This Springer book provides a perfect platform to submit chapters that discuss the prospective developments and innovative ideas in artificial intelligence and machine learning techniques in the diagnosis of COVID-19.COVID-19 is a huge challenge to humanity and the medical sciences. So far as of today, we have been unable to find a medical solution (Vaccine). However, globally, we are still managing the use of technology for our work, communications, analytics, and predictions with the use of advancement in data science, communication technologies (5G & Internet), and AI. Therefore, we might be able to continue and live safely with the use of research in advancements in data science, AI, machine learning, mobile apps, etc., until we can find a medical solution such as a vaccine.We have selected eleven chapters after the vigorous review process. Each chapter has demonstrated the research contributions and research novelty. Each group of authors must fulfill strict requirements.

This book takes a forward-looking approach by bringing in research and contributions that facilitate in mapping the impact of AI and big data on businesses, the nature of work along with providing practical solutions for preparing the work, workplace, and the workforce of the future.Organizations globally have been experiencing immense transformation due to the reinvention and redefining of the business models due to the dynamic nature of the business environment. Looking at an organizational context, undeniably, the definition of 'work' and 'organizations' is genuinely changing. Artificial intelligence, big data, automation, and robotics are a few of those keywords that are seemingly entering the workplace and reshaping the way work is being done. Moreover, the transition that is being addressed herein not only focuses upon aspects that are operative within an organization like the organizational culture, team building, networking, recruitments, and so on but also aims to address the external aspects like supply chain management, value chain analysis, investment management, etc. Broadly, every single step that is now taken is intensely experiencing this impact upon its functioning. This book serves as a guide not just to the academia but also to the industry to adopt suitable strategies that offer insights into global best practices as well as the innovations in the domain.

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 devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "e;n-D glasses,"e; where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

This book presents novel approaches to the formal specification of concurrent and parallel systems, mathematical models for describing such systems, and programming and verification concepts for their implementation. A special emphasis is on methods based on artificial intelligence and machine learning techniques.Chapters are revised selected papers from the 29th International Workshop on Concurrency, Specification, and Programming (CS&P 2021), Berlin, Germany. Nine independent chapters cover formal approaches to topics such as requirements formalization, parsing, or granular computing, as well as their applications in recommender systems, decision making, security, optimization, and other areas. The book thus addresses both researchers and practitioners in its field.

The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions.

This book presents an authoritative collection of contributions reporting on computational intelligence, fuzzy systems as well as artificial intelligence techniques for modeling, optimization, control and decision-making together with applications and case studies in engineering, management and economic sciences. Dedicated to the Academician of the Polish Academy of Sciences, Professor Janusz Kacprzyk in recognition of his pioneering work, the book reports on theories, methods and new challenges in artificial intelligence, thus offering not only a timely reference guide but also a source of new ideas and inspirations for graduate students and researchers alike.The book consists of the 18 chapters, presented by distinguished and experienced authors from 16 different countries (Australia, Brazil, Canada, Chile, Germany, Hungary, Israel, Italy, China, R.N.Macedonia, Saudi Arabia, Spain, Turkey, United States, Ukraine, and Vietnam). All chapters are grouped into three parts: Computational Intelligence and Fuzzy Systems, Artificial Intelligence Techniques in Modelling and Optimization, and Computational Intelligence in Control and Decision Support Processes.The book reflects recent developments and new directions in artificial intelligence, including computation method of the interval hull to solutions of interval and fuzzy interval linear systems, fuzzy-Petri-networks in supervisory control of Markov processes in robotic systems, fuzzy approaches for linguistic data summaries, first-approximation analysis for choosing fuzzy or neural systems and type-1 or type-2 fuzzy sets, matrix resolving functions in game dynamic problems, evolving stacking neuro-fuzzy probabilistic networks and their combined learning in online pattern recognition tasks, structural optimization of fuzzy control and decision-making systems, neural and granular fuzzy adaptive modeling, state and action abstraction for search and reinforcement learning algorithms. Among the most successful and perspective implementations in practical areas of human activity are tentative algorithms for neurological disorders, human-centric question-answering system, OWA operators in pensions, evaluation of the perception of public safety through fuzzy and multi-criteria approach, a multicriteria hierarchical approach to investment location choice, intelligent traffic signal control and generative adversarial networks in cybersecurity.

This book includes the latest research in the diverse field of intelligent distributed computing, covering a multitude of aspects in both distributed computing and intelligent systems. It includes contributions in machine learning, distributed systems & agents, text- and research-centric applications, social systems, and smart cities. It was written by leading experts in the field, who presented their work as part of the 15th International Symposium on Intelligent Distributed Computing (IDC 2022).

This book looks at cyber security challenges with topical advancements in computational intelligence and communication technologies. This book includes invited peer-reviewed chapters on the emerging intelligent computing and communication technology research advancements, experimental outcomes, and cyber security practices, threats, and attacks with challenges. The book begins with a state-of-the-art survey and reviews of cyber security trends and issues. It further covers areas such as developments in intelligent computing and communication, smart healthcare, agriculture, transportation, online education, and many more real-life applications using IoT, big data, cloud computing, artificial intelligence, data science, and machine learning. This book is of interest to graduate/postgraduate students, researchers, and academicians. This book will be a valuable resource for practitioners and professionals working in smart city visualization through secure and intelligent application design, development, deployment to foster digital revolution, and reliable integration of advanced computing and communication technologies with global significance.

This book assesses the place of logic, mathematics, and computer science in present day, interdisciplinary areas of computational linguistics. Computational linguistics studies natural language in its various manifestations from a computational point of view, both on the theoretical level (modeling grammar modules dealing with natural language form and meaning and the relation between these two) and on the practical level (developing applications for language and speech technology). It is a collection of chapters presenting new and future research. The book focuses mainly on logical approaches to computational processing of natural language and on the applicability of methods and techniques from the study of formal languages, programming, and other specification languages. It presents work from other approaches to linguistics, as well, especially because they inspire new work and approaches.

This book gathers extended versions of papers presented at DoSIER 2021 (the 2021 Third Doctoral Symposium on Intelligence Enabled Research, held at Cooch Behar Government Engineering College, West Bengal, India, during November 12-13, 2021). The papers address the rapidly expanding research area of computational intelligence, which, no longer limited to specific computational fields, has since made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design, to name but a few. Presenting chapters written by experts active in these areas, the book offers a valuable reference guide for researchers and industrial practitioners alike and inspires future studies.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics.The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO's recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.

The book presents the state of the art of the Internet of Things (IoT), applied to Human-Centered Design (HCD) projects addressed to ageing users, from the perspective of health, care and well-being. The current focus on the ageing population is opening up new opportunities for the development of niche solutions aimed at the niche category of older users who are beginning to experience physical and cognitive decline but are still independent and need to maintain their autonomy for as long as possible. The combination between the needs expressed by older users and the opportunities offered by the recent innovative technologies related to the Internet of Things allows research institutions, stakeholders, and academia to target and design new solutions for older users, safeguarding their well-being, health, and care, improving their quality of life. This book discusses and analyses the most recent services, products, systems and environments specifically conceived for older users, in order to enhance health, care, well-being and improve their quality of life. This approach is coherent with the percept of AAL or enhanced living environment, looking to the users' comfort, autonomy, engagement and healthcare. The book describes and analyses aspects of HCD with older users looking to the emerging technologies, products, services, and environments analysed in their actual application in different areas, always concerning the design for the elderly related to the IoT, just as the development of biomonitoring devices, tools for activity recognition and simulation, creation of smart living environments, solutions for their autonomy, assistance and engagement enhancing health, care and wellbeing. The book is intended for researchers, designers, engineers, and practitioners in healthcare to connect academia, stakeholders, and research institutions to foster education, research and innovation.

This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today's technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle's speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality?Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

This book comprises carefully selected and reviewed outcomes of the 13th International Workshop on Automated Negotiations (ACAN) held in Vienna, 2022, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI) 2022. It focuses on the applications and challenges of agent-based negotiation including agreement technology, mechanism design, electronic commerce, recommender systems, supply chain management, social choice theory, and others.This book is intended for the academic and industrial researchers of various communities of autonomous agents and multi-agent systems, as well as graduate students studying in those areas or having interest in them.