Debabrata Samanta – författare

The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few.��

Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications�explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.�

Provides insights into the theory, algorithms, implementation, and application of computational intelligence techniques Covers a wide range of applications of deep learning across various domains which are researching the applications of computational intelligence Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques

Blockchain Technology for Emerging Applications: A Comprehensive Approach explores recent theories and applications of the execution of blockchain technology. Chapters look at a wide range of application areas, including healthcare, digital physical frameworks, web of-things, smart transportation frameworks, interruption identification frameworks, ballot-casting, architecture, smart urban communities, and digital rights administration. The book addresses the engineering, plan objectives, difficulties, constraints, and potential answers for blockchain-based frameworks.

It also looks at blockchain-based design perspectives of these intelligent architectures for evaluating and interpreting real-world trends. Chapters expand on different models which have shown considerable success in dealing with an extensive range of applications, including their ability to extract complex hidden features and learn efficient representation in unsupervised environments for blockchain security pattern analysis.

Introduces the basic architecture and taxonomy of blockchain technology Surveys the most recent developments and challenges in blockchain-enabled technology for various application domains with fundamental and technical depth Investigates how to devise secure and reliable applications and blockchain-enabled decentralized secure solutions using blockchain technology

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. Investigates various methodologies and algorithms for data summarization, clustering and classification Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets

With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

Impact of Climate Change on Social and Mental Well-Being, increases the awareness and understanding of how global climate change, including natural disasters, loss of habitat, and displacement can affect social and mental well-being. This book provides valuable insights into the connection between environmental issues and mental health, and the long-term implications of these issues. Sections dedicated to adaptation and solutions, offer coping mechanisms and strategies for eco-anxiety and climate grief. This book also explores the social, economic, and political factors that contribute to climate change and how these factors impact human well-being.Reviews the social impacts of climate change including forced migration, and social equity. Outlines the links between climate change and suicidal ideationIdentifies coping strategies for climate-related stressors

IoT Security: Fundamentals and Key Enabling Technologies explores the complex interactions between Internet of Things (IoT) and the pressing need for effective cyber security solutions. Diving into real-world case studies to provide insights into implementing efficient security measures that safeguard against online dangers, this book comprehensively analyzes the challenges and possibilities presented by intelligent technologies fueling transformational change, emphasizing the crucial role cybersecurity plays in defending networks, data, and user privacy in an increasingly interconnected digital ecosystem.Coverage includes cryptographic methods, communication networks, device identity and access, data governance and privacy, as well as regulatory frameworks and standards for IoT security. Computer science researchers and engineers will benefit from this compilation of cutting-edge research and practical case studies, learning how to minimize risks for IoT and intelligent technologies.Covers emerging application areas in IoT and possible applications of cybersecurity solutionsPresents cutting-edge blockchain-enabled applications for smart cities, cyber-physical systems, cloud computing, intelligent transportation systems, and network securityOffers both theoretical insights and applications of blockchain, edge computing, machine learning/AI, penetration testing, and collaborative security approaches

The book provides a comprehensive discussion for utilizing computational models such as artificial neural networks, agent-based models, and decision field theory, for reliability engineering. It further presents optimization solutions for smart and secure healthcare services. The text showcases how to predict the failure and repair rates of healthcare subsystems using computational intelligence.This book:Explores how data-driven methodologies and advanced computational intelligence are revolutionizing the healthcare industry, promoting efficiency, accessibility, and sustainabilityHighlights the pivotal role that big data analytics plays in harnessing vast amounts of patient records, clinical information, and real-time medical data to provide timely insights for healthcare professionals and policymakersDiscusses the integration of artificial intelligence and machine learning techniques in healthcare, with a focus on revolutionizing disease detection, treatment planning, and resource allocationLays the foundation for developing sustainable healthcare systems that are adaptable to long-term challenges, such as population growth, emerging diseases, and resource constraintsCovers computational intelligence techniques, like fuzzy logic, neural networks, and evolutionary computations, emphasizing their role in solving complex, data-driven healthcare problemsIncludes topics like data management, visualization, protection, and complex adaptive systems, as well as hybrid computational intelligence techniques for synergistic problem-solving strategiesThis volume will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer engineering, and mathematics.

The book provides a comprehensive discussion for utilizing computational models such as artificial neural networks, agent-based models, and decision field theory, for reliability engineering. It further presents optimization solutions for smart and secure healthcare services. The text showcases how to predict the failure and repair rates of healthcare subsystems using computational intelligence.This book:Explores how data-driven methodologies and advanced computational intelligence are revolutionizing the healthcare industry, promoting efficiency, accessibility, and sustainabilityHighlights the pivotal role that big data analytics plays in harnessing vast amounts of patient records, clinical information, and real-time medical data to provide timely insights for healthcare professionals and policymakersDiscusses the integration of artificial intelligence and machine learning techniques in healthcare, with a focus on revolutionizing disease detection, treatment planning, and resource allocationLays the foundation for developing sustainable healthcare systems that are adaptable to long-term challenges, such as population growth, emerging diseases, and resource constraintsCovers computational intelligence techniques, like fuzzy logic, neural networks, and evolutionary computations, emphasizing their role in solving complex, data-driven healthcare problemsIncludes topics like data management, visualization, protection, and complex adaptive systems, as well as hybrid computational intelligence techniques for synergistic problem-solving strategiesThis volume will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer engineering, and mathematics.

The Internet of Things (IoT) has emerged as a fundamental component of contemporary digital ecosystems, facilitating extensive connectivity across sectors like healthcare, smart cities, transportation, industrial automation, and critical infrastructure. While this widespread interconnectivity has brought substantial advantages, it has also increased the risk of cyberattacks, which leaves IoT systems vulnerable to a wide range of complex security threats. The diversity of devices, reliance on open communication channels, and limited resources exacerbate these vulnerabilities, making traditional rule-based security approaches inadequate for addressing modern challenges.AI and ML in IoT Security: Challenges, Solutions, and Future Directions explores how these smart technologies are critical in securing IoT systems. It explains how they can be used to analyze vast amounts of data, detect anomalies, and respond to evolving threats in real time. It also explores how:TinyML enables intelligent, autonomous defense directly on constrained IoT devicesExplainable AI can enhance transparency, trust, and human–machine collaboration in protecting critical IoT-enabled infrastructureIntegrating deep learning, NLP, reinforcement learning, and SOAR systems demonstrates scalable and explainable intrusion detection across IoT, cloud, and edge environmentsEnsemble learning can achieve accurate and timely detection with acceptable computational overhead.Providing a comprehensive and forward-looking perspective on securing IoT ecosystems using AI and ML, the book is a critical reference for researchers, practitioners, graduate students, and industry professionals seeking to design intelligent, resilient, and privacy-aware IoT security solutions.

SMART HEALTHCARE SYSTEM DESIGN This book deeply discusses the major challenges and issues for security and privacy aspects of smart health-care systems. The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security. The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies. Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations. Audience: Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable.

CYBER SECURITY AND NETWORK SECURITYWritten and edited by a team of experts in the field, this is the most comprehensive and up-to-date study of the practical applications of cyber security and network security for engineers, scientists, students, and other professionals. Digital assaults are quickly becoming one of the most predominant issues on the planet. As digital wrongdoing keeps on expanding, it is increasingly more important to investigate new methodologies and advances that help guarantee the security of online networks. Ongoing advances and innovations have made great advances for taking care of security issues in a methodical manner. In light of this, organized security innovations have been delivered so as to guarantee the security of programming and correspondence functionalities at fundamental, improved, and engineering levels.This outstanding new volume covers all of the latest advances, innovations, and developments in practical applications for cybersecurity and network security. This team of editors represents some of the most well-known and respected experts in the area, creating this comprehensive, up-to-date coverage of the issues of the day and state of the art. Whether for the veteran engineer or scientist or a student, this volume is a must-have for any library.

This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.

This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.

This book gathers selected papers presented at Sixth International Symposium on Signal and Image Processing (ISSIP 2025), organized by Rochester Institute of Technology Kosovo, Kosovo, during November 24 – 25, 2025. It presents fascinating, state-of-the-art research findings in the field of signal and image processing. It includes conference papers covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis.

This book features research papers presented at the International Conference on Data Mining and Information Security (ICDMIS 2025) held in Phnom Penh in collaboration with Eminent College of Management & Technology (ECMT) and University of Puthisastra, Cambodia, during October 7–8, 2025. The book is organized in five volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT), and information security.

This book presents a curated collection of peer-reviewed research papers from the 4th International Conference on Innovations in Data Analytics (ICIDA 2025), held at Eminent College of Management & Technology (ECMT), India, in collaboration with the International Knowledge Research Foundation, on 5–6 December 2025. The conference served as a dynamic platform for academicians, researchers, and industry professionals to exchange ideas and showcase advancements in the rapidly evolving domains of data analytics and intelligent systems.In today’s data-driven world, the integration of Data Mining, Machine Learning, and Information Security has become essential for extracting meaningful insights and ensuring the protection of sensitive information. This book highlights cutting-edge research that addresses contemporary challenges in handling large-scale data, developing intelligent computational models, and securing digital infrastructures.This book encompasses a wide spectrum of topics, including Artificial Intelligence, Data Analytics, Internet of Things (IoT), Information Security, and emerging trends in computing and communication technologies. The book reflects both theoretical innovations and practical applications, offering valuable insights for researchers, practitioners, and students alike.By bringing together high-quality work from diverse domains, this book aims to foster interdisciplinary research and contribute to the advancement of knowledge in data analytics and its allied fields.

This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.

This book gathers a collection of high-quality peer-reviewed research papers presented at International Conference on Cyber Intelligence and Information Retrieval (CIIR 2021), held at Institute of Engineering & Management, Kolkata, India during 20–21 May 2021. The book covers research papers in the field of privacy and security in the cloud, data loss prevention and recovery, high-performance networks, network security and cryptography, image and signal processing, artificial immune systems, information and network security, data science techniques and applications, data warehousing and data mining, data mining in dynamic environment, higher-order neural computing, rough set and fuzzy set theory, and nature-inspired computing techniques.

The book covers computational statistics, its methodologies and applications for IoT device. It includes the details in the areas of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design of experiments, Bayesian analysis, survival analysis and data mining in computational statistics.