Subrata Chowdhury - Böcker

Harnessing Artificial Intelligence to Ensure Diverse Global Teams explores new research and applications of AI which can be used to address the distinct challenges of diverse, distributed teams. Incorporating compelling case studies and strategic guidance, the book demonstrates how AI can be developed and applied within systems and programs to promote inclusion, break down barriers, and enhance collaboration in cross-cultural organizational settings. Providing case studies and examples, this book equips computer scientists and engineers with actionable strategies for integrating AI seamlessly into programs and applications designed for diverse global teams. With contributions from experts in AI and team dynamics, this book will benefit leaders and team members seeking to leverage AI for improved teamwork across geographical and cultural boundaries, providing an up-to-date resource for uniting, uplifting, and optimizing global collaborations through inclusive artificial intelligence.Provides key insights into how AI can be applied to address the specific challenges of diverse global teamsDiscusses how AI developers consider bias, data privacy, and the ethical implantation of AIAddresses challenges and opportunities in using artificial intelligence and its various applications, offering a roadmap for leaders looking to leverage AI for team success

This book focuses on futuristic approaches and designs for real-time systems and applications, as well as the fundamental concepts of including advanced techniques and tools in models of data-driven blockchain ecosystems.The Data-Driven Blockchain Ecosystem: Fundamentals, Applications, and Emerging Technologies discusses how to implement and manage processes for releasing and delivering blockchain applications. It presents the core of blockchain technology, IoT-based and AI-based blockchain systems, and various manufacturing areas related to Industry 4.0. The book illustrates how to apply design principles to develop and manage blockchain networks, and also covers the role that cloud computing plays in blockchain applications.All major technologies involved in blockchain-embedded applications are included in this book, which makes it useful to engineering students, researchers, academicians, and professionals interested in the core of blockchain technology.

This book focuses on futuristic approaches and designs for real-time systems and applications, as well as the fundamental concepts of including advanced techniques and tools in models of data-driven blockchain ecosystems.The Data-Driven Blockchain Ecosystem: Fundamentals, Applications, and Emerging Technologies discusses how to implement and manage processes for releasing and delivering blockchain applications. It presents the core of blockchain technology, IoT-based and AI-based blockchain systems, and various manufacturing areas related to Industry 4.0. The book illustrates how to apply design principles to develop and manage blockchain networks, and also covers the role that cloud computing plays in blockchain applications.All major technologies involved in blockchain-embedded applications are included in this book, which makes it useful to engineering students, researchers, academicians, and professionals interested in the core of blockchain technology.

Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.This book:Discusses the 5G new radio system design and architecture as specified in 3GPP documentsHighlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systemsIdentifies both theoretical and practical problems that can occur in mobile communication systemsCovers machine learning techniques such as autoencoder and Q-learning in a comprehensive mannerExplores how to apply machine learning techniques to mobile systems to solve modern problemsThis book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.This book:Discusses the 5G new radio system design and architecture as specified in 3GPP documentsHighlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systemsIdentifies both theoretical and practical problems that can occur in mobile communication systemsCovers machine learning techniques such as autoencoder and Q-learning in a comprehensive mannerExplores how to apply machine learning techniques to mobile systems to solve modern problemsThis book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

The development of intelligent transportation systems, especially autonomous underwater vehicles, has become significant in marine engineering, with an aim to enhance energy efficiency management and communication systems. This book covers different aspects of optimization of autonomous underwater vehicles and their propulsion systems via machine learning techniques. It further analyses hydrodynamic characteristics including the study of experimental investigation combined with hydrodynamic characteristics backed by MATLAB® codes and simulation study results. Features: Covers utilization of machine learning techniques with a focus on marine science and ocean engineering. Details effect of the intelligent transportation system (ITS) into the sustainable environment and ecology system. Evaluates performance of particle swarm intelligence-based optimization techniques. Reviews propulsion performance of the remote-controlled vehicles based on machine learning techniques. Includes MATLAB® examples and simulation study results.This book is aimed at graduate students and researchers in marine engineering and technology, computer science, and control system engineering.

The development of intelligent transportation systems, especially autonomous underwater vehicles, has become significant in marine engineering, with an aim to enhance energy efficiency management and communication systems. This book covers different aspects of optimization of autonomous underwater vehicles and their propulsion systems via machine learning techniques. It further analyses hydrodynamic characteristics including the study of experimental investigation combined with hydrodynamic characteristics backed by MATLAB® codes and simulation study results. Features: Covers utilization of machine learning techniques with a focus on marine science and ocean engineering. Details effect of the intelligent transportation system (ITS) into the sustainable environment and ecology system. Evaluates performance of particle swarm intelligence-based optimization techniques. Reviews propulsion performance of the remote-controlled vehicles based on machine learning techniques. Includes MATLAB® examples and simulation study results.This book is aimed at graduate students and researchers in marine engineering and technology, computer science, and control system engineering.

This book explores the critical issue of fairness in human-machine interfaces. It delves into the integration of technology and cognitive science to develop AI systems that are unbiased, reliable, and user-friendly. The book also sheds light on emotional data processing in AI accelerators and federated learning modules. Additionally, it covers machine learning, knowledge representation, and the application of knowledge graphs to understand and optimize the behaviour of AI assistance devices.Features:Explains complex issues of Cognitive Fairness Aware Contextual Proactive Federated Protocol collects data and identifies individual emotional issues and resolves them by contextual solitary proactive communicationDiscusses emotional data processing challenges through AI accelerator with federated learning module to generate periodical counselling messagesAddresses data analysis anomalies in Graph Database Modelling by anom-aly prediction and anomaly detectionDescribes anomaly detection techniques in the form of abnormal data records, messages, events, groups, and/or other unexpected observations in graph database modellingExplains how outlier detection for data analysis deals with the detection of patterns in Graph DatabaseThis book is for researchers, academics, students, AI practitioners and developers, ethics experts in AI technology and machine-learning practitioners interested in fairness in human-machine interfaces.

The integration of AI in detecting autism holds promise for early intervention, personalized treatment plans, enhanced accuracy, and improved outcomes for individuals on the autism spectrum.The book discusses the use of AI in detecting autism spectrum disorder (ASD). AI analyses behavioral patterns, speech, and cognitive responses. Machine learning algorithms are used to identify potential markers of ASD. AI can process vast amounts of data, including social interactions, facial expressions, and language nuances to help arrive at a diagnosis that would otherwise not be possible. AI-driven tools like natural language processing, computer vision, and predictive analytics provide objective assessments. These assessments complement traditional diagnostic methods, offer more accurate and efficient evaluations. Further, AI assists data analysis models in identifying unnoticeable behavioral signals that might normally escape detection by human observation, enhancing the precision of ASD diagnosis. Covering AI applications in detection, diagnosis, and life support, the book highlights how technology is reshaping autism care and research.Key Features:Investigates recent research on AI based autism diagnosis modelsExplores various ML models for diagnosis using behaviour analysis, speech analysisReviews various AI models for clinical data analysisInvestigates early prediction toolsExplores tools for assisting and parental care

This book presents a comprehensive analysis of the historical progression, present condition, and prospective trajectory of biomedical robotics through the lens of engineering. The analysis investigates the transformative impact on healthcare delivery, with a focus on precision, adaptability, and integration of artificial intelligence.BioMed Bots Assurance: Securing Robotics, Healthcare, and IT Convergence commences by providing readers with an extensive exposition of the swiftly progressing field of biomedical robotics, thereby establishing a solid foundation for a meticulous scrutiny of its transformative capacity within contemporary healthcare. This book explores multiple challenges that happen during the integration of robotics into healthcare facilities, with extra focus on implementation protocols of robust IT security. It bestows in-depth knowledge of responsible and ethical use of biomedical robotics in health care through its care analyses on privacy concerns and ethics issues. The authors include several case studies that show the realization of successful biomedical robotic systems in real-world environments, address the regulatory landscape, and offer guidance on maintaining healthcare standards.The book is crafted for a wide-ranging audience, including undergraduate and postgraduate students, professionals, researchers, and practitioners in the fields of biomedical engineering, robotics, healthcare technology, and information technology (IT) security. It serves as an extensive resource covering fundamental concepts, advanced research, and practical applications within the realm of biomedical robotics.

Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify.Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties.Deep Learning in Medical Image Processing and Analysis introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed.Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models.

Generative AI Revolution: Transforming Healthcare and Life Sciences towards Sustainable Development in Industry 6.0 examines how generative artificial intelligence is redefining modern healthcare systems. As the world moves toward Industry 6.0—an era of human-centric, intelligent, and sustainable innovation—this book explores how AI enhances patient care, medical diagnostics, drug discovery, and population health management. Organized into three thematic sections, the volume covers AI-driven patient engagement, mental health analytics, disease prediction models, deep learning–based imaging diagnostics, and AI-enabled drug development pipelines. It also addresses healthcare supply chains, epidemiological modeling, and data-driven decision support systems that strengthen resilience and efficiency in healthcare ecosystems. Emphasizing ethical and responsible AI adoption aligned with global sustainability goals, this book is an essential resource for researchers, healthcare professionals, policymakers, and industry leaders seeking to harness generative AI to build smarter, more sustainable healthcare solutions.

This book provides a global forum for leading experts in artificial intelligence to present their latest discoveries and concepts, as well as to collaborate on advancing novel intersubjective insights and methodologies. AIR's objective is to provide an environment that integrates human and technological demands with intelligent systems by merging innovative approaches with scientifically grounded procedures. This volume serves as a motivational resource for current research in artificial intelligence. The results presented here stem from a comprehensive study on autonomous functions and artificial intelligence, as well as their relevance to real-world issues. In a rapidly advancing technology landscape, human analysis, decision-making, and creativity are being transformed. The principles of artificial intelligence originate from the formulation of computations aimed at fostering intelligence and evolve into analytical tools that are universally accessible.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the world still lacks a fully integrated healthcare system. The intrinsic complexity and development of human biology, as well as the differences across patients, have repeatedly demonstrated the significance of the human element in the diagnosis and treatment of illnesses. But as digital technology develops, healthcare providers will undoubtedly need to use it more and more to give patients the best treatment possible. The extensive use of machine learning in numerous industries, including healthcare, has been made possible by advancements in data technologies, including storage capacity, processing capability, and data transit speeds. The need for a personalized medicine or "precision medicine" approach to healthcare has been highlighted by current trends in medicine due to the complexity of providing effective healthcare to each individual. Personalized medicine aims to identify, forecast, and analyze diagnostic decisions using vast volumes of healthcare data so that doctors may then apply them to each unique patient. These data may include, but are not limited to, information on a person’s genes or family history, medical imaging data, drug combinations, patient health outcomes at the community level, and natural language processing of pre-existing medical documentation.This book provides various insights into machine learning techniques in healthcare system data and its analysis. Recent technological advancements in the healthcare system represent cutting-edge innovations and global research successes in performance modelling, analysis, and applications.