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Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.
This book describes the Throughput Model methodology that can enable individuals and organizations to better identify, understand, and use algorithms to solve daily problems. The Throughput Model is a progressive model intended to advance the artificial intelligence (AI) field since it represents symbol manipulation in six algorithmic pathways that are theorized to mimic the essential pillars of human cognition, namely, perception, information, judgment, and decision choice. The six AI algorithmic pathways are (1) Expedient Algorithmic Pathway, (2) Ruling Algorithmic Guide Pathway, (3) Analytical Algorithmic Pathway, (4) Revisionist Algorithmic Pathway, (5) Value Driven Algorithmic Pathway, and (6) Global Perspective Algorithmic Pathway. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Key Features: - Covers general concepts of Artificial intelligence and machine learning - Explains the importance of dominant AI algorithms for business and AI research - Provides information about 6 unique algorithmic pathways in the Throughput Model - Provides information to create a roadmap towards building architectures that combine the strengths of the symbolic approaches for analyzing big data - Explains how to understand the functions of an AI algorithm to solve problems and make good decisions - informs managers who are interested in employing ethical and trustworthiness features in systems. Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems.
Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.
Advances in Computers, Volume presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on - Contains novel subject matter that is relevant to computer science - Includes the expertise of contributing authors - Presents an easy to comprehend writing style
This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.
This book is devoted to the new standards, technologies, and communication systems for Artificial Intelligence of Things (AIoT) networks. Smart and intelligent communication networks have gained significant attention due to the combination of AI and IoT networks to improve human and machine interfaces and enhance data processing and services. AIoT networks involve the collection of data from several devices and sensor nodes in the environment. AI can enhance these networks to make them faster, greener, smarter, and safer. Computer vision, language processing, and speech recognition are some examples of AIoT networks. Due to a large number of devices in today’s world, efficient and intelligent data processing is essential for problem-solving and decision-making. AI multiplies the value of these networks and promotes intelligence and learning capabilities, especially in homes, offices, and cities. However, several challenges have been observed in deploying AIoT networks, such as scalability, complexity, accuracy, and robustness. In addition, these networks are integrated with cloud, 5G networks, and blockchain methods for service provision. Many different solutions have been proposed to address issues related to machine and deep learning methods, ontology-based approaches, genetic algorithms, and fuzzy-based systems. This book aims to contribute to the state of the art and present current standards, technologies, and approaches for AIoT networks. This book focuses on existing solutions in AIoT network technologies, applications, services, standards, architectures, and security provisions. This book also introduces some new architectures and models for AIoT networks.
In the landscape of e-commerce, data security has become a concern as businesses navigate the complexities of sensitive customer information protection and cyber threat mitigation. Strategies involving cloud computing, blockchain technology, artificial intelligence, and machine learning offer solutions to strengthen data security and ensure transactional integrity. Implementing these technologies requires a balance of innovation and efficient security protocols. The development and adoption of security strategies is necessary to positively integrate cutting-edge technologies for effective security in online business. Strategies for E-Commerce Data Security: Cloud, Blockchain, AI, and Machine Learning addresses the need for advanced security measures, while examining the current state of e-commerce data security. It explores strategies such as cloud computing, blockchain, artificial intelligence, and machine learning. This book covers topics such as cybersecurity, cloud technology, and forensics, and is a useful resource for computer engineers, business owners, security professionals, government officials, academicians, scientists, and researchers.
In the field of medical research and technological innovation, few challenges are as profound and pressing as the quest to understand and combat Alzheimer’s Disease. This neurodegenerative disorder, characterized by progressive memory loss and cognitive decline, affects millions of individuals worldwide and poses significant challenges for patients, families, and healthcare systems alike. The search for effective diagnostic tools, prognostic indicators, and therapeutic interventions remains a critical area of scientific inquiry. The emergence of Artificial Intelligence (AI) has heralded a new era of possibilities in healthcare, offering transformative potential for the study and treatment of Alzheimer’s Disease. By leveraging advanced computational techniques, machine learning algorithms, and data analytics, AI holds the promise of revolutionizing our approach to understanding the complexities of this disease. From early diagnosis to personalized treatment and patient monitoring, AI's applications in Alzheimer’s research and care are rapidly expanding, presenting both opportunities and challenges that warrant thorough exploration. This book aims to provide a comprehensive overview of the intersection between AI and Alzheimer’s Disease. It is designed to serve as a valuable resource for researchers, clinicians, policymakers, and students who are engaged in the fields of neurology, artificial intelligence, and healthcare technology. Through a detailed examination of current advancements, practical applications, and future directions, this work seeks to illuminate the transformative impact of AI on Alzheimer’s research and patient care.
This volume provides an interdisciplinary collection of essays from leaders in various fields addressing the current and future challenges arising from the implementation of AI in brain and mental health. Artificial Intelligence (AI) has the potential to transform health care and improve biomedical research. While the potential of AI in brain and mental health is tremendous, its ethical, regulatory and social impacts have not been assessed in a comprehensive and systemic way. The volume is structured according to three main sections, each of them focusing on different types of AI technologies. Part 1, Big Data and Automated Learning: Scientific and Ethical Considerations, specifically addresses issues arising from the use of AI software, especially machine learning, in the clinical context or for therapeutic applications. Part 2, AI for Digital Mental Health and Assistive Robotics: Philosophical and Regulatory Challenges, examines philosophical, ethical and regulatory issues arising from the use of an array of technologies beyond the clinical context. In the final section of the volume, Part 3 entitled AI in Neuroscience and Neurotechnology: Ethical, Social and Policy Issues, contributions examine some of the implications of AI in neuroscience and neurotechnology and the regulatory gaps or ambiguities that could potentially hamper the responsible development and implementation of AI solutions in brain and mental health. In light of its comprehensiveness and multi-disciplinary character, this book marks an important milestone in the public understanding of the ethics of AI in brain and mental health and provides a useful resource for any future investigation in this crucial and rapidly evolving area of AI application. The book is of interest to a wide audience in neuroethics, robotics, computer science, neuroscience, psychiatry and mental health.