This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.

Intelligent Computational Systems presents current and future developments in artificial Intelligence (AI) in a multi-disciplinary context. Readers will learn about the pervasive and ubiquitous roles of artificial intelligence and gain a perspective about the need for intelligent systems to behave rationally when interacting with humans in complex and realistic domains. This reference covers widespread applications of AI discussed in 11 chapters which cover topics such as AI and behavioral simulations, AI schools, automated negotiation, language analysis and learning, financial prediction, sensor management, Multi-agent systems, and much more. This reference work will assist researchers, advanced-level students and practitioners in information technology and computer science fields interested in the broad applications of AI.

The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence.

This reference distills information about emerging technologies and applications for smart city design and sustainable urban planning. Chapters present technology use-cases that have radical novelty and high scalability with a prominent impact on community living standards. These technologies prepare urban and rural dwellings for the transformation to the smart world. Applications and techniques highlighted in the book use a combination of artificial intelligence and IoT technologies in areas like transportation, energy, healthcare, education, governance, and manufacturing, to name a few. The book serves as a learning resource for smart city design and sustainable infrastructure planning. Scholars and professionals who are interested in understanding ways for transforming communities into smart communities can also benefit from the cases presented in the book.

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.

Introduction to Sensors in IoT and Cloud Computing Applications provides information about sensors and their applications. Readers are first introduced to the concept of small instruments and their application as sensors. The chapters which follow explain Internet of Things (IoT) architecture while providing notes on the implementation, demonstration and related issues of IoT systems. The book continues to explore the topic by providing information about sensor-cloud infrastructure, mobile cloud, fog computing (an extension of cloud computing that takes cloud computing to the cutting-edge of networking where data is produced) and integration of IoT devices with cloud computing. The book also presents notes on the taxonomy of fog-computing systems. The six chapters in this book provide essential information for general readers, and students of computer science to understand the basics of cloud computing networks, related concepts and applications.

This book informs the reader about applications of ArtificialIntelligence (AI) and nature-inspired algorithms in different situations. Eachchapter in this book is written by topic experts on AI, nature-inspired algorithmsand data science. Chapters are structured to provide an introduction to the topic, the computational methodology required for experimentation and analysis, and a discussion of results results The basic concepts relevant to these topics are explained, includingevolutionary computing (EC), artificial neural networks (ANN), swarmintelligence (SI), and fuzzy systems (FS). Additionally, the book also covers optimizationalgorithms for data analysis. The contents include algorithms that can be used in systems designedfor plant science research, environmental analysis, computer vision andhealthcare. There are a variety of use cases highlighted in the book that demonstrate how computer algorithms can be used to simulate and understand natural phenomena, such as moving object detection, COVID-19 detection, genetic diversity, physiology and much more. Additionally, the contributors provide useful tips specific to some algorithsm such as load balancing techniques and fuzzy PID controls. The goal of the book is to equip the reader students and data analysts with the information needed to apply basic AI algorithms to resolve actualproblems encountered in a professional environment.

Demystifying Emerging Trends in Green Technology explores the transformative intersection of computational intelligence, disruptive technologies, and green innovations. This volume offers insights into diverse fields such as blockchain, IoT, artificial intelligence, machine learning, and sustainable development. Each chapter presents cutting-edge research and practical solutions addressing environmental sustainability, energy efficiency, and eco-friendly technologies.With contributions from leading researchers, this book discusses advancements like blockchain-based security, green marketing, smart waste management, sustainable agriculture, and innovative healthcare solutions. It emphasizes the role of interdisciplinary approaches in driving a greener and smarter future.Key Features:- Integration of AI, IoT, and blockchain in sustainable systems- Applications in healthcare, agriculture, energy, and environmental science- Practical and innovative solutions for real-world challenges- Insights into future trends in green technology and disruptive innovationReadership:Ideal for academics, researchers, professionals, and enthusiasts seeking to advance green technology practices.

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems. The book also presents solutions for analyzing complex patterns in user data and improving productivity. Readers will be able to understand the technology that drives big data solutions in HCI projects and understand its capacity in transforming an organization.The book also helps the reader to understand HCI system design and explains how to evaluate an application portfolio that can be used when selecting pilot projects.This book is a resource for researchers, students, and professionals interested in the fields of HCI, artificial intelligence, data analytics, and computer engineering.

The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers. Key Features: - 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology - Provides examples of algorithms for heart diseases, Alzheimers disease, cancer, pneumonia and more - Includes techniques to detect plant disease - Includes information about the application of machine learning in specific imaging modalities - Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN - Each chapter provides an introduction and literature review and the relevant protocols to follow The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.

Intelligent Systems for IoE Based Smart Cities provides simplified information about complexities of cyber physical systems, the Internet of Everything (IoE) and smart city infrastructure. It presents 11 edited chapters that reveal how intelligent systems and IoE are driving the evolution of smart cities, making them more efficient, interconnected, and responsive to the needs of citizens. The book content represents comprehensive exploration of the transformative potential and challenges of IoE-based smart cities, fueled by Artificial Intelligence (AI) and Machine Learning (ML) innovations. Key Topics: Physical layer design considerations that underpin smart city infrastructureEnabling technologies for intelligent systems within the context of smart computing environmentsSmart sensors and actuators, their applications, challenges, and future trends in IoE-based smart citiesApplications, enabling technologies, challenges, and future trends of IoE for smart cities.The integration of Artificial Intelligence, Natural Language Processing, and smart cities for enhanced urban experiencesmachine learning-based intrusion detection techniques for countering attacks on the Internet of VehiclesSmartphone-based indoor positioning applications using trilateration and the role of sensors in IoT ecosystemsIoT, blockchain, and cloud-based technology for secure frameworks and data analyticsBlockchain and smart contracts in shaping the future of smart cities. This is a timely reference for researchers, professionals, and students interested in the convergence IoT, intelligent systems and urban studies into smart city planning and design. Audience Researchers, professionals, and students interested in the convergence of IoT, intelligent systems and urban studies into smart city planning and design.

Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector. This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with an introduction to AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry use-case demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more. Additional features of the book include a summary and references in every chapter, and several full-color images to visualize concepts for easy understanding. It is an ideal handbook for both students and instructors in undergraduate level courses in artificial intelligence, data science, engineering and computer science who are required to understand AI/ML in a practical context. Audience Students and instructors in artificial intelligence, data science, engineering and computer science courses

Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions examines cutting-edge applications, from blockchain-powered IoT innovations in healthcare systems to intelligent health profile management, remote patient monitoring, and healthcare credential verification. Additionally, the book extends its insights into blockchain-enabled IoT applications in smart agriculture, highlighting AI-driven technologies for health management and sustainable practices. With expert analyses, case studies, and practical guidance, this book offers readers a roadmap for implementing these technologies to improve efficiency, security, and data management in healthcare. It is an invaluable resource for industry professionals, researchers, and students interested in the future of healthcare technology. Key Features:- Exploration of blockchain and IoT applications in healthcare and agriculture- In-depth case studies and expert analyses- Practical insights into technology challenges and benefits Readership:Ideal for professionals, researchers, and students in healthcare and technology.

Smart cities and villages have enhanced the quality of lives of residents. Various computer-assisted technologies have been harnessed for the development of smart cities and villages in order to provide solutions for common and niche urban problems. The development of smart environments has been possible due on advances in computing power and artificial intelligence (AI) that have allowed the deployment of scalable technologies. Artificial Intelligence for Smart Cities and Smart Villages: Advanced Technologies, Development, and Challenges summarizes the role of AI in planning and designing smart solutions for urban and rural environments. This book is divided into three sections to impart a better understanding of the topics to readers. These sections are: 1) Demystifying smart cities and villages: A traditional perspective, 2) Smart innovations for rural lifestyle management solutions, and 3) Case studies. Through this book, readers will be able to understand various advanced technologies that are vital to the development of smart cities and villages. The book presents 15 chapters that present effective solutions to urban and rural challenges. Concepts highlighted in chapters include smart farms, indoor object classification systems, smart transportation, blockchains for medical information, humanoid robots for rural education, IoT devices for farming, and much more. This book is intended for undergraduate and graduate engineering students across all disciplines, security providers in the IT and related fields, and trainees working for infrastructure management companies. Researchers and consultants at all levels working in the areas of artificial intelligence, machine learning, IoT, blockchain, network security, and cloud computing will also find the contents beneficial in planning projects involving smart environments.

The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: Comprehensive coverage of advanced mathematical concepts and techniques in data scienceContributions from established scientists, researchers, and academiciansReal-world case studies and practical applications of mathematical methodsFocus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysisIn-depth exploration of data sciences connection with mathematics, computer science, and artificial intelligenceScholarly references for each chapterSuitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.

This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. ReadershipLearners and professionals in healthcare service training programs and health administration departments.

This book is a comprehensive review of secondary metabolite production from plant tissue culture. The editors have compiled 12 meticulously organized chapters that provide the relevant theoretical and practical frameworks in this subject using empirical research findings. The goal of the book is to explain the rationale behind in vitro production of secondary metabolites from some important medicinal plants. Biotechnological strategies like metabolic engineering and the biosynthesis, transport and modulation of important secondary metabolites are explained along with research studies on specific plants. In addition to the benefits of secondary metabolites, the book also aims to highlight the commercial value of medicinal plants for pharmaceutical and healthcare ventures. Topics covered in this part include: 1. Elicitation Strategies and Metabolic Engineering to boost metabolite production with case studies in metabolic engineering with examples of Scaevola Taccada and Catharanthus Roseus. 2. Stress response investigation and the role of glandular trichomes as bio-cell factories 3. Plant growth regulators and rapid regeneration techniques using swift plantlets regeneration and phytochemical characterization 4. Nutraceuticals, antimicrobials, and genomic applications of in vitro cultures with an example of Hassawi rice and its genomics 5. Sustainable approaches for saving endangered medicinal plants The book caters to a wide readership. It primarily prepares graduate students, researchers, biotechnologists, giving them a grasp of the key methodologies in the secondary metabolite production. It is a secondary reference for support executives, industry professionals, and policymakers at corporate and government levels to understand the importance of plant tissue culture and maximizing its impact in the herbal industry. Readership Graduate students and researchers in plant biotechnology courses; industry professionals and policymakers in the herbal industry.