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Applications of Artificial Intelligence and Machine Learning
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning—ICAAAIML 2021. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business, and security. This book contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book is a valuable resource for students, academics, and practitioners in the industry working on AI applications.
R 8,781.97
Artificial Intelligence and Machine Learning for Real-World Applications
This book introduces foundational and advanced concepts in artificial intelligence (AI) and machine learning (ML), focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and generative AI, Artificial Intelligence and Machine Learning for Real-World Applications: A Beginner's Guide with Case Studies includes practical Python implementations and case studies from healthcare, agriculture, and education. Beginning with core concepts such as AI fundamentals, knowledge representation, and statistical techniques, the text gradually advances to cover ML algorithms, deep learning architectures, and the basics of generative AI. Detailed discussions of data preprocessing, model training, evaluation metrics, and Python-based implementation make this book both practical and accessible.Offers real-world examples and case studies illustrating the societal impact and practical applications of AI and ML technologiesDiscusses data preprocessing techniques, model selection, and evaluation metrics with practical implementation in Python and in detailExplores AI problem-solving processes, knowledge representation, and model training strategies, catering to readers with varying levels of technical expertiseCovers AI and ML principles spanning statistical techniques, ML algorithms, deep learning structures, and generative AI basicsFocuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by the elderly and special needs individualsThis book is for professionals, researchers, and scholars interested in the application of AI and ML.
R 4,459.58
Artificial Intelligence and Machine Learning for Real-World Applications
This book introduces foundational and advanced concepts in artificial intelligence (AI) and machine learning (ML), focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and generative AI, Artificial Intelligence and Machine Learning for Real-World Applications: A Beginner's Guide with Case Studies includes practical Python implementations and case studies from healthcare, agriculture, and education. Beginning with core concepts such as AI fundamentals, knowledge representation, and statistical techniques, the text gradually advances to cover ML algorithms, deep learning architectures, and the basics of generative AI. Detailed discussions of data preprocessing, model training, evaluation metrics, and Python-based implementation make this book both practical and accessible.Offers real-world examples and case studies illustrating the societal impact and practical applications of AI and ML technologiesDiscusses data preprocessing techniques, model selection, and evaluation metrics with practical implementation in Python and in detailExplores AI problem-solving processes, knowledge representation, and model training strategies, catering to readers with varying levels of technical expertiseCovers AI and ML principles spanning statistical techniques, ML algorithms, deep learning structures, and generative AI basicsFocuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by the elderly and special needs individualsThis book is for professionals, researchers, and scholars interested in the application of AI and ML.
R 5,092.68
Machine Learning and Artificial Intelligence with Industrial Applications
This book presents the tools used in machine learning (ML) and the benefits of using such tools in facilities. It focus on real life business applications, explaining the most popular algorithms easily and clearly without the use of calculus or matrix/vector algebra. Replete with case studies, this book provides a working knowledge of ML current and future capabilities and the impact it will have on every business. It demonstrates that it is also possible to carry out successful ML and AI projects in any manufacturing plant, even without fully fulfilling the five V (Volume, Velocity, Variety, Veracity and Value) usually associated with big data. This book takes a closer look at how AI and ML are also able to work for industrial area, as well as how you could adapt some of the standard tips and techniques (usually for big data) for your own needs in your SME. Organizations which first understand these tools and know how to use them will benefit at the expense of their rivals.
R 7,661.07
Artificial Intelligence and Machine Learning
This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.
R 6,648.72
Machine Learning and Artificial Intelligence with Industrial Applications
This book presents the tools used in machine learning (ML) and the benefits of using such tools in facilities. It focus on real life business applications, explaining the most popular algorithms easily and clearly without the use of calculus or matrix/vector algebra. Replete with case studies, this book provides a working knowledge of ML current and future capabilities and the impact it will have on every business. It demonstrates that it is also possible to carry out successful ML and AI projects in any manufacturing plant, even without fully fulfilling the five V (Volume, Velocity, Variety, Veracity and Value) usually associated with big data. This book takes a closer look at how AI and ML are also able to work for industrial area, as well as how you could adapt some of the standard tips and techniques (usually for big data) for your own needs in your SME. Organizations which first understand these tools and know how to use them will benefit at the expense of their rivals.
R 7,405.72
Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry
Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy.Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations.This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.
R 5,675.21
Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing.Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.
R 9,660.32
Artificial Intelligence and Machine Learning
This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.
R 2,975.02
Artificial Intelligence and Machine Learning for Smart Community
Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.• Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems• Discusses the integration of artificial intelligence with machine learning using mathematical modeling• Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices• Introduces modern algorithms such as convolutional neural networks and support vector machines• Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.
R 6,653.42
Artificial Intelligence and Machine Learning Applications for Sustainable Development
The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems.This book:Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational research.Showcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applications.Illustrates strategies for sustainable development using artificial intelligence and machine learning applications. Presents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilization.Highlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sources.It is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, energy, and environmental engineering.
R 9,962.88
Artificial Intelligence and Applications
This book constitutes the proceedings of the 26th International Conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024.The 38 full papers included in this book were carefully reviewed and selected from 376 submissions. They have been organized in topical sections as follows: Deep convolutional neural networks, ANNs, and applications; machine learning and novel applications; large language models and applications; data science, recognition and authentication methods and applications; artificial intelligence and applications; XXIV Technical Session on Applications of Advanced AI Techniques to information management for solving company-related problems.
R 4,304.11
Machine Learning and Artificial Intelligence
The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned.The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs.
R 2,931.00
Artificial Intelligence and Machine Learning Applications for Sustainable Development
The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems.This book:Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational researchShowcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applicationsIllustrates strategies for sustainable development using artificial intelligence and machine learning applicationsPresents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilizationHighlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sourcesIt is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical, electronics and communications, energy, and environmental engineering.
R 3,187.00
Applications of Machine Learning and Artificial Intelligence in Education
Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era's current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated—or traditional—learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline.
R 8,315.41
Applications of Machine Learning and Artificial Intelligence in Education
Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era's current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated—or traditional—learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline.
R 6,391.47
R 1,107.00
R 4,592.06
Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry
Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy.Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations.This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.
R 2,711.00
Artificial Intelligence and Machine Learning Fundamentals
Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook DescriptionMachine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is forArtificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
R 1,140.00
Artificial Intelligence and Machine Learning Algorithms for Engineering Applications
This book comprehensively covers core algorithms and techniques used in artificial intelligence (AI) and machine learning (ML) for engineering applications. It further explores the use of AI in civil and structural engineering, quality control, and product design.Features:Presents autonomous robots using onboard computing and artificial intelligence (AI) algorithms to process the data from their sensors and make real-time decisionsDiscusses nature-based optimization-based computing techniques to enhance the computational speed for solving engineering problemsProvides conceptual and practical knowledge about the design of modern computation techniques with advanced tools and methodologiesHighlights the importance of using smart techniques including AI and ML in product design and developmentCovers time series analysis and forecasting in engineering, robotic process automation, and autonomous robots in manufacturingThe text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, manufacturing engineering, and environmental engineering.
R 8,248.58
Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications
This book presents various machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: biomedical engineering, signal processing, and computer science.Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications explores recent advancements in the use of AI/ML in practical engineering applications by inviting top experts to share the outcomes of their most recent work. Among the topics explored are detection, measurement, and monitoring of signals (biosensors and biomedical devices) and the use of diagnostic interpretations of bioelectric data using signal-processing techniques. The authors also address several machine learning tasks, such as classification (supervised learning) and clustering (unsupervised learning), in the context of engineering. Finally, the book also describes the development of new biomaterials for use in the body.The book will be a great help to researchers and academics working in the fields of biomedical signaling and/or human-machine interface.
R 7,075.23
Artificial Intelligence and Machine Learning for Smart Community
Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systemsDiscusses the integration of artificial intelligence with machine learning using mathematical modelingElaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matricesIntroduces modern algorithms such as convolutional neural networks and support vector machinesPresents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.
R 2,979.74
Advances and Trends in Artificial Intelligence. Theory and Applications
This book constitutes the refereed proceedings of the 39th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems on Advances and Trends in Artificial Intelligence, IEA/AIE 2026, held in Kuala Lumpur, Malaysia, July 6–8, 2026.The 99 full papers and 8 short papers included in this book were carefully reviewed and selected from 210 submissions. They focus on the following topical sections: Industry, Manufacturing and Fault Diagnosis; Security, Privacy and Trustworthy AI; Reinforcement and Multi-Agent Learning; Machine Learning Methods and Evaluation; Adaptive Learning and Contrastive/Energy-Based Methods; Fuzzy Systems, Optimization and Emerging Applications.
R 2,233.40
Artificial Intelligence and Machine Learning for Sustainable Development
Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development.The book examines cutting edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. It would be of special ot researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.
R 7,613.89
Artificial Intelligence and Machine Learning Techniques for Civil Engineering
In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.
R 9,679.66
Artificial Intelligence and Knowledge Processing
Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book.This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.
R 9,333.79
R 4,070.00
AI and Machine Learning Applications and Implications in Customer Support and Analytics
In the modern data-driven era, artificial intelligence (AI) and machine learning (ML) technologies that allow a computer to mimic intelligent human behavior are essential for organizations to achieve business excellence and assist organizations in extracting useful information from raw data. AI and ML have existed for decades, but in the age of big data, this sort of analysis is in higher demand than ever, especially for customer support and analytics. AI and Machine Learning Applications and Implications in Customer Support and Analytics investigates the applications of AI and ML and how they can be implemented to enhance customer support and analytics at various levels of organizations. This book is ideal for marketing professionals, managers, business owners, researchers, practitioners, academicians, instructors, university libraries, and students, and covers topics such as artificial intelligence, machine learning, supervised learning, deep learning, customer sentiment analysis, data mining, neural networks, and business analytics.
R 12,552.27
Artificial Intelligence and Machine Learning for Sustainable Development
Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development.The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.
R 2,674.00
Application of Artificial Intelligence
This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming.After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments.The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
R 3,510.92
Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.
R 7,355.36
Artificial Intelligence and Machine Learning Techniques in Engineering and Management
The present book covers various facets of Artificial Intelligence, Machine Learning, and Fuzzy Logic. It includes a brief discussion on performance indicators, Classical and Advanced Machine Learning algorithms, Fuzzy logic-based modelling algorithms, Emerging Research Areas, including Blockchain, recent ML techniques, Evolutionary Algorithms, Large Language Model (LLM)-based Generative AI, the Internet of Things, Big Data, Decision Support Systems, Taguchi design of experiments, data augmentation, and Cross-Validation, and representative case studies. The appendix covers representative AI tools, data sources, books, and journals on AI. The present book can support undergraduate, postgraduate, and Ph.D. students in Artificial Intelligence, Generative Artificial Intelligence, Machine Learning, Data Sciences, Soft Computing, and Fuzzy Logic in Engineering and Management and allied fields. The proposed book has immense value in the interdisciplinary and cross-disciplinary context.
R 3,663.00
Measurement, Automation, and Control in Artificial Intelligence and Machine Learning
This book first provides users with the fundamentals of process automation, process automation architecture, and their application areas. The authors then show how artificial intelligence (AI) and machine learning (ML) enhance these fields. The book delves into the transformative era of integrative process automation and control engineering (IPACE) and aims to empower readers to harness the power of Industry 4.0.It transcends siloed approaches, uniting cutting-edge technologies like the Industrial Internet of Things (IIoT), AI, ML, and digital twins with established process automation and control principles. Furthermore, the authors share hands-on experience with the technology along with applications in various sectors such as transportation, smart homes, production/manufacturing, renewable energy, and agriculture. The book discusses new technological advances, current research trends, and industry requirements. This book serves as a roadmap for process automation practitioners, engineers, scientists, executives, and students, inspiring them to embrace the opportunities and challenges of IPACE and shape the future of industrial automation.
R 8,397.10
Artificial Intelligence, Machine Learning, and Data Science Technologies
This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.
R 9,321.13
Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing
Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
R 2,984.74
Artificial Intelligence and Machine Learning for COVID-19
This book is dedicated to addressing the major challenges in fighting COVID-19 using artificial intelligence (AI) and machine learning (ML) – from cost and complexity to availability and accuracy. The aim of this book is to focus on both the design and implementation of AI-based approaches in proposed COVID-19 solutions that are enabled and supported by sensor networks, cloud computing, and 5G and beyond. This book presents research that contributes to the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases. The authors present the latest theoretical developments, real-world applications, and future perspectives on this topic. This book brings together a broad multidisciplinary community, aiming to integrate ideas, theories, models, and techniques from across different disciplines on intelligent solutions/systems, and to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies.
R 4,478.09
Machine Learning and Artificial Intelligence in Healthcare Systems
This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques discusses AI-based smart paradigms for reliable prediction of infectious disease dynamics; such paradigms can help prevent disease transmission. It highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research. The book offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods. Case studies illustrating the business processes that underlie the use of big data and health analytics to improve healthcare delivery are center stage. Innovative techniques used for extracting user social behavior (known as sentiment analysis for healthcare-related purposes) round out the diverse array of topics this reference book covers. Contributions from experts in the field, this book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.
R 9,961.12
AI and Machine Learning Applications in Supply Chains and Marketing
While artificial intelligence (AI) simulates human intelligence in machines, machine learning (ML) enables systems to learn from data without explicit programming. In marketing and supply chain management, AI and ML empower businesses to analyze consumer behavior, personalize experiences, optimize advertising strategies, forecast consumer demands, manage inventory, plan routes, and mitigate risks. Businesses can enhance efficiency, accuracy, decision-making, customer engagement, and cost-effectiveness when integrating AI and ML in marketing and supply chain operations. Further research is necessary to drive success in the dynamic marketplace. AI and Machine Learning Applications in Supply Chains and Marketing bridges the gap between theoretical knowledge and practical application of AI and ML in marketing and supply chain management. It examines emerging technologies that can revolutionize industries by transforming business operations. This book covers topics such as data analysis, sustainable development, and blockchain, and is a useful resource for business owners, economists, marketing professionals, engineers, computer scientists, academicians, and researchers.
R 10,069.15
Explainable Artificial Intelligence and Interpretable Machine Learning in Education
In a rapidly evolving landscape of educational research, explainable artificial intelligence (XAI) and interpretable machine learning (ML) are emerging as pivotal tools that enhance transparency, efficiency, and innovation. This book serves as a comprehensive guide to understanding and leveraging these technologies to transform teaching, learning, and research practices. It aims to bridge the gap between complex technological advancements and practical educational applications. It delves into how XAI and ML can be harnessed to analyze vast educational datasets, assess student performance, and design adaptive learning environments, all while ensuring the interpretability and ethical deployment of AI systems. Through a blend of theoretical insights and real-world examples, the book explores topics such as the foundations of XAI, the development of ML algorithms for education, and the ethical implications of data-driven decision-making. A unique feature of this volume is its interdisciplinary approach, combining perspectives from educators, researchers, and data scientists. It emphasizes collaboration and encourages contributors to address emerging trends, challenges, and opportunities in the application of XAI and ML. Case studies from diverse educational contexts provide practical insights and inspire innovative solutions to pressing educational issues. The book serves as a comprehensive and definitive guide for practitioners and researchers dedicated to enhancing educational processes.
R 6,118.00
