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.

Reinventing Technological Innovations with Artificial Intelligence delves into the transformative impact of Augmented and Virtual Reality (AVR) technology across industries. The book explores the merging of real and digital worlds, paving the way for personalized experiences in areas such as tourism, marketing, education, and more. With the potential to redefine business practices and societal norms in the era of Industry 4.0, AVR technologies hold untapped potential beyond gaming and entertainment. This volume presents a comprehensive overview of the current landscape, challenges, and prospects of integrating AVR with Artificial Intelligence (AI) for innovation and sustainability in various domains. The book presents 11 edited chapters contributed by technology and innovation experts that explore applications of AI, AR and VR technologies in different sectors in both public and private sectors. The editors have included reviews of technologies that impact human resource management, corporate social responsibility, healthcare, supply chain and criminal investigation. The reviews also highlight the role of AI in sustainable agriculture and smart cities. Key Features: Unveils the role of AVR in transforming real surroundings into digitally enhanced personal experiences. Explores AVR's applications beyond gaming in diverse sectors like marketing, construction, education, and more. Discusses challenges such as technical limitations, high costs, and resistance to adopting AVR. Addresses the need to enhance the reliability and effectiveness of AVR technologies in various industries. Provides a comprehensive perspective on AI innovations, AR, and VR technologies with real-world examples. The book is an informative reference for researchers, professionals, and experts in technology, innovation, who are interested in the convergence of Augmented and Virtual Reality with AI for practical applications in diverse industries.

Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.

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. 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.

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 highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include: - The Role of Deep Learning in Healthcare Industry: Limitations - Generative Adversarial Networks for Deep Learning in Healthcare - The Role of Blockchain in the Healthcare Sector - Brain Tumor Detection Based on Different Deep Neural Networks Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening. Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.

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, Alzheimer’s 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.

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimodal Affective Computing offers readers a concise overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches in applied affective computing systems and social signal processing. It covers affective facial expression and recognition, affective body expression and recognition, affective speech processing, affective text, and dialogue processing, recognizing affect using physiological measures, computational models of emotion and theoretical foundations, and affective sound and music processing. This book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.The book is an informative resource for academicians, professionals, researchers, and students at engineering and medical institutions working in the areas of applied affective computing, sentiment analysis, and emotion recognition.

Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications, including fraud detection and prevention, self-driving cars, recommendation systems, facial recognition technology, and intelligent computing. This book helps beginners learn the art and science of machine learning. It presens real-world examples that leverage the popular Python machine learning ecosystem, The topics covered in this book include machine learning basics: supervised and unsupervised learning, linear regression and logistic regression, Support Vector Machines (SVMs). It also delves into special topics such as neural networks, theory of generalisation, and bias and fairness in machine learning. After reading this book, computer science and engineering students - at college and university levels - will receive a complete understanding of machine learning fundamentals and will be able to implement neural network solutions in information systems, and also extend them to their advantage.

This book is a quick review of machine learning methods for engineering applications. It provides an introduction to the principles of machine learning and common algorithms in the first section. Proceeding chapters summarize and analyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying machine learning methods to software engineering tasks. Finally, it gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general. Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book. Key Features: - Describes real-world problems that can be solved using machine learning - Explains methods for directly applying machine learning techniques to concrete real-world problems - Explains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). - It does not require prior knowledge of the machine learning. This book is meant to be an introduction to artificial intelligence (AI), machine earning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a background in advanced mathematics.