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.

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.

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 reference is a compilation of work from different technology groups that give an insight into strategies used for technology optimization and technical R&D. Each of the 14 chapters presented in the book are contributed by engineering experts and focus on different aspects of smart technologies. The chapters cover a wide range of technical disciplines with a list of references for further reading. The compilation demonstrates methods that are useful to apprentices and researchers involved in the development of technologies in different fields: · Wireless networking · Signal processing · Control and machine engineering · SOC design · Materials science and nanotechnology · Biomedical engineering · Power electronics The contributions in this book provide interesting examples for product development such as custom nanomaterials, digital electronics, smart devices and antennas. The content also serves as a reference for designing special components used for complex systems like wireless communication systems, automated control systems and organic waste processing systems. The content is structured in a format suitable for both learners and advanced researchers, making this reference essential to engineers at all levels.

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.

This book presents research focused on the design of fractal antennas using bio-inspired computing techniques. The authors present designs for fractal antennas having desirable features like size reduction characteristics, enhanced gain, and improved bandwidths. The research is summarized in six chapters which highlight the important issues related to fractal antenna design and the mentioned computing techniques. Chapters demonstrate several applied concepts and techniques used in the process such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The work aims to provide cost-effective and efficient solutions to the demand for compact antennas due to the increasing demand for reduced sizes of components in modern wireless communication devices. A key feature of the book includes an extensive literature survey to understand the concept of fractal antennas, their features, and design approaches. Another key feature is the systematic approach to antenna design. The book explains how the IE3D software is used to simulate various fractal antennas, and how the results can be used to select a design. This is followed by ANN model development and testing for optimization, and an exploration of ANN ensemble models for the design of fractal antennas. The bio-inspired computing techniques based on GA, PSO, and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the given computing techniques is also explained to demonstrate how to select the best algorithm for a given bio-inspired design. Finally, the book explains how to evaluate antenna designs. This book is a valuable resource for students (from UG to PG levels) and research scholars undertaking learning modules or projects on microstrip and patch antenna design in communications or electronics engineering courses.

Increasingly global and online social interactions and financial transactions involve digital data, computing devices and the internet. With cloud computing, remote computing, enterprise mobility and e-commerce on the rise, network security has become a priority. Selecting an appropriate algorithm and policy is a challenge for computer security engineers, as new technologies provide malicious users with opportunities to intrude into computer networks. New Age Cyber Threat Mitigation for Cloud Computing Networks provides cloud and network engineers answers to cybersecurity challenges. It highlights new options, methodologies and feasible solutions that can be implemented in cloud architecture and IT Infrastructure, thereby securing end users. Chapters cover many topics related to cyber threats in the modern era. These topics include: · Ransomware and DDoS attacks · Security algorithms · Design and implementation solutions for resilient and fault-tolerant cloud and network services · Security policy · End user data security The book is an essential resource for anyone involved in cloud computing and network security, including learners, professionals and enthusiasts.

Futuristic Projects in Energy and Automation Sectors is a review of analyses on energy transitions in power grids and the opportunities and challenges for building sustainable energy systems to improve human capabilities. 14 chapters examine renewable energy-based and automated systems, with a focus on projects that are designed with sustainability in mind. Topics covered in this review include 1) power systems, 2) renewable energy, 3) power electronics, 4) energy storage and conversion, 5) home automation, 6) control systems, 7) robotics, 8) artificial intelligence, and 9) technology to fight COVID-19. This review will be of interest to scholars, and policymakers interested in futuristic and urban and rural energy planning, sustainable and renewable energy projects, sustainable development, and environment management.

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care. Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science. Key topics covered in the book: - An introduction to the concept of the Internet of Medical Things (IoMT). - Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data. - A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs). - Artificial Intelligence and the Internet of Medical Things. - A review of new machine learning and AI solutions in different medical areas. - A Deep Learning based solution to optimize obstacle recognition for visually impaired patients. - A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications. - Deep Learning for brain tumor detection. - Blockchain and patient data management.