Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec. Finally, several cutting-edge deep learning topics are discussed, including dropout regularization, attention, explainability, and adversarial attacks. Most of the examples in the book are drawn from the field of information security, with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming, and elementary computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/.

Helps readers to transition from traditional statistics to modern data science Reviews the pros and cons of open source and commercial software packages, and their proper applications in specific situations. Explores data using dynamic methods rather than counting on dichotomous thinking. Considers alternate models using ensemble models and model comparison rather than fixing a preconceived hypothesis/model on a single method. Shows how to find the hidden pattern in the data by dynamic visualization rather than over-relying on numeric results.

Why do minds exist? How did mud and stone develop into beings that can experience longing, regret, love and compassion-beings that are aware of their own experience? Until recently, science offered few answers to these existential questions. Journey of the Mind is the first book to offer a unified account of the mind that explains how consciousness, language, the Self and civilisation emerged incrementally out of chaos. The journey begins three billion years ago with the emergence of the simplest possible mind, a nanoscopic archeon, then ascends through amoebas, worms, frogs, birds, monkeys and AI, examining successively smarter ways of thinking. The authors explain the mathematical principles generating conscious experience and show how these principles led cities and democratic nations to develop new forms of consciousness-the self-aware "superminds". Journey of the Mind concludes by contemplating a higher stage of consciousness already emerging-and the ultimate fate of all minds in the universe.

1. Understand the audit culture, challenges, and benefits of the CAE role in digitally transforming business environment in smart cities 2. Identify ways to advance the value of Internal Audit in digital era 3. Use and control the resources of the city efficiently, and to ensure that the system units work properly in an integrated way.

Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provides far-reaching insights into the recent techniques forming the backbone of smart environments, and addresses the vulnerabilities that give rise to the challenges in real-word implementation. The book focuses on the benefits related to the emerging applications such as machine learning, blockchain and cyber security. Key Features: Introduces the latest trends in the fields of machine learning, blockchain and cyber security Discusses the fundamentals, challenges and architectural overviews with concepts Explores recent advancements in machine learning, blockchain, and cyber security Examines recent trends in emerging technologies This book is primarily aimed at graduates, researchers, and professionals working in the areas of machine learning, blockchain, and cyber security.

The primary goal of this book is to address the issues faced by teachers in the adoption of digital tools into their teaching and their students learning. This book also addresses the issues confronting educators in the integration of digital technologies into their teaching and their students' learning. Such issues include a skepticism of the added value of technology to educational learning outcomes, the perception of the requirement to keep up with the fast pace of technological innovation, a lack of knowledge of affordable educational digital tools and a lack of understanding of pedagogical strategies to embrace digital technologies in their teaching. This book presents theoretical perspectives of learning and teaching today's digital students with technology and proposes a pragmatic and sustainable framework for teachers' professional learning to embed digital technologies into their repertoire of teaching strategies in a systematic, coherent and comfortable manner so that technology integration becomes an almost effortless pedagogy in their day-to-day teaching. Some of the objectives are given below: Shares valuable insights into the influence of technology on teaching and learning in higher education Provides deeper insights on higher education and sustainability interact Studies innovations from various perspectives Investigates how the educators and students apply the unique innovative and emotional dimensions in modern age of learning Provides a timely overview of changes in education reforms and policy research globally Evaluates the problematic relationship between globalization, the state, and education reforms.

- Introduce Decision Support Systems (DSS) with artificial intelligence for the Industry 4.0 Environments - Provide the essentials of recent applications of Machine Learning and Probabilistic Graphical Models for DSS - Consider the process uncertainty when developing the DSS helps these studies closer to reality - Provide general concepts for extracting knowledge from big data effectively and interpret decisions for DSS - Introduce real-world case studies in various fields like Engineering, Management, Healthcare with guidance and recommendations for the practical applications of these studies

This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-making system and management, and so forth Describes applicability of fuzzy optimization techniques in areas of science and management Resolves several issues based on uncertainty using member function Helps map different problems based on mathematical models Includes issues and problems based on linear and nonlinear optimizations Focuses on management science such as manpower management and inventory planning This book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.

Developments in deep learning in the past decade have led to phenomenal growth in AI-based automated medical diagnosis, opening a door to a new era of both medical research and medical industry. It is a golden age for researchers involved in the development and application of advanced machine learning techniques for medical and clinical problems. This book captures the most recent important advances in this cross-disciplinary topic and brings the latest advances to a wide audience including experts, researchers, students, industry developers and medical services.

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)-the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments. This book offers comprehensive coverage of the most essential topics, including: Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

Reviews the literature of the Moth-Flame Optimization algorithm; Provides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithm; Proposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems; Demonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithm; Introduces several applications areas of the Moth-Flame Optimization algorithm focusing in sustainability.

- Offers a comprehensive technological path from basic theories to categorization of existing algorithms - Covers state-of-the-art Auto Encoder, Generative Networks, Synthetic data, Self-Driving cars and cognitive AI-based decision makings. - Includes practical evaluations with python on GAN and using synthetic data - Provides an overview of the trends, and applications to provide you with ML landscape

Provides an overview of the role engineering plays in climate change and environmental pollution Presents an updated overview of Green Engineering focusing on green technology Innovations Explores energy management strategies Discusses green communication technologies, green computing technologies, green smart buildings, green smart lighting, green smart mobility management, fuel-efficient transportation, paperless offices, energy efficiency measures, waste recycling, etc. Identifies the development of sustainable plans and programs at the urban level within the current legislative framework

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Introduction to Quantum Natural Language Processing. Overview of Leadership and AI. The Age of Quantum Superiority. Challenges To Today's Leadership. AI-induced Strategic Implementation and Organizational Performance.

A systematic presentation of the randomized machine learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields. Provides new numerical methods for random global optimization and computation of multidimensional integrals. A universal algorithm for randomized machine learning.

A pandemic does not only bring health concerns for society but also significantly affects individuals and government and business operations. Recently, COVID-19 has substantially hampered conventional businesses and organizations worldwide. Digital technology can help achieve business continuity and overcome challenges caused by pandemic situations. Digital innovation is the application of digital technology to existing business problems. Ideas such as digital transformation and digitization are closely related to digital innovation. In this pandemic period, many businesses recognize that they need to transform, innovate, and adopt new technologies to stay competitive. However, digital transformation is an inherently complex process, and the time pressure to adopt quickly may result in further complexities for organizations in fostering digital technologies. Digital Innovations for Pandemics: Concepts, Challenges, Constraints, and Opportunities presents the potential of digital responses to the COVID-19 pandemic. It explores new digital concepts for learning and teaching, provides an overview of organizational responses to the crisis through digital technologies, and examines digital solutions developed to manage the crisis. Examining how information systems researchers can contribute to these global efforts, this book seeks to showcase how consumers, citizens, entrepreneurs, organizations, institutions, and governments are leveraging new and emerging digital innovations to disrupt and transform value creation in the pandemic era. It captures the breadth of digital innovations carried out to handle the pandemic and looks at the use of digital technologies to strengthen various processes. The book features the following: Solutions on how digital technologies enable responses to a global crisis An analysis of information systems used during the management of the COVID-19 pandemic New concepts for digital business and innovative content models for different sectors This book is written for advanced undergraduate students, postgraduate students, researchers, and scholars in the field of digital business, education, and healthcare. It includes theoretical chapters and case studies from leading scholars and practitioners on the technology-adoption practices of non-government organizations (NGOs), government, and business.

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master's students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications. This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.

All the topics that a professional AI programmer needs are covered within Each topic is accompanied by an example game, where the reader simply adds their own code and see the effects their changes have Covers complex topics but in an easy to read manner and with conversational delivery

* The book offers a well-balanced mathematical analysis of modelling physical systems. * Summarizes basic principles in differential geometry and convex analysis as needed. * The book covers a wide range of industrial and social applications, and bridges the gap between core theory and costly experiments through simulations and modelling. * The focus of the book is manifold ranging from stability of fluid flows, nano fluids, drug delivery, and security of image data to Pandemic modeling etc.

Highlights the importance and applications of Swarm Intelligence and Machine learning in Healthcare industry. Elaborates Swarm Intelligence and Machine Learning for Cancer Detection. Focuses on applying Swarm Intelligence and Machine Learning for Heart Disease detection and diagnosis. Explores of the concepts of machine learning along with swarm intelligence techniques, along with recent research developments in healthcare sectors. Investigates how healthcare companies can leverage the tapestry of big data to discover new business values. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms.

Nanetti outlines a methodology for deploying artificial intelligence and machine learning to enhance historical research. Historical events are the treasure of human experiences, the heritage that societies have used to remain resilient and express their identities. Nanetti has created and developed an interdisciplinary methodology supported by practice-based research that serves as a pathway between historical and computer sciences to design and build computational structures that analyse how societies create narratives about historical events. This consilience pathway aims to make historical memory machine-understandable. It turns history into a computational discipline through an interdisciplinary blend of philological accuracy, historical scholarship, history-based media projects, and computational tools. Nanetti presents the theory behind this methodology from a humanities perspective and discusses its practical application in user interface and experience. An essential read for historians and scholars working in the digital humanities.

Conceptually, as well as practically, digitalization is similar to the implementation of a modern computation model - the model may be a centralized setup using a mainframe or it may be extended to an N-tier architecture. Regardless of the specifics of the implementation, however, the conceptual model of data processing remains the same. Digitalization is nothing but a system relying on digital technologies to create, conduct and, potentially, expand a business activity of some sort. Digitalization can be used to create an e-commerce model for a small business or to create a global supply and distribution chain geared toward almost any kind of a business. It could also be used for non-profit purposes, such as on-line education and telemedicine or e-government. Digitalization: Contexts, Roles, and Outcomes is a contemplation and analysis of the socio-technical system that is known as digitalization. It considers the context of digitalization as well as the ways by which digitalization offers value to the context within which it operates. This book aims to offer readers an entry point to a path of inquiry into the different aspects of digitalization. The goal is to identify main directions for further inquiry as well as to outline the most obvious obstacles along the way. The book aims to guide readers on their own unique journeys using the basic ideas, principles, and concepts synthesized, developed, and presented in the book. It is beneficial to both practitioners and researchers. The book covers: The functionality of digitalization The significance of digitalization Identifying the context of digitalization Designing a control system A cognitive model for the theory of digitalization Designing a theory of digitalization The book helps readers to consider the subject of digitalization in a rigorous and rational way so their own perspectives can emerge stronger and be substantiated and reinforced by building an argument vis-a-vis perspectives and points examined in this book.

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection. With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in the sea. The second is an untethered biomimetic robotic fish with a camera stabilizer, its control methods based on visual tracking. The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Additionally, the mainstream technologies covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics. Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from this work.