Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guide CompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers. With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers: All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities 100% coverage of objectives for the NEW CompTIA Data+ exam Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms Ideal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start to beginning or accelerating a career as an in-demand data analyst.

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.

IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety Enables readers to understand a broad area of state-of-the-art research in physical IoT-enabled security IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety describes new techniques in unobtrusive surveillance that enable people to act and communicate freely, while at the same time protecting them from malevolent behavior. It begins by characterizing the latest on surveillance systems deployed at smart campuses, miniatures of smart cities with more demanding frameworks that enable learning, social interaction, and creativity, and by performing a comparative assessment in the area of unobtrusive surveillance systems for smart campuses. A proposed taxonomy for IoT-enabled smart campus unfolds in five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, the book presents the state of the art and then makes a comparative assessment to classify the systems. Finally, the book extracts valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by unobtrusive surveillance systems for smart campuses. IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety includes specific discussion of: Smart campus's prior work taxonomies and classifications, a proposed taxonomy, and an adopted weight scoring model Personal consumer benefits and potential social dilemmas encountered when adopting an unobtrusive surveillance system Systems that focus on smart buildings, public spaces, smart lighting and smart traffic lights, smart labs, and smart campus ambient intelligence A case study of a spatiotemporal authentication unobtrusive surveillance system for smart campus safety and emerging issues for further research directions IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety is an essential resource for computer science and engineering academics, professionals, and every individual who is working and doing research in the area of unobtrusive surveillance systems and physical security to face malevolent behavior in smart campuses.

Industrial Tomography: Systems and Applications, Second Edition thoroughly explores the important techniques of industrial tomography, also discusses image reconstruction, systems, and applications. This book presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries.

Drawn to Life is a two-volume collection of the legendary lectures of long-time Disney animator Walt Stanchfield. For over 20 years, Walt mentored a new generation of animators at the Walt Disney Studios and influenced such talented artists such as Tim Burton, Brad Bird, Glen Keane, and Andreas Deja. His writing and drawings have become must-have lessons for fine artists, film professionals, animators, and students looking for inspiration and essential training in drawing and the art of animation. Written by Walt Stanchfield (1919–2000), who began work for the Walt Disney Studios in the 1950s. His work can be seen in films such as Sleeping Beauty, The Jungle Book, 101 Dalmatians, and Peter Pan. Edited by Disney Legend and Oscar®-nominated producer Don Hahn, whose credits include the classic Beauty and the Beast, The Lion King, and Hunchback of Notre Dame.

Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment.

As technology continues to develop, the healthcare industry must adapt and implement new technologies and services. Recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine must be considered to ensure patients receive the best possible care. Advancements in Bio-Medical Image Processing and Authentication in Telemedicine introduces recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine and discusses the design of high-accuracy decision support systems. Covering key topics such as artificial intelligence, medical imaging, telemedicine, and technology, this premier reference source is ideal for medical professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented -the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing.

Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health. With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

Advances in Computers, Volume 126 presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on VLSI for Super-Computing: Creativity in R+D from Applications and Algorithms to Masks and Chips, Bulk Bitwise Execution Model in Memory: Mechanisms, Implementation, and Evaluation, Embracing the Laws of Physics: Three Reversible Models of Computation, WSNs in Environmental Monitoring: Data Acquisition and Dissemination Aspects, Energy efficient implementation of tensor operations using dataflow paradigm for machine learning, and A Run-Time Job Scheduling Algorithm for Cluster Architectures with DataFlow Accelerators.

Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

AI is going to change your world – but don’t panic.
 
As AI becomes more widespread in the workplace and in society, what impact will it have on your job, your life and the world around you? If AI can take on more and more of the tasks people perform at work, and do them more efficiently, where does that leave human beings?
 
Taking the Anxiety out of AI  explains how to live with AI, how to benefit from it, and how to avoid being replaced by it. The book explores the differences between human intelligence and artificial intelligence, considers what tasks will always be performed better by humans, and sets out possible futures in which humans and AI work together. It provides tools to work out how AI will affect your role, what skills you need to learn, and which mindsets will equip you to thrive in the future. The book concludes with a guide to current AI programs and how to use them.
 
Whether you have experience with AI, or simply want to learn more about it, this book is an invaluable guide for navigating your future.

Smart homes use Internet-connected devices, artificial intelligence, protocols and numerous technologies to enable people to remotely monitor their home, as well as manage various systems within it via the Internet using a smartphone or a computer. A smart home is programmed to act autonomously to improve comfort levels, save energy and potentially ensure safety; the result is a better way of life. Innovative solutions continue to be developed by researchers and engineers and thus smart home technologies are constantly evolving. By the same token, cybercrime is also becoming more prevalent. Indeed, a smart home system is made up of connected devices that cybercriminals can infiltrate to access private information, commit cyber vandalism or infect devices using botnets. This book addresses cyber attacks such as sniffing, port scanning, address spoofing, session hijacking, ransomware and denial of service. It presents, analyzes and discusses the various aspects of cybersecurity as well as solutions proposed by the research community to counter the risks. Cybersecurity in Smart Homes is intended for people who wish to understand the architectures, protocols and different technologies used in smart homes.

Acoustics: Sound Fields, Transducers and Vibration, Second Edition guides readers through the basics of sound fields, the laws governing sound generation, radiation, and propagation, and general terminology. Specific sections cover microphones (electromagnetic, electrostatic, and ribbon), earphones, and horns, loudspeaker enclosures, baffles and transmission lines, miniature applications (e.g. MEMS microphones and micro speakers in tablets and smart phones), sound in enclosures of all sizes, such as school rooms, offices, auditoriums and living rooms, and fluid-structure interaction. Numerical examples and summary charts are given throughout the text to make the material easily applicable to practical design. New to this edition: A chapter on electrostatic loudspeakers A chapter on vibrating surfaces (membranes, plates, and shells) Readers will find this to be a valuable resource for experimenters, acoustical consultants, and to those who anticipate being engineering designers of audio equipment. It will serve as both a text for students in engineering departments and as a valuable reference for practicing engineers.

Cognitive Models for Sustainable Environment reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent/cognitive models and algorithms to develop sustainable solutions for the mitigation of environmental pollution. It unveils intelligent and cognitive models to address issues related to the effective monitoring of environmental pollution and sustainable environmental design. As such, the book focuses on the overall well-being of the global environment for better sustenance and livelihood. The book covers novel cognitive models for effective environmental pollution data management at par with the standards laid down by the World Health Organization. Every chapter is supported by real-life case studies, illustrative examples and video demonstrations that enlighten readers.

Blockchain Technology for Emerging Applications: A Comprehensive Approach explores recent theories and applications of the execution of blockchain technology. Chapters look at a wide range of application areas, including healthcare, digital physical frameworks, web of-things, smart transportation frameworks, interruption identification frameworks, ballot-casting, architecture, smart urban communities, and digital rights administration. The book addresses the engineering, plan objectives, difficulties, constraints, and potential answers for blockchain-based frameworks. It also looks at blockchain-based design perspectives of these intelligent architectures for evaluating and interpreting real-world trends. Chapters expand on different models which have shown considerable success in dealing with an extensive range of applications, including their ability to extract complex hidden features and learn efficient representation in unsupervised environments for blockchain security pattern analysis.

Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics techniques which can apply opinion mining and text analytics on literary works and social media. This book focuses on theories, method and approaches in which data analytic techniques can be used to analyze data from social media, literary books, novels, news, texts, and beyond to provide a meaningful pattern. The subject area of this book is multidisciplinary; related to data science, artificial intelligence, social science and humanities, and literature. This is an essential resource for scholars, Students and lecturers from various fields of data science, artificial intelligence, social science and humanities, and literature, university libraries, new agencies, and many more.

Machine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

Optimum-Path Forest: Theory, Algorithms, and Applications was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions.