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Summary: This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Summary: The book compiles efficient design and test methodologies for the implementation of reversible logic circuits. The methodologies covered in the book are design approaches, test approaches, fault tolerance in reversible circuits and physical implementation techniques. The book also covers the challenges and the reversible logic circuits to meet these challenges stimulated during each stage of work cycle. The novel computing paradigms are being explored to serve as a basis for fast and low power computation.

Summary: "The Empire Strikes Back (1980), the second film in the original Star Wars trilogy, is often cited as the 'best' and most popular Star Wars movie. In her compelling study, Rebecca Harrison draws on previously unpublished archival research to reveal a variety of original and often surprising perspectives on the film, from the cast and crew who worked on its production through to the audiences who watched it in cinemas. Harrison guides readers on a journey that begins with the film's production in 1979 and ends with a discussion about its contemporary status as an object of reverence and nostalgia. She demonstrates how Empire's meaning and significance has continually shifted over the past 40 years not only within the franchise, but also in broader conversations about film authorship, genre, and identity. Offering new insights and original analysis of Empire via its cultural context, production history, textual analysis, exhibition, reception, and post-1980 re-evaluations of the film, the book provides a timely and relevant reassessment of this enduringly popular film"-- Provided by publisher.

Summary: Evolve your traditional intranet platform into a next-generation digital workspace with this comprehensive book. Through in-depth coverage of strategies, methods, and case studies, you will learn how to design and build an employee experience platform (EXP) for improved employee productivity, engagement, and collaboration. In Build a Next-Generation Digital Workplace, author Dr. Shailesh Kumar Shivakumar takes you through the advantages of EXPs and shows you how to successfully implement one in your organization. This book provides extensive coverage of topics such as EXP design, user experience, content strategy, integration, EXP development, collaboration, and EXP governance. Real-world case studies are also presented to explore practical applications. Employee experience platforms play a vital role in engaging, empowering, and retaining the employees of an organization. Next-generation workplaces demand constant innovation and responsiveness, and this book readies you to fulfill that need with an employee experience platform. You will: Understand key design elements of EXP, including the visual design, EXP strategy, EXP transformation themes, information architecture, and navigation design. Gain insights into end-to-end EXP topics needed to successfully design, implement, and maintain next-generation digital workplace platforms. Study methods used in the EXP lifecycle, such as requirements and design, development, governance, and maintenance Execute the main steps involved in digital transformation of legacy intranet platforms to EXP. Discover emerging trends in digital workplace such as gamification, machine-led operations model and maintenance model, employee-centric design (including persona based design and employee journey mapping), cloud transformation, and design transformation. Comprehend proven methods for legacy Intranet modernization, collaboration, solution validation, migration, and more.

Summary: "To understand the dynamic patterns of behaviours and interactions between athletes that characterise successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts. By examining how AI methodologies are utilised in team games, such as football, as well as individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches. Artificial Intelligence in Sport Performance Analysis provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, sport science as well as related subjects such as engineering, computer and data science, and statistics"-- Provided by publisher.

Summary: This contributed volume discusses diverse topics to demystify the rapidly emerging and evolving blockchain technology, the emergence of integrated platforms and hosted third-party tools, and the development of decentralized applications for various business domains. It presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of as well as the hindrances to this technology.

Summary: A biological neural network passes information between receptors and effectors by using electrical impulses. Neural networks are a collection of neurons that allow the body to process various stimuli and respond to them. Signals passed between neurons are either inhibitory or excitatory, and an action potential is initiated when the excitatory signals reach threshold potential. This book is compiled in such a manner, that it will provide in-depth knowledge about the theory and practice of brain theory and neural networks. With state-of-the-art inputs by acclaimed experts of this field, this book targets students and professionals.

Summary: "This book will provide in depth information about handling and managing healthcare data by Machine Learning methods. It will express the long-standing challenges in healthcare informatics and provide rational orientations on how to deal with them"-- Provided by publisher.

Summary: "In this new edition, Brian Carroll explores writing and editing for digital media with information about voice, style, media formats, and content development, combining hands-on exercises with new sections on idea generation, multi-modal storytelling, podcasting, and information credibility. Carroll explains and demonstrates how to effectively write for digital spaces - whether crafting a story for a website, writing for an app, blogging, or using social media to expand the conversation. Each chapter features lessons and exercises through which students can build a solid understanding of the ways that digital communication provides opportunities for dynamic storytelling and multi-directional communication. Updated with contemporary examples and new pedagogy, the fourth edition broadens its scope, helping digital writers and editors in all fields, including public relations, marketing, and social media management. Writing and Editing for Digital Media is an ideal handbook for students from all backgrounds who are looking to develop their writing and editing skills for this ever-evolving industry"-- Provided by publisher.

Summary: "This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"-- Provided by publisher.

Summary: "From terrorism and public health to museums, schools, and homes, FUTUREPROOF asks us to expand our purview of security, turning our attention to how security is lived, felt, and perceived through the violences of everyday life. Editors Asher Ghertner, Daniel Goldstein, and Hudson Fann propose the modality of "security aesthetics" as a way to understand how sensory and affective experiences help regulate bodies, spaces, and states in the service of "futureproofing" society against anticipated risks and perils. Taken broadly as the domain of sense perception, aesthetics provides a framework for considering the body's interactions with the material world and the processes by which these interactions translate into subjective judgments of taste. The contributions to this volume show us how security aesthetics work to fashion differently imagined futures, illuminating the interplay of sense perception and articulated reason in the operations of security apparatuses"-- Provided by publisher.

Summary: This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on ‘scikit-learn,’ but other Python libraries like ‘Gensim’ or ‘PyTorch’ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models.

Summary: "The Routledge Handbook on Historic Urban Landscapes in Asia-Pacific sheds light onto the balancing act of urban heritage management, focusing specifically on the Asia-Pacific regions in which this challenge is imminent and in need of effective solutions. Urban heritage, while being threatened amidst myriad forces of global and ecological change, provides a vital social, cultural, and economic asset for regeneration and sustenance of liveability of inhabited urban areas worldwide. This six-part volume takes a critical look at the concept of Historic Urban Landscapes, the approach that UNESCO promotes to achieve holistic management of urban heritage, through the lens of issues, prospects, and experiences of urban regeneration of the selected geo-cultural context. It further discusses the difficult task that heritage managers encounter in conceptualizing, mapping, curating, and sustaining the plurality, poetics, and politics of urban heritage of the regions in question. The connective thesis that weaves the chapters in this volume together reinforces for readers that the management of urban heritage considers cities as dynamic entities, palimpsests of historical memories, collages of social diversity, territories of contested identities, and sites for sustainable liveability. Throughout this edited collection, chapters argue for recognizing the totality of the eco-cultural urban fabric, embracing change, building social cohesion, and initiating strategic socio-economic progress in the conservation of historic urban landscapes. Containing thirty-six contributions written by leading regional experts, and illustrated with over 200 black and white images and tables, this volume provides a much-needed resource on historic urban landscapes for students, scholars and researchers"-- Provided by publisher.

Summary: "Targeting Chronic Inflammatory Lung Diseases Using Advanced Drug Delivery Systems explores the development of novel therapeutics and diagnostics to improve pulmonary disease management, looking down to the nanoscale level for an efficient system of targeting and managing respiratory disease. The book examines numerous nanoparticle-based drug systems such as nanocrystals, dendrimers, polymeric micelles, protein-based, carbon nanotube, and liposomes that can offer advantages over traditional drug delivery systems. Starting with a brief introduction on different types of nanoparticles in respiratory disease conditions, the book then focuses on current trends in disease pathology that use different in vitro and in vivo models. The comprehensive resource is designed for those new to the field and to specialized scientists and researchers involved in pulmonary research and drug development"-- Provided by publisher.

Summary: "When working with a data set, a machine learning engineer might train a model but find that the results are not as good as they need. To get better results, they can try to improve the model or collect more data, but there is another avenue: feature engineering. The feature engineering process can help improve results by modifying the data's features to better capture the nature of the problem. This process is partly an art and partly a palette of tricks and recipes. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks"-- Provided by publisher.

Summary: This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications. .

Summary: Artificial intelligence (AI) has landed on Earth like an alien spacecraft from a science fiction novel. We now have self-driving cars; computers that can diagnose illnesses, recommend treatments, and predict and solve crimes; smart phones that can route us from point A to point B; gadgets that can understand and respond to spoken commands; and much more. All of this is made possible by machine learning (ML). Although AI and ML can be used to help us answer questions and solve problems, for many of us they create more questions than answers, questions such as “What is intelligence?” “How do machines learn?” “What is a machine learning algorithm, and how does it work?” “Will machines take my job?” “Will they take over the world?” and, for those in business, “What do I need to know about AI and ML to harness its power?” This book tackles those questions and more as it brings you up to speed on the basics of AI, ML, and artificial neural networks. You begin by reading the story of AI that traces its evolution from the early 1950s to the present, revealing many of the core ideas that continue to drive its development. You explore recent technological innovations that have reinvigorated the field by providing the “big data” that makes machine learning so powerful — innovations such as GPS, social media and electronic transactions. And you discover how machines learn through the use of powerful processing combined with machine learning algorithms and artificial neural networks that mimic the function of the human brain. All of these ideas are discussed against a backdrop of potential new business opportunities and technologies, so you can begin to harness the power of AI to achieve and maintain competitive advantage.

Summary: "Transnational Media: Concepts and Cases provides a clear and engaging overview of media communication from a global and a region-based perspective. Rather than focusing on just complex theories and industry-specific analyses, this unique book offers an inclusive, comparative approach to both journalism and entertainment media--introducing readers to the essential concepts, systems, transnational influences, and power dynamics that shape global media flow. Broad coverage of different media forms from Asia, Africa, the Americas, Europe, and Oceania offers country-based and transnational perspectives while highlighting examples of media trends in television, radio, film, journalism, social media, music, and others. Promoting a balanced, multipolar exploration of transnational media, this innovative book discusses topics such as media concentration, the cultural, political, and economic impact of media, and the primary centers of new and traditional media activities. Chapters organized by geographic region offer instructive pedagogical features--including case studies and essays, and illustrations, maps and charts--that strengthen understanding of distinctive and emerging practices in the production, distribution, and consumption of media products.Explores a wide range of global media topics, infrastructures, cultures, and political-economic climates Written in an engaging, relatable, and easy to understand style Covers major aspects of journalism and various forms of entertainment media Organized by regions of the world to reflect a global perspective Includes newly-written case studies by international scholars from each region Designed for undergraduate and graduate courses in comparative media analysis, international media and communication, and related areas of study"-- Provided by publisher.

Summary: This book reviews the literature on hand posture estimation using generative methods, identifying the current gaps, such as sensitivity to hand shapes, sensitivity to a good initial posture, difficult hand posture recovery in cases of loss in tracking, and lack of addressing multiple objectives to maximize accuracy and minimize computational cost. To fill these gaps, it proposes a new 3D hand model that combines the best features of the current 3D hand models in the literature. It also discusses the development of a hand shape optimization technique. To find the global optimum for the single-objective problem formulated, it improves and applies particle swarm optimization (PSO), one of the most highly regarded optimization algorithms and one that is used successfully in both science and industry. After formulating the problem, multi-objective particle swarm optimization (MOPSO) is employed to estimate the Pareto optimal front as the solution for this bi-objective problem. The book also demonstrates the effectiveness of the improved PSO in hand posture recovery in cases of tracking loss. Lastly, the book examines the formulation of hand posture estimation as a bi-objective problem for the first time. The case studies included feature 50 hand postures extracted from five standard datasets, and were used to benchmark the proposed 3D hand model, hand shape optimization, and hand posture recovery.

Summary: "The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"-- Provided by publisher.

Summary: "This groundbreaking collection advances understanding of the concept of media practices by critically interrogating its relevance for the study of citizen and activist media. Media as practice has emerged as a powerful approach to understanding the media's significance in contemporary society. Bringing together contributions from leading scholars in sociology, media and communication, social movement and critical data studies, this book stimulates dialogue across previously separate traditions of research on citizen and activist media practices and stakes out future directions for research in this burgeoning interdisciplinary field. Framed by a foreword by Nick Couldry and a substantial introductory chapter by the editors, contributions to the volume trace the roots and appropriations of the concept of media practice in Latin American communication theory; reflect on the relationship between activist agency and technological affordances; explore the relevance of the media practice approach for the study of media activism, including activism that takes media as its central object of struggle; and demonstrate the significance of the media practice approach for understanding processes of mediatization and datafication. Offering both a comprehensive introduction to scholarship on citizen media and practice and a cutting-edge exploration of a novel theoretical framework, the book is ideal for students and experienced scholars alike"-- Provided by publisher.

Summary: In this book Part I presents first an overview of the ECHORD++ project, with its mission and vision together with a detailed structure of its functionalities and instruments: Experiments, Robotic Innovation Facilities and Public end-user Driven Technology Innovation PDTI. Chapter 1 explains how the project is born, the partners, the different instruments and the new concept of cascade funding projects. This novelty made ECHORD++ a special project along the huge number of research groups and consortia involved in the whole project. So far, it is the European funded project with more research team and partners involved in the robotic field. In Chapter 2, one of the instruments in ECHORD++ is explained in detail: RIF. Robotic innovation facilities are a set of laboratories across Europe funded with the project with the goal of hosting consortia involved in any experiment that have special needs when testing their robotic research. In the chapter the three different and specific RIFs will be described and analyzed. Chapter 3 explains an important instrument in ECHORD++: the Experiments. In this part, a big number of research groups have been involve in short time funded research projects. The chapter explains the management of such Experiments, from the call for participation, the candidates selection, the monitoring, reviews and funding for each of the 36 experiments funded for Echord. Chapter 4 is very special because it presents the innovation of funding public end-user driven technology, in particular, robotic technology. The robotic challenge is the key of such an instruments together with the management of the different consortia that participated competitively in the success of the robotic challenge proposed by a public entity, selected also with a very special and innovative process.

Summary: "This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order. Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future"-- Provided by publisher.

Summary: The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available"--Publisher's description.

Summary: "This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data"-- Provided by publisher.

Summary: Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. You will: Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples.

Summary: "Americans say that reading, watching, or listening to the news is a leading cause of stress. Of course journalists, as watchdogs and public informants, must disseminate information that is inherently negative, but experts argue that the news media's emphasis on the problem has had a negative effect on the public, the press itself, and democracy. At the same time, the past sixty years have seen a rise of journalistic practices that purport to cover the news beyond the typical problem-based narrative. These genres of journalistic reporting are not positive news or fluff reporting: They are rigorous reporting philosophies and practices that share a common goal - reporting beyond the problem-based narrative, thereby exemplifying a commitment to the social responsibility theory of the press, which asserts that journalists have a duty to consider society's best interests. However, there is little academic or professional understanding of these journalistic approaches. As such, this book provides an in-depth examination of socially-responsible news reporting practices, such as constructive journalism, solutions journalism, and peace journalism. Each chapter focuses on one reporting form, defining it and detailing its evolution and status among scholars and practitioners, as well as discussing its known effects and future direction. This edited volume is the first academic book published on these forms of reporting in the United States. It provides a comprehensive resource that explores the theoretical underpinnings of these journalistic genres that grounds these approaches and allows for a coherent line of research to follow as these approaches evolve"-- Provided by publisher.

Summary: "Fintech has emerged as one of the fastest growing sectors in the financial services industry and has radically disrupted traditional banking. However, it has become clear that in order for both to thrive the culture between fintech and incumbent firms will need to change from one of competition to one of collaboration. The Financial Services Guide to Fintech looks at this trend in detail, using case studies of successful partnerships to show how banks and fintech organizations can work together to innovate faster and increase profitability. Written by an experienced fintech advisor and influencer, this book explains the fundamental concepts of this exciting space and the key segments to have emerged, including regtech, robo-advisory, blockchain and personal finance management. It looks at the successes and failures of bank-fintech collaboration, focusing on technologies and start-ups that are highly relevant to banks' product and business areas such as cash management, compliance and tax. With international coverage of key markets in Europe, Africa, Asia, Latin America and the US, The Financial Services Guide to Fintech offers practical guidance, use cases and business models for banks and financial services firms to use when working with fintech companies"-- Provided by publisher.

Summary: "Learn about the everyday uses and benefits of artificial intelligence, as well as the risks and dangers that come with it"-- Provided by publisher.

Summary: "This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"-- Provided by publisher.

Summary: "The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice"-- Provided by publisher.

Summary: Glossaries are the useful reference sources generally referred to by the students, teachers, researches etc. in their day-to-day work. Glossaries can be very useful for helping the students, research scholars etc. to understand words from their use in reading or in class. This book covers the wide range of terms (A–Z) likely to be encountered by students of journalism and mass communication. It is an essential companion to all the students taking courses in Journalism and Mass Communication studies, as well as related subjects such as Communications Studies, Media Studies, Television and Radio Production etc. Assuming little or no prior knowledge of the subject, it covers terminology relating to the journalism and mass communication. Related topic areas are covered where they impact on or offer explanations of journalism, for example, in law and sociology. Effort has been made to incorporate as many terms as possible and cover common terms including Information and communication technology which can be very useful for those working in the area of journalism and mass communication and other related areas. This book consists of alphabetical list of terms which provide simple definitions and some illustrations for easy understanding of the terms. This book is also an indispensable reference tool for researchers, students and professionals in this area. Attempts have been made to describe the various concepts in systematic way and in a very simple and easy language. The main objective of writing this book to provide sufficient number of specialized and technical words and their definitions related to the area of journalism and mass communication. The special feature of this Glossary is that at the end a chronology of events is given in the area of journalism, mass communication, mass media and related areas.

Summary: "Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use."--Page 4 of cover.

Summary: This book focuses on some aspects of public health in the first three decades of the twentieth-century in Bombay Presidency.

Summary: Journalism in Turkey has an ambivalent characteristic. On the one hand, the social demand for genuine journalism has increased, and on the other hand, news has turned into a tool within the polluted political polarization atmosphere. In the age of fake news and post-truth, practices of journalism in Turkey both contain significantly striking examples of how media professionals overcome the barriers and also give some clues about the changing nature of journalism. The book examines the deep crisis mainstream media experience in Turkey. New-born media institutions, alternatives, their start-up strategies, and transformation of journalism field are scrutinized by qualitative and quantitative methods. The book aims to present a current picture of journalism in Turkey by underlining both historical continuities and breaks from the tradition.

Summary: "Where Ideas Go to Die explores the troubled relationship of US journalism and intellect. A defender of common sense, the press is irked at intellect yet often dependent on its critical autonomy. A postwar observation from Richard Hofstadter applies to contemporary journalists: "Men do not rise in the morning, grin at themselves in their mirrors, and say: 'Ah, today I shall torment an intellectual and strangle an idea!'" The book nevertheless documents the prowess of news media in the policing of intellect. Control extends beyond suppression of ideas and ways of thinking to the aggressive rendering of dissent into deviance. The social control of intellect by journalism is accompanied by social control of journalism in newsrooms and in classrooms where norms are cultivated. Anti-intellectualism consequently operates like dark matter in media, a presence inferred by its effects rather than directly observed or acknowledged. When journalists anticipate a punitive public, the reified resentment is no more real than the fiction of omnipotent citizens in democratic theory, yet the audience imagined compels how intellect is rendered in the news as nuisance, deviance, or object of ridicule. Journalism's contribution to the social control of ideas is poignantly democratic: audiences are cast in consequential roles that affirm their wisdom in a closed, self-referential system. The book concludes with a discussion about what intellectual journalism would look like. Interviews with 25 "dangerous professors" demonstrate how alliances in the academic-media nexus can seed intellect in newswork"-- Provided by publisher.

Summary: As soon as the Spectator model spread from England to continental Europe and began to be incorporated in French, Dutch and German translations and adaptions, the respective journalistic networks and negotiations regularly exceeded local, regional, and even national boundaries and took on international dimensions. The contributions of the present volume outline the historical development and the intricate literary, artistic, journalistic and scientific communication and distribution networks of the moral weeklies and periodical essays inspired by the Spectator prototype in Europe and North America. Thus, these periodicals become visible as parts and products of ramified learned and creative negotiations on genres, writing techniques and topics.

Summary: How do journalists know what they know? Who gets to decide what good journalism is and when it's done right? This text contends that, despite thoughtful explorations of the role of publics in journalism, the profession's methodologies and practices still don't adequately address matters of race, gender, intersectionality and settler colonialism. Drawing on their five years of research with journalists in the US and Canada, in a variety of news organizations from startups and freelancers to mainstream media, the authors investigate modern journalism's founding ideals and methods and their relationship to power to examine emerging multiple journalisms.

Summary: Find the various relationships among variables that can be present in big data as well as other data sets. This book also covers information entropy, permutation tests, combinatorics, predictor selections, and eigenvalues to give you a well-rounded view of data mining and algorithms in C++. Furthermore, Data Mining Algorithms in C++ includes classic techniques that are widely available in standard statistical packages, such as maximum likelihood factor analysis and varimax rotation. After reading and using this book, you'll come away with many code samples and routines that can be repurposed into your own data mining tools and algorithms toolbox. This will allow you to integrate these techniques in your various data and analysis projects. You will: Discover useful data mining techniques and algorithms using the C++ programming language Carry out permutation tests Work with the various relationships and screening types for these relationships Master predictor selections Use the DATAMINE program.

Summary: You'll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You'll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you'll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You'll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud.

Summary: Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries About This Book Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications Packed with practical advice and tips to help you get to grips with applied machine learning Who This Book Is For If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary. What You Will Learn Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve Learn how to implement classification, regression, and clustering Develop a sustainable strategy for customer retention by predicting likely churn candidates Build a scalable recommendation engine with Apache Mahout Apply machine learning to fraud, anomaly, and outlier detection Experiment with deep learning concepts, algorithms, and the toolbox for deep learning Write your own activity recognition model for eHealth applications using mobile sensors In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data.

Summary: "This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order. Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future"-- Provided by publisher.

Summary: This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry.

Summary: This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.

Summary: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you'll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learn Understand the key mathematical concepts for building neural network models Discover core multivariable calculus concepts Improve the performance of deep learning models using optimization techniques Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer Understand computational graphs and their importance in DL Explore the backpropagation algorithm to reduce output error Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning

Summary: "This book examines the latest advances and trends in computer vision and machine learning algorithms for various applications"-- Provided by publisher.

Summary: The age of detachment is over. Interactive, interconnected and participatory, journalism today is a constant live-stream of outrage, terror, polarised politics and fake news. With our news landscape dominated by emotionally charged material, Stephen Jukes investigates emotionality’s impact on the practice of journalism and the journalists themselves. Adopting a psychosocial approach and including interviews with top journalists, this book combines theory and practice to explore:the history of emotive journalism social media’s use of emotion to engage audiences emotion's impact on how journalists work how journalists deal with their own emotions when they are covering difficult stories Navigating the complex relationship between Journalism and Emotion through the exploration of key concepts, in-depth interviews with influential journalists and analysis of traumatic news stories, this book is a must-read for academics and social science students alike.

Summary: The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing. .