Applications in industry and technology Books

Book SynopsisMeghan Casey owns Do Better Content Consulting. She helps a wide variety of clientsfrom startups, nonprofits, colleges and universities, Fortune 50 companies, and everything in betweensolve the messy content problems most organizations encounter every day. Meghan has also helped several agencies and clients build their capacity to do content strategy. Perhaps her proudest moments are when content strategy practitioners tell Meghan that the first edition of this book helped them launch their content strategy career, tackle a difficult content project, or get a promotion. A regular trainer and speaker on content strategy topics, she once inspired participants to spontaneously do the wave in a workshop setting. Yep, that really happened. Meghan has been working with content and communications since 1996, after receiving her Bachelor of Arts degree in writing from Concordia College. She also holds a Master of Arts in nonprofit management from Hamline University.Table of ContentsPART I: GET BUDGET AND BUY-IN 1 IDENTIFY PROBLEMS AND OPPORTUNITIES 2 CONVINCE LEADERS AND GET THE RESOURCES PART II: SET UP FOR SUCCESS 3 GET STAKEHOLDERS ON BOARD 4 ASSEMBLE YOUR CROSS‑DISCIPLINE TEAM 5 PREPARE FOR CHANGE PART III: DIG IN AND GET THE DIRT 6 UNDERSTAND YOUR BUSINESS ENVIRONMENT 7 LEARN ABOUT YOUR AUDIENCE AND USERS 8 GET FAMILIAR WITH YOUR CONTENT 9 EVALUATE YOUR PROCESSES PART IV: ARTICULATE YOUR STRATEGY 10 ALIGN ON A STRATEGIC FOUNDATION 11 SET YOUR CONTENT COMPASS PART V: DESIGN YOUR CONTENT 12 PRIORITIZE BASED ON YOUR STRATEGY 13 ORGANIZE FOR INTUITIVE WAYFINDING 14 DEFINE THE CONTENT EXPERIENCE 15 SPECIFY CONTENT STRUCTURE AND REQUIREMENTS PART VI: IMPLEMENT AND EVOLVE 16 DEFINE HOW YOU'LL GOVERN YOUR CONTENT 17 BUILD OUT YOUR CONTENT PLAYBOOK

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Book SynopsisUnlocking the Metaverse Highly comprehensive resource providing insight into how the Metaverse, and digital worlds in general, can be leveraged for business success Unlocking the Metaverse focuses on the strategic implementation of processes and the execution of Metaverse strategies, technologies, and innovations and provides readers with real world tools and strategies to succeed with market demands. The text provides a clear and concise description of what the Metaverse is and what its value means to readers and their companies. A continuous interaction with readers inside the book's virtual world in the Metaverse provides both structured and unstructured interactions with the highly qualified author and his guests in periodic and ongoing public events, serving as a repository of continuous learning and a sandbox for continuous innovations to be explored, analyzed, and reported. Unlocking the Metaverse covers sample topics such as: ConTable of ContentsAbout the Author ix Acknowledgments xi List of Acronyms xiii Introduction: How to Use This Book xv Chapter 1: Definitions 1 Metaverse 1 Digital Twin 4 Virtual Worlds 6 Blockchain 7 Fungible Token (FT) 8 Non-fungible Token (NFT) 8 Smart Contracts 9 Tokenomics 11 GPT 11 Chapter 2: Digital Twins, Virtual Worlds, and the Metaverse 14 Digital Twins 14 Gaming 20 Monetization 22 Virtual Worlds 23 Reality Capture and Motion Capture 24 Chase Olson – Reality Capture 25 Avatars 30 Avatar Interface 31 Avatars as Metahumans/Humanoids (MoCap) 32 NPCs as Reference Oracles 32 Virtual Worlds in the Metaverse Examples 34 Cybersecurity and Safety 37 Metaverse 38 Industrial Metaverse 41 Chapter 3: Metaverse Mechanisms and Solutions 44 Blockchain 45 Workflows 46 Capital Asset Delivery Using Smart Contracts Workflow 46 Construction Documents as the Digital DNA of the Built Environment 47 Ethereum blockchain 47 Digital Twin 48 Geo Location and Workflow 48 Facility Management 49 Challenges 49 Governance in a Decentralized Digital Environment 51 Cybersecurity 51 Trust 52 Data 53 Avatars 53 Smart Contracts 53 Value Propositions 53 Increased Efficiency 54 Improved Data Collection and Analysis 54 Accurate and Trusted Facility Data and Information 54 Tokenomics 55 Woven Collisions: NFTs and the Metaverse 56 LOE 58 Real-Estate-Backed Digital Asset Securities 59 Web 3 60 AI 61 GPT 63 ChatGPT Model 65 Chapter 4: The Crystal Ball 67 Scarcity and Abundance 69 Edge Computing 70 Censorship 72 Thought Leader Interviews 73 Damon Hernandez – The Metaverse 73 Hugh Seaton – Data 86 Cody Nowak – Process 97 Arol Wolford – The Industry's Future 104 Conclusion 109 Index 113

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This heavily revised open access edition provides a thorough overview of the technologies available to assemble, manage and assess the quality of health information systems. It details a variety of scenarios in the context of both health and heath care, including where prevention and wellness are related, such as the treatment of both acute and chronic diseases. Stakeholder requirements are also described to provide perspectives for describing the architectures and management techniques associated with health information systems, enabling the reader to develop a detailed holistic overview of the subject. Health Information Systems: Technological and Management Perspectives features a detailed overview of how information systems in health care can be managed and is a vital resource for medical informatics students seeking an up-to-date text on the topic.

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Book SynopsisThis book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.Table of Contents1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model..- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic.- 3. Automation of COVID-19 Disease Diagnosis from Radiograph.- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals.- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine.- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION .- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review.- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases.- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section.- 10. Transformation in Health Sector during Pandemic by Photonics Devices .- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES.- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions.- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition.- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects.- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID’19 AND FUTURE PANDEMICS.- 16. “Role of digital healthcare in rehabilitation during pandemic”.- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES.- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.

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Book SynopsisThis heavily revised open access edition provides a thorough overview of the technologies available to assemble, manage and assess the quality of health information systems. It details a variety of scenarios in the context of both health and heath care, including where prevention and wellness are related, such as the treatment of both acute and chronic diseases. Stakeholder requirements are also described to provide perspectives for describing the architectures and management techniques associated with health information systems, enabling the reader to develop a detailed holistic overview of the subject. Health Information Systems: Technological and Management Perspectives features a detailed overview of how information systems in health care can be managed and is a vital resource for medical informatics students seeking an up-to-date text on the topic.Table of ContentsIntroduction.- Motivation And Objective Of The Book.- Life Situations.- Stakeholders’ Requirements.- Example.- Summary.- Basic Concepts And Terms.- Introduction.- Data, Information And Knowledge.- Systems And Subsystems.- Information Systems.- Health Information Systems.- Information Logistics In Health Information Systems.- Functions And Processes Of Health Care Settings.- Information Processing Tools Of Health Information Systems.- Electronic Health Records As A Part Of Health Information Systems.- Architecture And Infrastructure Of Health Information Systems.- Management Of Health Information Systems.- Modeling Information Systems.- Using The Snik Ontology Together With This Book.- Examples.- Exercises.- Summary.- Technological Perspective: Architectures, Integration And Standards.- Introduction.- Layers Of Architectures.- Integrity And Integration.- Standards For Interoperability.- Specific Health Information Systems.- Examples.- Exercises.- Summary.- Management Perspective: Tasks, Scope And Governance.- Introduction.- Strategic, Tactical And Operational Management Of Information Systems.- Tasks And Methods Of Strategic Management Of Information Systems.- It Service Management.- Data Governance.- It Governance.- Managing Specific Health Information Systems.- Examples.- Exercises.- Summary.- Quintessence: Quality.- Fulfillment Of Stakeholders’ Requirements.- Evaluation.- Summary.- Thesaurus.- Literature Cited.

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Book SynopsisThis book is a beginner's guide to AutomationML Edition 2, written for students, engineers, lecturers, developers and those interested. In guides through the basics of AutomationML Edition 2, CAEX and the AutomationML Editor. AutomationML stands for digitisation of engineering data and engineering workflows. AutomationML achieves both human readability and machine-readability. It is a method for converting data into digital information, and it supports the special needs of iterative engineering data exchange. AutomationML is in the hot spot of the digitisation of automation engineering data. It enables the modelling and transport of engineering data in a vendor neutral and machine-readable models, a valuable source of digital innovation. Machine readable engineering data makes the data accessible and interpretable by software, enabling a plethora of opportunities. This book carefully introduces AutomationML, its goals, values and innovations. It teaches the architecture of AutomationML and explains the language elements with a multitude of examples and step-by-step instructions. Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/

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Book SynopsisThe debate over eHealth is alive as never before. Supporters suggest that it will result in dramatic innovations in healthcare, including a giant leap towards patient-centered care, new opportunities to improve effectiveness, and enhanced wellness and quality of life. In addition, the growing market value of investments in health IT suggests that eHealth can offer at least a partial cure for the current economic stagnation. Detractors counter these arguments by claiming that eHealth has already failed: the UK Department of Health has shut down the NHS National Program for IT, Google has discontinued its Health flagship, and doubts have arisen over privacy safeguards for both patients and medical professionals. This book briefly explains why caregivers, professionals, technicians, patients, politicians, and others should all consider themselves stakeholders in eHealth. It offers myth-busting responses to some ill-considered arguments from both sides of the trench, in the process allowing a fresh look at eHealth. In addition, it describes how the technical failures of previous eHealth systems can be avoided, examines the legal basis of eHealth, and discusses associated ethical issues.​ Table of Contents1 Introduction: The debate over eHealth.- 2 Definitions of eHealth.- 3 An introduction to the technological basis of eHealth.- 4 eHealth and me: The implications of the Net for health care relationships.- 5 Legally eHealth.- 6 EU support to eHealth and cost-benefits.- 7 No (e)Health without (e)Research.- 8 eHealth policy.- 9 The high-tech face of eHealth.- 10 The data-driven revolution of healthcare.- 11 eEducation and eHealth: a call for action.- 12 Conclusions.

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Book SynopsisThis publication is sponsored by the American Association for Medical Systems and Informatics. The Board of AAMSI and the Board of the Society for Computer Medicine, one of AAMSI''s predecessors, agreed that a book on application of medical systems and informatics for the practitioner would help promote high quality health care and they charged the Committee on Standards of the Society for Computer Medicine to write such a text. It is intended as a guide for the field of medical systems and informatics with emphasis on standards, terminology, and coding systems. The text, a result of three years of research and effort, has been reviewed by the Board of Directors of AAMSI and approved by the Publications Committee. We believe that you will find it valuable and hope to revise it from time to time to meet current needs. On behalf of the members of the Association, we congratulate thTable of ContentsI Introduction to Accounting Systems.- 1 Beyond Billing: Some Things You Should Know Before You Begin.- 2 What Sort of Data Should Be Collected and Why?.- 3 The Accounting Module.- II Introduction to Administrative Systems.- 4 The Administration Module.- III Introduction to Health Care Delivery Systems.- 5 How Computers Can Help in Patient Care and Practice (Health Care Delivery).- 6 How Physician Professional Education and Development Can Be Enhanced by Computers.- 7 The Medical Record Summary, Contents, and Utilization.- 8 History Gathering Techniques Via the Computer.- 9 Supervising and Keeping Track of Patient Care.- 10 Quality Assessment and Quality Control of Patient Care.- 11 Patient/Parent/Family Educational Assists.- IV Introduction to Planning, Vendors, and Implementation.- 12 Planning for Automation: The Total Office Practice System.- 13 Be Prepared to Give, Not Receive, the Sales Pitch.- 14 Problems with System Implementation.- 15 Office Computing and the Right to Privacy.- 16 Summary.- Appendices.

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Book SynopsisThis textbook provides a detailed resource introducing the subdiscipline of mental health informatics. It systematically reviews the methods, paradigms, tools and knowledge base in both clinical and bioinformatics and across the spectrum from research to clinical care. Key foundational technologies, such as terminologies, ontologies and data exchange standards are presented and given context within the complex landscape of mental health conditions, research and care. The learning health system model is utilized to emphasize the bi-directional nature of the translational science associated with mental health processes. Descriptions of the data, technologies, paradigms and products that are generated by and used in each process and their limitations are discussed. Mental Health Informatics: Enabling a Learning Mental Healthcare System is a comprehensive introductory resource for students, educators and researchers in mental health informatics and related behavioral sciences. It is an ideal resource for use in a survey course for both pre- and post-doctoral training programs, as well as for healthcare administrators, funding entities, vendors and product developers working to make mental healthcare more evidence-based. Table of ContentsPrecision Medicine and the Learning Health System.- What is Informatics?.- What is Mental Health?.- Epistemological Differences between the Behavioral and Biological Sciences.- What is Mental Health Informatics?.- Big Picture: Process View of Mental Health Care Delivery.- Introduction to Technologies for Data Acquisition in Mental Health.- Informatics Technologies for the Acquisition of Biological Data.- Informatics Technologies for the Acquisition of Psychological and Behavioral Data.- Informatics Technologies for the Acquisition of Interpersonal, Social and Environmental Data: The Exosome.- Informatics Technologies for the Acquisition of Population Level Data.- Introduction to Methods for Extracting Meaningful Information from Data in Mental Health.- Concept and Knowledge Representation to Transform Data into Information in Mental Health.- Bioinformatics Methods in Mental Health Research and Practice.- Psychometric Methods.- Computational Models and Analytic Methods.- Natural Language Processing in Mental Health Research and Practice.- Introduction to Paradigms for Knowledge Discovery in Mental Health.- Paradigms for Knowledge Discovery in Clinical and Research Data Repositories.- Biomarker Discovery.- Data Visualization.- Inferential Analysis and Predictive Modeling.- The Role of Biological Information in Mental Health Research and Practice.- Electronic Health Record (EHR) Systems in Mental Health.- Computerized Clinical Decision Support in Mental Health.- Informatics Technologies in Detection and Diagnosis of Mental Health Conditions.- Informatics Technologies in Prevention and Treatment of Mental Health Conditions.- Informatics Technologies for Care Coordination and Continuity of Care.- Informatics Technologies to Improve Patient Safety in Mental Health.- Registries.- Disseminating Newly Acquired Knowledge.- Ethical Legal and Social Issues (ELSI).- The Future of Mental Health Informatics.

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Book SynopsisThis book provides an overview of the innovative concepts, methodologies and frameworks that will increase the feasibility of the existing telemedicine system. With the arrival of advanced technologies, telehealth has become a new subject, requiring a different understanding of IT devices and of their use, to fulfill health needs. Different topics are discussed - from the basics of TeleMedicine, to help readers understand the technology from ground up, to details about the infrastructure and communication technologies to offer deeper insights into the technology. The use of IoT and cloud services along with the use of blockchain technology in TeleMedicine are also discussed. Detailed information about the use of machine learning and computer vision techniques for the proper transmission of medical data - keeping in mind the bandwidth of the network - are provided. The book will be a readily accessible source of information for professionals working in the area of information technology as well as for the all those involved in the healthcare environment.Table of Contentssee attachment

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Book SynopsisComputer software has been productive in helping individuals with cognitive disabilities. Personalizing the user interface is an important strategy in designing software for these users, because of the barriers created by conventional user interfaces for the cognitively disabled. Cognitive assistive technology (CAT) has typically been used to provide help with everyday activities, outside of cognitive rehabilitation therapy. This book describes a quarter century of computing R&D at the Institute for Cognitive Prosthetics, focusing on the needs of individuals with cognitive disabilities from brain injury. Models and methods from Human Computer Interaction (HCI) have been particularly valuable, initially in illuminating those needs. Subsequently HCI methods have expanded CAT to be powerful rehabilitation therapy tools, restoring some damaged cognitive abilities which have resisted conventional therapy. Patient-Centered Design (PCD) emerged as a design methodology which incorporates both clinical and technical factors. PCD also takes advantage of the patient's ability to redesign and refine the user interface, and to achieve a very good fit between user and system. Cognitive Prosthetics Telerehabilitation is a powerful therapy modality. Essential characteristics are delivering service to patients in their own home, having the patient's priority activities be the focus of therapy, using cognitive prosthetic software which applies Patient Centered Design, and videoconferencing with a workspace shared between therapist and patient. Cognitive Prosthetics Telerehabilitation has a rich set of advantages for the many stakeholders involved with brain injury rehabilitation.Table of ContentsIntroduction.- Some Clinical Features of the Cognitive Disabilities Domain with TBI Examples.- Adapting Computer Software to Address Cognitive Disabilities.- The Primacy of the User Interface.- Patient-Centered Design.- Cognitive Prosthetics Telerehabilitation.- The Active User and the Engaged User.- Patient Case Studies in the Use of Cognitive Assistive Technology: Successes and Failures.- Conclusions, Factors Influencing Outcomes, Anomalies, and Opportunities.- Bibliography.

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Book SynopsisIn line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on “Artificial Intelligence and Machine Learning for Healthcare”. The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society. Table of ContentsArtificial Intelligence for the future of medicine.- A Survival Analysis Guide in Oncology.- Social Media Sentiment Analysis related to COVID-19 Vaccinations.- Healthcare support using data mining: A case study on stroke prediction.

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Book SynopsisMachine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.Table of ContentsMachine Learning for Health Informatics.- Bagging Soft Decision Trees.- Grammars for Discrete Dynamics.- Empowering Bridging Term Discovery for Cross-domain Literature Mining in the TextFlows Platform.- Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice.- Deep learning trends for focal brain pathology segmentation in MRI.- Differentiation between Normal and Epileptic EEG using K-Nearest-Neighbors Technique.- Survey on Feature Extraction and Applications of Biosignals.- Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning.- Machine Learning and Data mining Methods for Managing Parkinson’s Disease.- Challenges of Medical Text and Image Processing: Machine Learning Approaches.- Visual Intelligent Decision Support Systems in the medical field: design and evaluation.

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Book SynopsisComputational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in Computational Drug Discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.Table of ContentsVolume 1 Preface xv Acknowledgments xix About the Editors xxi Part I Molecular Dynamics and Related Methods in Drug Discovery 1 1 Binding Free Energy Calculations in Drug Discovery 3Anitade Ruiter and Chris Oostenbrink 2 Gaussian Accelerated Molecular Dynamics in Drug Discovery 21Hung N. Do, Jinan Wang, Keya Joshi, Kushal Koirala, and Yinglong Miao 3 MD Simulations for Drug-Target(Un)binding Kinetics 45Steffen Wolf 4 Solvation Thermodynamics and its Applications in Drug Discovery 65Kuzhanthaivelan Saravanan and Ramesh K. Sistla 5 Site-Identification by Ligand Competitive Saturation as a Paradigm of Co-solvent MD Methods 83Asuka A. Orr and Alexander D. MacKerell Jr. Part II Quantum Mechanics Application for Drug Discovery 119 6 QM/MM for Structure-Based Drug Design: Techniques and Applications 121Marc W. van der Kamp and Jaida Begum 7 Recent Advances in Practical Quantum Mechanics and Mixed-QM/MM-Driven X-Ray Crystallography and Cryogenic Electron Microscopy (Cryo-EM) and Their Impact on Structure-Based Drug Discovery 157Oleg Borbulevych and Lance M. Westerhoff 8 Quantum-Chemical Analyses of Interactions for Biochemical Applications 183Dmitri G. Fedorov Part III Artificial Intelligence in Pre-clinical Drug Discovery 211 9 The Role of Computer-Aided Drug Design in Drug Discovery 213Stormvander Voort, Andreas Bender, and Bart A. Westerman 10 AI-Based Protein Structure Predictions and Their Implications in Drug Discovery 227Tahsin F. Kellici, Dimitar Hristozov, and Inaki Morao 11 Deep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule Ligands 255Venkatesh Mysore, Nilkanth Patel, and Adegoke Ojewole 12 Using Artificial Intelligence for de novo Drug Design and Retrosynthesis 275Rohit Arora, Nicolas Brosse, Clarisse Descamps, Nicolas Devaux, Nicolas Do Huu, Philippe Gendreau, Yann Gaston-Mathé, Maud Parrot, Quentin Perron, and Hamza Tajmouati 13 Reliability and Applicability Assessment for Machine Learning Models 299Fabio Urbina and Sean Ekins Volume 2 Preface xv Acknowledgments xix About the Editors xxi Part IV Chemical Space and Knowledge-Based Drug Discovery 315 14 Enumerable Libraries and Accessible Chemical Space in Drug Discovery 317Tim Knehans, Nicholas A. Boyles, and Pieter H. Bos 15 Navigating Chemical Space 337Akos Tarcsay, András Volford, Jonathan Buttrick, Jan-Constantin Christopherson, Máte Erdos, and Zoltán B. Szabó 16 Visualization, Exploration, and Screening of Chemical Space in Drug Discovery 365José J. Naveja, Fernanda I. Saldívar-González, Diana L. Prado-Romero, Angel J.Ruiz-Moreno, Marco Velasco-Velázquez, Ramón Alain Miranda-Quintana, and José L. Medina-Franco 17 SAR Knowledge Bases for Driving Drug Discovery 395Nishanth Kandepedu, Anil Kumar Manchala, and Norman Azoulay 18 Cambridge Structural Database (CSD)–Drug Discovery Through Data Mining & Knowledge-Based Tools 419Francesca Stanzione, Rupesh Chikhale, and Laura Friggeri Part V Structure-Based Virtual Screening Using Docking 441 19 Structure-Based Ultra-Large Virtual Screenings 443Christoph Gorgulla 20 Community Benchmarking Exercises for Docking and Scoring 471Bharti Devi, Anurag TK Baidya, and Rajnish Kumar PartVI In Silico ADMET Modeling 495 21 Advances in the Application of In Silico ADMET Models–An Industry Perspective 497Wenyi Wang, Fjodor Melnikov, Joe Napoli, and Prashant Desai Part VII Computational Approaches for New Therapeutic Modalities 537 22 Modeling the Structures of Ternary Complexes Mediated by Molecular Glues 539Michael L. Drummond 23 Free Energy Calculations in Covalent Drug Design 561Levente M. Mihalovits, György G. Ferenczy, and György M. Keseru Part VIII Computing Technologies Driving Drug Discovery 579 24 Orion A Cloud-Native Molecular Design Platform 581Jesper Sorensen, Caitlin C. Bannan, Gaetano Calabrò, Varsha Jain, Grigory Ovanesyan, Addison Smith, She Zhang, Christopher I. Bayly, Tom A. Darden, Matthew T. Geballe, David N. LeBard, Mark McGann, Joseph B. Moon, Hari S. Muddana, Andrew Shewmaker, Jharrod LaFon, Robert W. Tolbert, A. Geoffrey Skillman, and Anthony Nicholls 25 Cloud-Native Rendering Platform and GPUs Aid Drug Discovery 617Mark Ross, Michael Drummond, Lance Westerhoff, Xavier Barbeu, Essam Metwally, Sasha Banks-Louie, Kevin Jorissen, Anup Ojah, and Ruzhu Chen 26 The Quantum Computing Paradigm 627Thomas Ehmer, Gopal Karemore, and Hans Melo Index 679

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Book SynopsisTable of ContentsGetting Started 1 Getting Acquainted 2 Creating Graphics and Text 3 Animating Symbols 4 Advanced Motion Tweening 5 Character Animation with Tweens 6 Character Animation with Bones 7 Animating the Camera 8 Animating Shapes and Using Masks 9 Creating Interactive Navigation 10 Working with Sound and Video 11 Publishing Index

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Book SynopsisKelly Kordes Anton has written and edited dozens of books and training resources on publishing technologies and InDesign, including nine previous editions of Adobe InDesign Classroom in a Book. She is a freelance writer based in Littleton, Colorado, and she also writes about ergonomics and lean manufacturing. Tina DeJarld has worked on the front lines of taking designs from the computer screen to real-world production since before InDesign 1.0. She is highly accomplished in both prepress and graphic design production and passionate about building files that will work correctly. Tina has handled thousands of large and complex projects, becoming an expert on InDesign best practices and techniques. Tina is currently a senior production artist at thePub, a production studio in Chicago.Table of ContentsGetting Started 1 Introducing the Workspace 2 Getting to Know InDesign 3 Setting Up a Document and Working with Pages 4 Working with Objects 5 Working with Color 6 Flowing Text 7 Editing Text 8 Working with Typography 9 Working with Styles 10 Creating Tables 11 Importing and Modifying Graphics 12 Working with Transparency 13 Printing and Exporting 14 Creating Adobe PDF Files with Form Fields 15 Creating a Fixed-Layout ePub and Publishing Online

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Book SynopsisMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisStatistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.Trade ReviewFrom the reviews:"Overall, the book provides an excellent reviewof DTRs up to date. After finishing reading the book, I planned to create a post-graduate seminar course on this topic using this book as a textbook. I enthusiastically recommend this book. This book will be a valuable reference for anyone interested and involved in research on personalized medicine." (Hyonggin An, Journal of Agricultural, Biological, and Environmental Statistics, April, 2015)“The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students, as well as master’s and doctoral students in the field of biostatistics and epidemiology and computer scientists. … This book provides a concise summary of the key findings in the statistical literature of dynamic treatment regimes. … The simple language and well-organized chapters are unsurpassed attributes of this book. It will be an exceptional resource for quick review.” (Parthiv Amin, Doody’s Book Reviews, November, 2013)Table of ContentsIntroduction.- The Data: Observational Studies and Sequentially Randomized Trials.- Statistical Reinforcement Learning.- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes.- Estimation of Optimal DTRs by Directly Modeling Regimes.- G-computation: Parametric Estimation of Optimal DTRs.- Estimation DTRs for Alternative Outcome Types.- Inference and Non-regularity.- Additional Considerations and Final Thoughts.- Glossary.- Index.- References.

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Book Synopsis- Practical in its scope and coverage, the authors have provided a tool-kit for the medical professional in the often complex field of medical informatics - All editors are from the Geisinger Health System, which has one of the largest Electron Health systmes in the USA, and is high in the list of the AMIA "100 Most Wire" healthcare systems - Describes the latest successes and pitfalls Table of ContentsIntroduction.- Preparation.- Organizational Climate.- Needs Assessment.- Vendor Selection and Contract Negotiation.- Infrastructure.- Workflow Assessment and Redesign.- Staffing and Managing Implementation Teams.- Support.- Usability.- Training.- Clinical Decision Support.- Translating Scope of Practice into Effective EHR Workflows.- System Integration.- Production Support.- Managing the Client-Vendor Partnership.- Implementation.- Phased Implementation.- Optimizing Primary-Care Practices.- Optimizing Specialty Practices.- Special-Purpose Software.- Optimizing Inpatient Care.- Extending EHR Access to Patients.- Extending EHR Access to External Physicians.- Summary and Prospect.

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Book SynopsisThis book provides a practically applicable guide to designing evidence-based medical apps and mHealth interventions. It features detailed guidance and case studies where applicable on the best practices and available techniques from both technological (platform technologies, toolkits, sensors) and research perspectives. This approach enables the reader to develop a deep understanding of how to collect the appropriate data and work with users to build a user friendly app for their target audience. Information on how researchers and designers can communicate their intentions with a variety of stakeholders including medical practitioners, developers and researchers to ensure the best possible decisions are made during the development process to produce an app of optimal quality that also considers usability. Developing Medical Apps and mHealth Interventions comprehensively covers the development of medical and health apps for researchers, informaticians and physicians, and is a valuable resource for the experienced professional and trainee seeking a text on how to develop user friendly medical apps.Table of ContentsIntroduction to mHealth.- Project development methodologies, management and data modelling.- Designing an mHealth intervention.- Application development and testing.- Data collection, storage and security.- Feeding back information to patients and users with visualisations.- Usability testing and deployment.- Designing an mHealth evaluation.- Data analysis methods.

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Book SynopsisArtificial intelligence advancements, machine intelligence innovations, and semantic web developments together make up semantic intelligence technologies. The edited book integrates artifi cial intelligence, machine learning, IoT, blockchain, and natural language processing with semantic web technologies. This book also aims to offer real-life solutions to the pressing issues currently being faced by semantic web technologies.

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Book SynopsisTable of Contents1. Einleitung und Fragestellung.- 2. Allgemeines.- 2.1 Typische Beispiele aus der Literatur.- 2.1.1 University Group Diabetes Program (UGDP)-Studie.- 2.1.2 Coronary Drug Project (CDP) — Studie.- 2.1.3 Clofibrat Studie.- 2.2 Generelle Probleme.- 2.2.1 Inhalt von Zwischenauswertungen.- 2.2.2 Gründe für einen vorzeitigen Studienabbruch.- 2.2.3 Allgemeine methodische statistische Aspekte bei Zwischenauswertungen und Studienabbruch.- 3. Verfahrensübersicht.- 3.1 Offene Sequentialpläne.- 3.2 Geschlossene Sequentialpläne.- 3.3 Gruppensequentielle Pläne.- 3.4 Testverfahren für Lebensdauerdaten.- 3.5 Multivariate Methoden und multiple Vergleiche.- 3.6 Sonstige Verfahren.- 4. Gemischte Strategien.- 4.1 Formale Ableitung.- 4.2 Praktische Berechnung.- 4.3 Ergebnisse.- 4.4 Vergleiche mit den bisherigen Verfahren.- 5. Verfahrensvergleiche bei Lebensdauerdaten durch Simulation.- 5.1 Simulationsmodell.- 5.2 Simulation und Ergebnisse.- 6. Schlußfolgerungen.- 7. Zusammenfassung.- 8. Literatur.- 9. Anhang.

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Book SynopsisValuation is a hot topic among life sciences professionals. There is no clear understanding on how to use the different valuation approaches and how to determine input parameters. Some do not value at all, arguing that it is not possible to get realistic and objective numbers out of it. Some claim it to be an art. In the following chapters we will provide the user with a concise val- tion manual, providing transparency and practical insight for all dealing with valuation in life sciences: project and portfolio managers, licensing executives, business developers, technology transfer managers, entrep- neurs, investors, and analysts. The purpose of the book is to explain how to apply discounted cash flow and real options valuation to life sciences p- jects, i.e. to license contracts, patents, and firms. We explain the fun- mentals and the pitfalls with case studies so that the reader is capable of performing the valuations on his own and repeat the theory in the exercises and case studies. The book is structured in five parts: In the first part, the introduction, we discuss the role of the players in the life sciences industry and their p- ticular interests. We describe why valuation is important to them, where they need it, and the current problems to it. The second part deals with the input parameters required for valuation in life sciences, i.e. success rates, costs, peak sales, and timelines.Trade ReviewFrom the reviews:"‘Valuation in Life Sciences: A Practical Guide’ leads readers step by step through the theory of life sciences valuation. … it is an important read for biotech investors. … It is the first book of its kind that combines industry data and valuation theory together with practical advice. It includes valuation techniques and tools that allow investors to pick undervalued biotech stocks." (GEN – genetic engineering news, February, 2007)"Valuation in Life Sciences: A Practical Guide opens up the black box and describes, step by step, a relatively simple procedure for quantitative valuation in life sciences. The book guides the reader through the recommended procedure of quantitative valuation … . This book is recommended to those who would like to acquire a profound understanding of quantitative valuation and use a simple spreadsheet approach of their own cases." (Rudolf Gygax, Nature Biotechnology, Vol. 25 (9), 2007)"This book provides some interesting examples – for business development and licensing executives – of where ROV may be used. … For the general reader and business development executive there is a good explanation of ROV and its applications in life science … . The book may also be of interest to biotechnology company business development executives and university technology transfer officers … . this book is essential reading as it not only provides the mathematical theory but also the applications in different types of deals." (Roger Davies, Business Development & Licensing Journal, Issue 4, 2007)From the reviews of the third edition:“The content and discussion contained in ‘Valuation in Life Sciences’ is excellent. The Swiss authors provide a practical guide to what mathematical tools gives meaningful valuations … . Recommended for anyone planning to be involved in the development of a biotech venture.” (C. Mathews, Amazon, December, 2010)“The authors always keep the necessary rigour to make the book a valuable reference to professionals such as business developers, investment bankers, or analysts. … it an excellent textbook for students as well. … ‘Valuation in Life Sciences’ addresses also the financial beginner with guided and easy-to-understand examples in Excel. I highly recommend the book to everyone in financial biotech.” (Amazon, November, 2009)“This book is an excellent overview of business development valuation and great for those who want to catch up on some techniques and tools. It gives an accurate picture of BD in pharma. For those in the business … it remains a very valuable tool.” (J. Serres, Amazon, March, 2009)Table of ContentsBasics of Valuation.- Valuation in Life Sciences.- Exercises.- Case Studies.

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Book SynopsisTable of ContentsDraft format and toc 1.The History of the CT Scan: How Computer-Assisted Tomography Changed the Landscape of Surgical Planning. We will review the advent of the CT scan, the evolution of CT technology, the different types of CT, and how the advances in technology led to the ability to model and plan surgeries. 2. DiY Stereolithography and 3D Printing The reader will learn about the history of stereolithography (3D models), the indications for stereolithography (when to use it), and given a "how to do it yourself" course enabling them to make their own 3D printed models in preparation of surgery (video). 3. Virtual Surgical Planning in Orthognathic Surgery: A Practical Workflow The reader will be given practical steps on how to plan for an orthognathic case, starting with a CT scan, appropriate models, and planning goals. 4. Virtual Surgical Planning in Head and Neck Reconstruction: A Practical Workflow The reader will be given practical steps on how to plan for a head and neck oncologic case, starting with a CT scan, appropriate models, and planning goals. 5. Virtual Surgical Planning in Cranial Surgery and Craniosynostosis: A Practical Workflow The reader will be given practical steps on how to plan for a cranial surgery (skull) case, starting with a CT scan, appropriate models, and planning goals. 6. ABC in Surgical Navigation The reader will learn the techniques of using navigation and computer-assisted guidance in the operating room with informative videos from illustrative cases. 7. Virtual Reality in Craniofacial Surgery The reader will get a primer on the use of virtual reality and augmented reality in surgical planning and in the operating room. 8. Robots in Craniofacial Surgery The reader will be given a futuristic view of the use of robots in craniofacial surgical cases

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Book SynopsisFor however smart your Roomba or Alexa might seem, historically, robots have been fairly dumb. They are only able to do their jobs when given a narrow set of tasks, confined in a controlled environment, and overseen by a human operator. But things are changing. A new breed of robots is in development that will operate largely on their own. They'll drive on roads and sidewalks, ferry deliveries within buildings, stock shelves in stores, and coordinate teams of doctors and nurses. These autonomous systems will find their way into busy, often unpredictable public spaces. They could be truly collaborative, augmenting human work by attending to the parts of tasks we don't do as well, without our having to stop and direct them. But consider, for a moment, the sorcerer's apprentice. The broom he set to work was also supposed to be collaborative, too, and should have made his life much easier. But the broom didn't know how to behave, and the apprentice no longer understood the thing he had made. The challenge of this next generation of robots is that, like the apprentice's broom, they will wreak complete havoc, inadvertently hurting or even killing people, unless we can recognize a simple truth: collaborative robots will be the first truly social creatures that technology has created. They will need to know how to behave in unfamiliar spaces and around untrained users and bystanders.Robot experts Julie Shah and Laura Major are among those engineers leading the development of collaborative robots, and in this book, they will offer their vision for how to make it in the new era of human-robot collaboration. They set out the blueprint for what they call working robots, which in many ways resemble service animals, and take readers through the many fascinating and surprising challenges that both engineers and the public will need to address in figuring out these machines can be responsibly integrated into society: what they will have to look like, how they will have to talk to strangers and what robot etiquette will be, whether we will have to "robot-proof" public spaces and infrastructure, and how the safety-critical work of human-robot collaboration will force a sea change in how the tech industry is regulated. Today, we still gawk at a car that drives by without a driver. Tomorrow, you might find yourself driving next to five of them. We can debate whether the singularity will ever come, but robots need not be superintelligent in order to revolutionize our relationship to technology. Read this book to find out how.

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Book SynopsisDo interventions improve health outcomes? This volume provides a model and road map to answer clinical questions related to intervention effectiveness research, quality improvement, and program evaluations. It offers clear and simple guidance for all phases of a clinical inquiry projects from planning through dissemination and communication of results and findings. The book emphasizes the value and importance of leveraging existing data to advance research, practice, and quality improvement efforts. Intervention and Effectiveness Research is a practical guide for organizing and navigating the intersections of research and practice. Structure, process and outcome worksheets for every step are provided together with examples from diverse settings and populations to lead readers through the process of implementing their own projects. The author guides readers through the process of designing, implementing, and evaluating projects. This book is intended for teachers of DNP and PhD programs in nursing and other disciplines, their students, and healthcare leaders who need to leverage data to demonstrate care quality and outcomes.Trade Review“This is a unique book, which can be used by students and novices. It can be useful for DNP students as they develop their end-of-program projects and for nurses as they begin to work on quality improvement and program evaluation projects. There is no comparable book in the field. This would be a great addition to the library of teachers and managers to share with their students and nurses.” (Michalene A. King, Doody's Book Reviews, November, 2017)Table of ContentsBackground: Why we need this bookChapter 1. Intervention Effectiveness Research History and State of the ScienceChapter 2. Project/Program Evaluation History and State of the Science 2.1 Worksheet to compare research and evaluation for a projectChapter 3. Methods for Interventions Effectiveness Research and Program EvaluationChapter 4. Existing Data: EHR data and administrative dataChapter 5. New Data: Observational Tools and SurveysChapter 6. Interviews: Professionals and Consumers6.1 Worksheet to plan methods for a projectChapter 7. Instruments: Baseline and follow up assessmentsChapter 8. Instruments: Intervention descriptionsChapter 9. Instruments: Problem classification9.1 Worksheet to describe baseline and final assessments 9.2 Worksheet to describe interventionsChapter 10. Ethical reviewChapter 11. ImplementationChapter 12. AnalysisChapter 13. Publication

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Book SynopsisEurope faces a challenge: how to apply information and communication technologies to health care. One problem is the widening gap between the expectations of citizens and the limited resources available to provide health services. It is here that advanced technology can serve as an important tool to find innovative and more efficient ways of delivering health services. This book reports the summary of a study performed under contract by a team of consultants for Directorate-General XIII of the Commission of the European Communities. It analyses the key factors governing the evolution of advanced information systems for health care and medicine in Europe and provides guidelines for placing current and future work within the framework of the Community research and development programmes.Table of ContentsVolume I: Executive Summary.- 1 Challenges and Opportunities.- 2 Status and Trends.- 3 Shaping Forces.- 4 Recommendations.- Volume II: Main Report.- 1 Background.- 2 Goals of a Future Action.- 3 Proposed Framework for Analysis and Definition of Actions.- 4 Guidelines for the Identification of Needed Actions.- 5 Identification of Major IHE Requirements and Priority Tasks.- Volume III: Issue Analyses.- 1 Alphanumeric Data and Text Coding Standards.- 2 Images and Biosignals, with Coding Standards.- 3 Medical Instrumentation and Devices.- 4 Knowledge Based and Decision Support Systems.- 5 Multimedia Workstations.- 6 Communication Networks and Archiving Systems.- 7 Modularity and Integration of Medical and Health Information Systems.- 8 Regulatory Tools and Incentives.- References.

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Book SynopsisThe book collects the contributions to the NATO Advanced Study Institute on "Speech Recognition and Understanding: Recent Advances, Trends and Applications", held in Cetraro, Italy, during the first two weeks of July 1990. This Institute focused on three topics that are considered of particular interest and rich of i'p.novation by researchers in the fields of speech recognition and understanding: Advances in Hidden Markov modeling, connectionist approaches to speech and language modeling, and linguistic processing including language and dialogue modeling. The purpose of any ASI is that of encouraging scientific communications between researchers of NATO countries through advanced tutorials and presentations: excellent tutorials were offered by invited speakers that present in this book 15 papers which sum­ marize or detail the topics covered in their lectures. The lectures were complemented by discussions, panel sections and by the presentation of related works carried on by some of the attending researchers: these presentations have been collected in 42 short contributions to the Proceedings. This volume, that the reader can find useful for an overview, although incomplete, of the state of the art in speech understanding, is divided into 6 Parts.Table of Contents1 Recent Results on Hidden Markov Models.- Invited papers.- Hidden Markov Models for Speech Recognition — Strengths and Limitations.- Hidden Markov Models and Speaker Adaptation.- A 20,000 word Automatic Speech Recognizer. Adaptation to French of the US TANGORA System.- Automatic Adjustments of the Markov Models Topology for Speech Recognition Applications over the Telephone.- Phonetic Structure Inference of Phonemic HMM.- Phonetic Units and Phonotactical Structure Inference by Ergodic Hidden Markov Models.- Clustering of Gaussian Densities in Hidden Markov Models.- Developments in High-Performance Connected Digit Recognition.- Robust Speaker-Independent Hidden Markov Model Based Word Spotter.- Robust Speech Recognition in Noisy and Reverberant Environments.- An ISDN Speech Server based on Speaker Independent Continuous Hidden Markov Models.- RAMSES: A Spanish Demisyllable Based Continuous Speech Recognition System.- Speaker Independent, 1000 Words Speech Recognition in Spanish.- Continuously Variable Transition Probability HMM for Speech Recognition.- 2 Continuous Speech Recognition Systems.- Invited papers.- Context-Dependent Phonetic Hidden Markov Models for Speaker-Independent Continuous Speech Recognition (Abstract).- Speaker-Independent Continuous Speech Recognition Using Continuous Density Hidden Markov Models.- Contributed papers.- A Fast Lexical Selection Strategy for Large Vocabulary Continuous Speech Recognition.- Performance of a Speaker-Independent Continuous Speech Recognizer.- Automatic Transformation of Speech Databases for Continuous Speech Recognition.- Iterative Optimization of the Data Driven Analysis in Continuous Speech.- Syllable-based Stochastic Models for Continuous Speech Recognition.- Word Hypothesization in Continuous Speech Recognition.- Phone Recognition Using High Order Phonotactic Constraints.- An Efficient Structure for Continuous Speech Recognition.- Search Organization for Large Vocabulary Continuous Speech Recognition.- 3 Connectionist Models of Speech.- Invited papers.- Neural Networks or Hidden Markov Models for Automatic Speech Recognition: Is there a Choice?.- Neural Networks for Continuous Speech Recognition.- Connectionist Large Vocabulary Speech Recognition.- The Cortical Column as a Model for Speech Recognition: Principles and First Experiments.- Contributed papers.- Radial Basis Functions for Speech Recognition.- Phonetic Features Extraction Using Time-Delay Neural Networks.- Improved Broad Phonetic Classification and Segmentation with an Auditory Model.- Automatic Learning of a Production Rule System for Acoustic-Phonetic Decoding.- 4 Stochastic Models for Language and Dialogue.- Invited papers.- Stochastic Grammars and Pattern Recognition.- Basic Methods of Probabilistic Context Free Grammars.- A Probabilistic Approach to Person-Robot Dialogue.- Contributed papers.- Experimenting Text Creation by Natural-Language, Large-Vocabulary Speech Recognition.- DUALGRAM: An Efficient Method for Representing Limited-Domain Language Models.- Strategies for Speech Recognition and Understanding Using Layered Protocols.- 5 Understanding and Dialogue Systems.- Invited papers.- TINA: A Probabilistic Syntactic Parser for Speech Understanding Systems.- The Voyager Speech Understanding System: A Progress Report.- The Interaction of Word Recognition and Linguistic Processing in Speech Understanding.- Linguistic Processing in a Speech Understanding System.- Contributed papers.- Linguistic Tools for Speech Recognition and Understanding.- Evidential Reasoning and the Combination of Knowledge and Statistical Techniques in Syllable Based Speech Recognition.- 6 Speech Analysis, Coding and Segmentation.- Contributed papers.- Data Base Management for Use with Acoustic-Phonetic Speech Data Bases.- BPF Outputs Compared with Formant Frequencies and LPCs for the Recognition of Vowels.- A Codification of Error Signal by Splines Functions.- Specific Distance for Feature Selection in Speech Recognition.- Multiple Template Modeling of Sublexical Units.- Learning Structural Models of Sublexical Units.- On the Use of Negative Samples in the MGGI Methodology and its Application for Difficult Vocabulary Recognition Tasks.- A New Method for Dynamic Time Alignment of Speech Waveforms.- A New Technique for Automatic Segmentation of Continuous Speech.- Segmentation of Speech based upon a Linear Model of the Effects of Coarticulation 549 P.J. D.

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Book SynopsisThis book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams.Trade ReviewEngaging in data science requires diplomacy for maximal impact. Namely, understanding the norms and priorities of data professionals helps you to spot risks and opportunities. As experienced, trusted data science advisors, and by providing valuable examples, Friedman and Swaminathan open a new data-driven world that spans every single industry vertical. -- Armen Kherlopian, CEO and Partner, Covenant Venture CapitalWinning with Data Science is refreshingly practical and clear. It’s also fun and empowering. After reading it, you’ll be more savvy about working with data teams and more valuable to your company. You may even become the envy of your colleagues (and competitors), who will wonder how you got so smart. -- Steven Strogatz, Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics, Cornell University, and author of Infinite PowersFriedman and Swaminathan have taken the complex topic of data science and made it accessible to everyone. Their creative use of characters, situations, and meaningful examples serve to demystify how to think about the field, how to use data science to solve everyday problems, and how to interact with data scientists to ensure successful projects. An excellent read, even for people who (think they) know a little about the field of data science! -- Melvin (Skip) Olson, global head, Integrated Evidence Strategy and Innovation, Novartis Pharma AGWinning with Data Science addresses a critical but often ignored obstacle in data science: the knowledge gap between business stakeholders and technical teams. This book cuts through data science buzzwords and empowers readers with the knowledge to cultivate thriving data cultures. Distinguishing itself from others, this book prioritizes effective communication and collaboration within the data science sphere, facilitating deeper discussions on intricate technical subjects. -- Jeff Chen, former chief data scientist of the U.S. Department of Commerce and coauthor of Data Science for Public PolicyA terrific work. Winning with Data Science expertly takes readers through daily 'data lives,' struggles with business problems, and the data science concepts that can help address them. -- Paul W. Thurman, Columbia University Mailman School of Public Health, and author of MBA Fundamentals: StatisticsFriedman and Swaminathan provide a deep understanding of data science methodologies to managers, striking exactly the right balance of complexity and accessibility. -- Kim Sweeny, Principal Projects Officer, Institute for Sustainable Industries & Liveable Cities, Victoria UniversityIn today's digital age, data is king. And for business leaders, extracting insights and using them to drive informed decisions is more crucial than ever. . . . If [you] want to speak the language of data and harness its potential, Winning with Data Science is a must-read. -- Ken Kuang, entrepreneur, and Founder, Torrey Hills TechnologiesBy the end of the book, you'll feel like a pro in talking about data, even if you're not a tech expert. -- Nirali Mehta, Founder and CEO, PHARMA-STATSWinning with Data Science tackles the complex topic of data science and simplifies it to make it accessible to anyone, enabling a more data-driven culture at your organization. -- David Mathison, CEO, Chief AI Officer Summit, CDO Club, and CDO SummitTable of ContentsAcknowledgmentsIntroduction1. Tools of the Trade2. The Data Science Project3. Data Science Foundations4. Making Decisions with Data5. Clustering, Segmenting, and Cutting Through the Noise6. Building Your First Model7. Tools for Machine Learning8. Pulling It Together9. EthicsConclusionNotesIndex

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Book SynopsisAmazon's despoticautomation and surveillance technologies may well be its downfallTrade Review'This accessible and richly detailed book brings together fascinating interviews with Italian Amazon workers, historical and economic analysis, and thoughtful critique' -- Lisa Nakamura, Lisa Nakamura, Director of the Digital Studies Institute and the Gwendolyn Calvert Baker Collegiate Professor of American Culture at the University of Michigan'Delfanti has done here what more critics of Amazon should - listen carefully to the people whose work makes the corporation function. Those of us fighting for a better future than Amazon's dystopia have much to learn from this book' -- Dania Rajendra, Inaugural Director, Athena Coalition‘Takes us to the heart of Amazon’s empire and masterfully unpacks the intensive labor, hyper-surveillance, and gamification of work that warehouse laborers experience each day’ -- Veena Dubal, Professor of Law, University of California, Hastings College of Law‘Deftly examines the dichotomy between Amazon's public personas and its union-busting, worker-surveilling behavior in fulfillment centers around the world’ -- ‘Engadget’Table of ContentsList of figures A note on methods Acknowledgments 1. Relentless 2. Work hard 3. Have fun 4. Customer obsession 5. Reimagine now 6. Make history Notes Index

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Book SynopsisThis book provides in depth knowledge about critical factors involved in the success of pervasive healthcare. The book first presents critical components and importance of pervasive healthcare. The authors then give insight into the pervasive healthcare information systems and key consideration related to remote patient monitoring and safety. The book provides in-depth discussion about the security issues and protocols for pervasive healthcare. This book explores concepts and techniques behind the successive pervasive healthcare systems by providing in-depth knowledge about patient empowerment, remote patient monitoring, network establishment and protocols for effective pervasive healthcare. The book also provides case studies in the field. It is an ideal resource for researchers, students and healthcare organizations to get insight about the state of the art in pervasive healthcare systems. Provides current research, developments, and applications in pervasive healthcare; Includes technologies such as machine learning, cryptography, fog computing, and big data in the advancement of e-healthcare; Pertinent for researchers, students, practitioners and healthcare decision makers. Table of ContentsIntroduction.- Hybrid Machine Learning Techniques for Reliability and Security of Healthcare IoT Data.- Cryptographic Operations and Approaches for Privacy of IoT Data in healthcare.- Standards and Approaches for use of Blockchain Technology in Pervasive Healthcare.- Prediction of REM sleep behaviour disorder using EEG signal applied EMG 1 EMG 2 channel.- Healthcare Assisted by Fog Computing for Secure Data Transmission in Healthcare IoTs.- Experimental approaches for prediction of breast cancer diseases using clustering concepts.- Incident reporting system for Pervasive Healthcare.- Blockchain Application for Healthcare record management.- Applying Machine Learning Approach for Disease prediction.- Mobile system for remote patient monitoring.- Securing Pervasive Healthcare system.- Remote Patient Monitoring: Benefits, Applications and Issues.- Self-reporting in Pervasive Healthcare.- Role of Big Data Analytics in Pervasive Healthcare.- ML Techniques for remote patient activity monitoring.- Trust Management in Pervasive Healthcare.- Disease Detection using Android App.- Application of computer assisted machine learning based techniques in detecting neurological disorders.- Conclusion.

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Book SynopsisThis book is a proficient guide to understanding artificial intelligence (IoT) and the Internet of Medical Things (IoMT) in healthcare. The book provides a comprehensive study on the applications of AI and IoT in various medical domains. The book shows how the implementation of innovative solutions in healthcare is beneficial, and IoT, together with AI, are strong drivers of the digital transformation regardless of what field the technologies are applied in. Therefore, this book provides a high level of understanding with the emerging technologies on the Internet of Things, wearable devices, and AI in IoMT, which offers the potential to acquire and process a tremendous amount of data from the physical world. Table of ContentsIntroduction.- Insights on Cognitive Neuroscience Based on Deep Neural Networks.- Robot Assisted Treatment for Children with Learning Disabilities.- Machine learning based approaches for detecting COVID-19 using clinical data in an IoMT environment.- COVID-19 Epidemic Analysis using Deep Learning Approaches.- A Cancer Diagnosis System for Detection of Lung Cancers in an IOMT Environment.- An IOMT-Based Diagnosis System for COVID-19.- Prediction of COVID-19 Epidemic Curve of India: A Machine Learning Approach.- Internet of Medical Things (IoMT) services during COVID-19 pandemic: Roles, challenges, and applications.- Tackling Security and Privacy in Internet of Medical Things (IoMT).- Lightweight AI-Based Security Solutions in IoMT.- Conclusion.

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Book SynopsisHeavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms Table of ContentsChallenges of Evaluation in Biomedical Informatics.- Evaluation as a Field.- Determining What to Study.- The Structure of Objectivist Studies.- Measurement Fundamentals.- Developing and Improving Measurement Methods.- The Design of Demonstration Studies.- Analyzing the Results of Demonstration Studies.- Subjectivist Approaches to Evaluation.- Performing Subjectivist Studies in the Qualitative Traditions Responsive to Users.- Economic Aspects of Evaluation.- Proposing and Communicating the Results of Evaluation Studies: Ethical, Legal, and Regulatory Issues.

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Book SynopsisThis essential book details intermediate-level statistical methods and frameworks for the clinician and medical researcher with an elementary grasp of health statistics and focuses on selecting the appropriate statistical method for many scenarios. Detailed evaluation of various methodologies familiarizes readers with the available techniques and equips them with the tools to select the best from a range of options. The inclusion of a hypothetical case study between a clinician and statistician charting the conception of the research idea through to results dissemination enables the reader to understand how to apply the concepts covered into their day-to-day clinical practice.Applied Statistical Considerations for Clinical Researchers focuses on how clinicians can approach statistical issues when confronted with a medical research problem by considering the data structure, how this relates to their study's aims and any potential knock-on effects relating to the evidence required to make correct clinical decisions. It covers the application of intermediate-level techniques in health statistics making it an ideal resource for the clinician seeking an up-to-date resource on the topic.Table of ContentsIntroduction.- Preliminaries.- Design.- Planning.- Data Acquisition.- Data Manipulation Analysis.- Inferencesty.- Dissemination.- A Case Study.- Conclusions.

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Book SynopsisThis book originates from the idea to adapt biomedical engineering and medical informatics to current clinical needs and proposes a paradigm shift in medical engineering, where the limitations of technology should no longer be the starting point of design, but rather the development of biomedical devices, software, and systems should stem from clinical needs and wishes. Gathering chapters written by authoritative researchers, working the interface between medicine and engineering, this book presents successful attempts of conceiving technology based on clinical practice. It reports on new strategies for medical diagnosis, rehabilitation, and eHealth, focusing on solutions to foster better quality of life through technology, with an emphasis on patients’ and clinical needs, and vulnerable populations. All in all, the book offers a reference guide and a source of inspiration for biomedical engineers, clinical scientists, physicians, and computer scientists. Yet, it also includes practical information for personnel using biomedical equipment, as well as timely insights that are expected to help health agencies and software firms in their decision-making processes.Table of ContentsIntroduction.- Medicine Based Engineering and Informatics to Foster Patient Physician Relationship.- Statistical Gait Analysis based on surface electromyography.- Brain-computer interfaces with functional electrical stimulation for motor neurorehabilitation: from research to clinical practice.- Biopotential acquisition systems.- Wearable Bioimpedance Measuring Devices.- Predictive cardiovascular engineering: transforming data into future insights on cardiovascular disease.- Engineering special medical devices for vulnerable groups.- Serious games and virtual reality for rehabilitation and follow up of wheelchaired persons.- Society 5.0 and a Human Centred Health Care.

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Book SynopsisThere are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems. In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.Table of ContentsSection 1: Medical Image Analysis using Artificial Intelligence Use of Deep Learning in Biomedical Imaging Detection of Breast Cancer Masses in Mammogram Images with Watershed Segmentation and Machine Learning Approach Cloud-based Glaucoma Diagnosis in Medical Imaging using Machine Learning Leucocytic Cell Nucleus Identification using Boundary Cell Detection algorithm with Dilation and Erosion based Morphometry Effective Prediction of Autism Using Ensemble Method Section 2: Artificial Intelligence (AI) Classification Models for COVID-19 Pandemic Automatic Classification of COVID-19 infected patients using Convolution Neural Network Models AI-Based Deep Random Forest Ensemble Model for Prediction of COVID-19 and Pneumonia from Chest X-Ray Images Section 3: Use of AI-Enabled IoT in Healthcare Internet of Things and Artificial Intelligence in Biomedical Systems Role of IoT in Healthcare Sector for Monitoring Diabetic Patients Section 4: Applications of Artificial Intelligence in Healthcare Low-Rank Representation based approach for subspace segmentation and clustering of biomedical image patterns Performance Comparison of Imputation Methods for Heart Disease Prediction Ayurnano: A solution towards herbal therapeutics using Artificial Intelligence approach Artificial Intelligence in Biomedical Education The Emergence of Natural Language Processing (NLP) Techniques in Healthcare AI Prospects and Difficulties of Artificial Intelligence (AI) Implementations in Naturopathy

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