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Elsevier

Books in Data general

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Probabilistic Modelling for Advanced Data Analysis

    • 1st Edition
    • Amit Kumar Tyagi + 1 more
    • English
    Probabilistic Modelling for Advanced Data Analysis provides a practical and rigorous guide for data practitioners to effectively implement probabilistic models in real-world scenarios. The book strikes a balance between high-level intuition and technical derivations, offering step-by-step explanations, real-world case studies, and Python implementation examples. The authors offer specific solutions that include modelling and quantifying uncertainty in data-driven decision-making, applying Bayesian inference to real-world problems, and implementing scalable probabilistic models for large-scale datasets, all of which contribute to explainable and trustworthy AI. Probabilistic modeling is a crucial tool in data analysis due to big data, artificial intelligence, and complex decision-making. Traditional statistical methods often fail to capture the inherent uncertainty in real-world datasets. This book presents readers with theoretical foundations and practical applications of probabilistic modeling, providing a structured approach for researchers, data scientists, and industry professionals. The book meets the increasing demand for uncertainty-aware AI models, Bayesian inference, and probabilistic graphical models across various fields of research. The authors have written a comprehensive handbook for probabilistic modelling, incorporating diverse perspectives and real-world case studies from a variety of fields. The book is written with accessibility in mind, benefiting readers from various backgrounds, including those new to the field.

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1st Edition
    • Shashi A. Chiplonkar + 1 more
    • English
    Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1st Edition
    • Pallavi Chavan + 2 more
    • English
    Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

  • Multimodal Learning Using Heterogeneous Data

    • 1st Edition
    • Saeid Eslamian + 3 more
    • English
    Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

  • Mathematical Modeling for Big Data Analytics

    • 1st Edition
    • Passent El-Kafrawy + 1 more
    • English
    Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1st Edition
    • Harris V. Georgiou
    • English
    Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

  • Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

    • 1st Edition
    • Tilottama Goswami + 2 more
    • English
    Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.

  • Applied Graph Data Science

    Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
    • 1st Edition
    • Pethuru Raj + 4 more
    • English
    Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

  • Data Science in the Medical Field

    • 1st Edition
    • Seifedine Kadry + 1 more
    • English
    Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

  • Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

    • 1st Edition
    • Siddhartha Bhattacharyya + 3 more
    • English
    Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

  • Cognitive Science, Computational Intelligence, and Data Analytics

    Methods and Applications with Python
    • 1st Edition
    • Vikas Khare + 2 more
    • English
    Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

  • Smart Spaces

    • 1st Edition
    • Zhihan Lyu
    • English
    Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems.People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks – a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

  • Object-Oriented Analysis and Design for Information Systems

    Modeling with BPMN, OCL, IFML, and Python
    • 2nd Edition
    • Raul Sidnei Wazlawick
    • English
    Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

  • Mathematical Methods in Data Science

    • 1st Edition
    • Jingli Ren + 1 more
    • English
    Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

  • Health Systems Science Education: Development and Implementation

    • 1st Edition
    • Volume 4
    • Rosalyn Maben-Feaster + 5 more
    • English
    **Selected for 2026 Doody's Core Titles in Medical Education**Now taught in a majority of medical schools nationwide, health systems science (HSS) prepares learners for the health systems of the future—an essential topic in modern health care. Health Systems Science Education, part of the American Medical Association’s MedEd Innovation Series, is a first-of-its-kind, instructor-focused field book that that equips educators to not just teach health systems science, but to know how to integrate and implement HSS comprehensively and effectively across the curriculum. This change management-oriented volume.

  • Meeting the Challenges of Data Quality Management

    • 1st Edition
    • Laura Sebastian-Coleman
    • English
    Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

  • Executing Data Quality Projects

    Ten Steps to Quality Data and Trusted Information (TM)
    • 2nd Edition
    • Danette McGilvray
    • English
    Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.

  • Intelligent Data Security Solutions for e-Health Applications

    • 1st Edition
    • Amit Kumar Singh + 1 more
    • English
    E-health applications such as tele-medicine, tele-radiology, tele-ophthalmology, and tele-diagnosis are very promising and have immense potential to improve global healthcare. They can improve access, equity, and quality through the connection of healthcare facilities and healthcare professionals, diminishing geographical and physical barriers. One critical issue, however, is related to the security of data transmission and access to the technologies of medical information. Currently, medical-related identity theft costs billions of dollars each year and altered medical information can put a person’s health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of hand-held devices for storing, accessing, and transmitting medical information is outpacing the privacy and security protections on those devices. Researchers are starting to develop some imperceptible marks to ensure the tamper-proofing, cost effective, and guaranteed originality of the medical records. However, the robustness, security and efficient image archiving and retrieval of medical data information against these cyberattacks is a challenging area for researchers in the field of e-health applications. Intelligent Data Security Solutions for e-Health Applications focuses on cutting-edge academic and industry-related research in this field, with particular emphasis on interdisciplinary approaches and novel techniques to provide security solutions for smart applications. The book provides an overview of cutting-edge security techniques and ideas to help graduate students, researchers, as well as IT professionals who want to understand the opportunities and challenges of using emerging techniques and algorithms for designing and developing more secure systems and methods for e-health applications.

  • Building Big Data Applications

    • 1st Edition
    • Krish Krishnan
    • English
    Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.).

  • Model Management and Analytics for Large Scale Systems

    • 1st Edition
    • Bedir Tekinerdogan + 4 more
    • English
    Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

  • Mosby's Dental Dictionary

    • 4th Edition
    • Elsevier Inc
    • English
    **Selected for Doody’s Core Titles® 2024 with "Essential Purchase" designation in Dictionaries/Termino... essential dental resource that goes beyond education! Mosby's Dental Dictionary, 4th Edition is the must-have, pocket-sized reference covering all areas of dentistry that’s designed for both students and practitioners. This new edition defines over 10,000 terms on dynamic areas of dentistry, including materials, imaging, surgery, orthodontics, pain control, and more.　Throughout the text, over 300 illustrations address new innovations, research, technology, and products in the field, and extensive appendices provide quick access to the information you will use every day. Plus, a free companion website contains more than 5,000 audio pronunciations, 500 additional images, videos, and animations to help illustrate key concepts.

  • Examen clinique et sémiologie - Macleod

    Interrogatoire et examen clinique - Sémiologie par appareil - Situations particulières
    • 1st Edition
    • J. Alastair Innes + 3 more
    • French
    L’examen clinique est essentiel à l’identification d’une maladie et l’évaluation de son pronostic.Cet ouvrage de référence dans le monde médical anglo-saxon explique avec détail comment évaluer les symptômes d’un patient et établir un diagnostic au travers de l’anamnèse et de l’examen physique.Son objectif est d’aider l’étudiant à développer les compétences cliniques nécessaires à l’obtention d’une anamnèse claire et les compétences pratiques indispensables à la détection et l’interprétation des signes cliniques des maladies.Le livre est composé de quatre grandes parties :Anamnèse et examen physique : elle aborde les principes généraux d’une bonne interaction avec le patient, depuis les bases du recueil de l’anamnèse et de la réalisation d’un examen physique à l’utilisation de tableaux cliniques permettant des diagnostics.Sémiolog... par appareil : cette partie traite des symptômes et des signes selon une approche par système.Situations spécifiques : elle illustre l’application de ces compétences à des situations cliniques particulières.Mise en pratique : cette partie s’intéresse à l’application de ces compétences à la pratique quotidienne et propose des conseils pour s’y préparer efficacement.Il est également proposé dans chaque chapitre des exemples d’Examen Clinique Objectif Structuré correspondant à une méthode d’évaluation des étudiants très prisée dans les pays anglo-saxons. Ils consistent en une mise en situation pratique face à un mannequin ou un comédien jouant un scénario clinique court.Ce livre aborde également les différentes étapes de chaque examen qui sont présentées de manière détaillée, étayées de nombreux encadrés et illustrations. Il est particulièrement adapté aux étudiants en DFGSM2-DFGSM3 dans le cadre de l’enseignement thématique « Sémiologie générale » et les enseignements intégrés par système (sémiologie clinique) mais également aux étudiants en pharmacie. Il intéressera également les étudiants en DFASM préparant les ECNi mais aussi les enseignants et praticiens désireux de mettre à jour leurs connaissances.

  • Meta-Analytics

    Consensus Approaches and System Patterns for Data Analysis
    • 1st Edition
    • Steven Simske
    • English
    Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

  • Rodak's Hematology

    Clinical Principles and Applications
    • 6th Edition
    • Elaine M. Keohane + 2 more
    • English
    **Selected for Doody’s Core Titles® 2024 in Laboratory Technology**Make sure you are thoroughly prepared to work in a clinical lab. Rodak’s Hematology: Clinical Principles and Applications, 6th Edition uses hundreds of full-color photomicrographs to help you understand the essentials of hematology. This new edition shows how to accurately identify cells, simplifies hemostasis and thrombosis concepts, and covers normal hematopoiesis through diseases of erythroid, myeloid, lymphoid, and megakaryocytic origins. Easy to follow and understand, this book also covers key topics including: working in a hematology lab; complementary testing areas such as flow cytometry, cytogenetics, and molecular diagnostics; the parts and functions of the cell; and laboratory testing of blood cells and body fluid cells.

  • Data Science

    Concepts and Practice
    • 2nd Edition
    • Vijay Kotu + 1 more
    • English
    Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

  • Principles and Practice of Big Data

    Preparing, Sharing, and Analyzing Complex Information
    • 2nd Edition
    • Jules J. Berman
    • English
    Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.

  • PROP - Module G: Nervous System, Law and Ethics, Psychology and Therapeutic Procedures Elsevier eBook on VitalSource

    • 4th Edition
    • Zenith
    • English

  • PROP - Module A: Integumentary, Sensory Systems, Patient Care and Communication Elsevier eBook on VitalSource

    • 4th Edition
    • Zenith
    • English

  • PROP - Module H: Health Insurance Basics, Claims Processing and Computerized Billing Elsevier eBook on VitalSource

    • 4th Edition
    • Zenith
    • English

  • PROP - Module F: Pediatrics, Geriatrics, Endocrine and Reproductive Systems Elsevier eBook on VitalSource

    • 4th Edition
    • Zenith
    • English

  • PROP - Module B: Skeletal and Muscular System, Infection Control, Minor Office Surgery and Pharmacology Elsevier eBook on VitalSource

    • 4th Edition
    • Zenith
    • English

  • Introduction to Data Compression

    • 5th Edition
    • Khalid Sayood
    • English
    Introduction to Data Compression, Fifth Edition, builds on the success of what is widely considered the best introduction and reference text on the art and science of data compression. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio and video. This new edition includes all the latest developments in the field. Khalid Sayood provides an extensive introduction to the theory underlying today’s compression techniques, with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, the book includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, and scalar and vector quantization. The book provides a comprehensive working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package.

  • Misch's Avoiding Complications in Oral Implantology

    • 1st Edition
    • Carl E. Misch + 1 more
    • English
    See how to effectively manage ALL dental implant complications throughout ALL phases of treatment! Avoiding Complications in Oral Implantology provides evidence-based management protocols for a wide range of implant problems such as placement complications, malpositioning, bleeding, infection, and nerve injuries. Hundreds of high-quality, full-color photos and illustrations clearly demonstrate the complications and their resolution. Edited by Carl Misch and Randolph Resnik — both well-known names in dental implantology and prosthodontics — and with a team of expert contributors, this authoritative guide helps you handle the implant-related complications that can occur as more and more patients choose dental implants.

  • Big Data and Smart Service Systems

    • 1st Edition
    • Xiwei Liu + 4 more
    • English
    Big Data and Smart Service Systems presents the theories and applications regarding Big Data and smart service systems, data acquisition, smart cities, business decision-making support, and smart service design. The rapid development of computer and Internet technologies has led the world to the era of Big Data. Big Data technologies are widely used, which has brought unprecedented impacts on traditional industries and lifestyle. More and more governments, business sectors, and institutions begin to realize data is becoming the most valuable asset and its analysis is becoming the core competitiveness.

  • Big Data

    Principles and Paradigms
    • 1st Edition
    • Rajkumar Buyya + 2 more
    • English
    Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

  • The Art and Science of Analyzing Software Data

    • 1st Edition
    • Christian Bird + 2 more
    • English
    The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.

  • How Nursing has Changed with Technology, An Issue of Nursing Clinics

    • 1st Edition
    • Volume 50-2
    • Maria Overstreet
    • English
    How has technology changed the art and science of nursing practice?Many facets of nursing practice have stayed constant over the years such as the way we advocate for our patients and the art of caring for our patients. However, nursing practice has evolved over the years especially in the bedside delivery of state of the art nursing care. Technology at the bedside has forced nurse educators to change the ways in which we always have taught nursing students. Technology has also begun to change the methods used in the actual bedside nursing care. In this issue, you will hear from some of the nursing experts in areas of nursing care that has changed in either the delivery of care or method of assessing care of the patient. Nursing experts will describe some of the historical changes and intrigue you in the changes expected to come to the bedside. Why is this issue important? First, we gain insight from a review of where we have been and nurses tend to reminisce on our past as well as romance our historical roots. Second, technology is ever changing and it is good practice to keep abreast of what is happening in other areas of nursing so that we can apply others successes in our own specific areas of nursing. Third, informatics in nursing is a growing field and nursing must embrace technology and learn to adapt various methods of delivery so that we can appropriately care for and advocate for our patients.With the changes in our national healthcare system, we must encourage nurses to try out new methods of delivery as well as encourage their ideas of how nursing can change. The articles in this issue reflect these changes.

  • Geriatric Dentistry, An Issue of Dental Clinics of North America

    • 1st Edition
    • Volume 58-4
    • Lisa A. Thompson
    • English
    This issue of Dental Clinics, edited by Lisa A. Thompson and Leonard J. Brennan, focuses on Geriatric Dentistry. Articles include: Our Current Geriatric Population: Demographic and Oral Health Care Utilization; Physiology of Aging of Older Adults: Systemic and Oral Health Considerations; Treatment Planning Considerations in Older Adults; Oral Health Disparity in Older Adults: Dental Decay and Tooth Loss; Oral Health Disparities in Older Adults: Oral bacteria, Inflammation and Aspiration Pneumonia; Salivary Function and Xerostomia and the Implications of Polypharmacy in Elderly; Systemic and Oral Health; Cognitive Impairment in Older Adults and Oral Health considerations: Treatment and Management; Integrating Oral Health into the Interdisciplinary Health Sciences Curriculum; Innovations in Dental Care Delivery for the Older Adult; and more!

  • Data Acquisition Techniques Using PC

    • 1st Edition
    • Howard Austerlitz
    • English
    Data Acquisition Techniques Using Personal Computers contains all the information required by a technical professional (engineer, scientist, technician) to implement a PC-based acquisition system. Including both basic tutorial information as well as some advanced topics, this work is suitable as a reference book for engineers or as a supplemental text for engineering students. It gives the reader enough understanding of the topics to implement a data acquisition system based on commercial products. A reader can alternatively learn how to custom build hardware or write his or her own software. Featuring diverse information, this book will be useful to both the technical professional and the hobbyist.

  • Archives and the computer

    • 2nd Edition
    • Michael J. Cook
    • English
    Archives and the Computer deals with the use of the computer and its systems and programs in archiving data and other related materials. The book covers topics such as the scope of automated systems in archives; systems for records management, archival description, and retrieval; and machine-readable archives. The selection also features examples of archives from different institutions such as the University of Liverpool, Berkshire County Record Office, and the National Maritime Museum.The text is recommended for archivists who would like to know more about the use of computers in archiving of records and other related information.

  • Nursing-Sensitive Indicators, An Issue of Nursing Clinics

    • 1st Edition
    • Volume 49-1
    • Cecilia Anne Kennedy Page
    • English
    Innovations that bring value propositions through new processes, resources, or technologies to practice. How are we changing outcomes through new innovations in practice? The articles in this issue will provide nurses with the information they need to improve patient outcomes. Articles are devoted to skin cancer screening in the medically underserved, innovative practices for risk assessment and documentation in preventative colon screenings, evidenced-based protocol for diagnosis and treatment of catheter associated urinary tract infection within adult neurocritical care patient population, changing the focus to the PATIENT plan of care, and a nursing focus on EMR usability enhancing documentation of patient outcomes.

  • Principles of Big Data

    Preparing, Sharing, and Analyzing Complex Information
    • 1st Edition
    • Jules J. Berman
    • English
    Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

  • Measuring Data Quality for Ongoing Improvement

    A Data Quality Assessment Framework
    • 1st Edition
    • Laura Sebastian-Coleman
    • English
    The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies.