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    Machine Learning and AI Techniques in Interactive Medical Image Analysis

    The healthcare industry is predominantly moving towards affordable, accessible, and quality health care.All organizations are striving to build communication compatibility among the wide range of devices that have operated independently.Recent developments in electronic devices have boosted the research in the medical imaging field.It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world.Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer.Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions.This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy.Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.

    Price: 304.00 £ | Shipping*: 0.00 £
  • Interactive Visual Data Analysis
    Interactive Visual Data Analysis

    In the age of big data, being able to make sense of data is an important key to success.Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods.It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes.The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data.A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways.Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis.The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology.Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features:Dedicated to the synthesis of visual, interactive, and analysis methodsSystematic top-down view on visualization, interaction, and automatic analysisBroad coverage of fundamental and advanced visualization techniquesComprehensive chapter on interacting with visual representationsExtensive integration of automatic computational methodsAccessible portrayal of cutting-edge visual analytics technologyForeword by Jack van WijkFor more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.

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  • An Analysis of Albert Bandura's Aggression : A Social Learning Analysis
    An Analysis of Albert Bandura's Aggression : A Social Learning Analysis

    Albert Bandura is the most cited living psychologist, and is regularly named as one of the most influential figures ever to have worked in his field.Much of his reputation stems from the theories and experiments described in his 1973 study Aggression: A Social Learning Analysis – a book that is both a classic of psychological study and a masterclass in the analytical skills central to good critical thinking.Bandura’s central contention is that much human learning is fundamentally social.As children imitate the behavior of those around them, and as their behaviors are reinforced by modelling, they entrench cognitive functions that more or less become part of their core personalities.The experiments that Bandura designed in order to prove his contentions with regard to learned aggressive tendencies show the powers of critical thinking analysis and evaluation at their best.Having set up a play environment for children in which they could be exposed to aggressive behavior (inflicted on a bobo doll), he was able to systematically examine their responses and learned behaviors, working out their functions and understanding the relationships between different aspects of behavior that combined to form a whole.Carefully evaluating at each stage the different extent to which children’s own aggressive behavior was affected by and modelled on what they saw.Bandura produced results that revolutionized psychology’s whole approach to human learning and behavior.

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  • Technology Innovation for the Circular Economy : Recycling, Remanufacturing, Design, System Analysis and Logistics
    Technology Innovation for the Circular Economy : Recycling, Remanufacturing, Design, System Analysis and Logistics

    TECHNOLOGY INNOVATION FOR THE CIRCULAR ECONOMY The book comprises 56 peer-reviewed chapters comprehensively covering in-depth areas of circular economy design, planning, business models, and enabling technologies. Some of the greatest opportunities for innovation in the circular economy are in remanufacturing, refurbishment, reuse, and recycling.Critical to its growth, however, are developments in product design approaches and the manufacturing business model that are often met with challenges in the current, largely linear economies of today’s global manufacturing chains. The conference hosted by the REMADE Institute in Rochester, NY, brought together U.S. and international researchers, industry engineers, technologists, and policymakers, to discuss the myriad intertwining issues relating to the circular economy. This book consists of 56 chapters in 10 distinct parts covering broad areas of research and applications in the circular economy area.The first four parts explore the system level work related to circular economy approaches, models and advancements including the use of artificial intelligence (AI) and machine learning to guide implementation, as well as design for circularity approaches.Mechanical and chemical recycling technologies follow, highlighting some of the most advanced research in those areas.Next, innovation in remanufacturing is addressed with descriptions of some of the most advanced work in this field.This is followed by tire remanufacturing and recycling, highlighting innovative technologies in addressing the volume of end-of-use tires.Pathways to net-zero emissions in manufacturing of materials concludes the book, with a focus on industrial decarbonization. Audience This book has a wide audience in academic institutes, business professionals and engineers in a variety of manufacturing industries.It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.

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  • Learning R for Geospatial Analysis
    Learning R for Geospatial Analysis

    This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming.The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required.By focusing on R exclusively, you will not need to depend on any external software a working installation of R is all that is necessary to begin.

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  • Analytical Technology in Nutrition Analysis
    Analytical Technology in Nutrition Analysis

    Due to increasing global food needs as a result of population growth, the use of new food sources has gained interest in the last decade. However, the inclusion of new foods in our diet, as well as the increased interest of the population in consuming foods with better nutritional properties, has increased the need for adequate food analytical methods. This monographic issue presents innovative methods of chemical analysis of foods, as well as the nutritional and chemical characterization of foods whose consumption is expected to increase worldwide in the coming years.

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  • Business Model Innovation : Concepts, Analysis, and Cases
    Business Model Innovation : Concepts, Analysis, and Cases

    Rooted in strategic management research, Business Model Innovation explores the concepts, tools, and techniques that enable organizations to gain and/or maintain a competitive advantage in the face of technological innovation, globalization, and an increasingly knowledge-intensive economy.Updated with all-new cases, this second edition of the must-have for those looking to grasp the fundamentals of business model innovation, explores the novel ways in which an organization can generate, deliver, and monetize benefits to customers.

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  • Deep Learning for Medical Image Analysis
    Deep Learning for Medical Image Analysis

    Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications.This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.

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