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Author: Khan, Rijwan
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 367
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Book Description
Despite the remarkable progress witnessed in the last decade in big data utilization and parallel processing techniques, a persistent disparity exists between the capabilities of computer-aided diagnosis systems and the intricacies of practical healthcare scenarios. This disconnection is particularly evident in the complex landscape of artificial intelligence (AI) and IoT innovations within the biomedical realm. The need to bridge this gap and explore the untapped potential in healthcare and biomedical applications has never been more crucial. As we navigate through these challenges, Applications of Parallel Data Processing for Biomedical Imaging offers insights and solutions to reshape the future of biomedical research. The objective of Applications of Parallel Data Processing for Biomedical Imaging is to bring together researchers from both the computer science and biomedical research communities. By showcasing state-of-the-art deep learning and large data analysis technologies, the book provides a platform for the cross-pollination of ideas between AI-based and traditional methodologies. The collaborative effort seeks to have a substantial impact on data mining, AI, computer vision, biomedical research, healthcare engineering, and other related fields. This interdisciplinary approach positions the book as a cornerstone for scholars, professors, and professionals working in software and medical fields, catering to both graduate and undergraduate students eager to explore the evolving landscape of parallel computing, artificial intelligence, and their applications in biomedical research.
Author: Khan, Rijwan
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 367
Get Book Here
Book Description
Despite the remarkable progress witnessed in the last decade in big data utilization and parallel processing techniques, a persistent disparity exists between the capabilities of computer-aided diagnosis systems and the intricacies of practical healthcare scenarios. This disconnection is particularly evident in the complex landscape of artificial intelligence (AI) and IoT innovations within the biomedical realm. The need to bridge this gap and explore the untapped potential in healthcare and biomedical applications has never been more crucial. As we navigate through these challenges, Applications of Parallel Data Processing for Biomedical Imaging offers insights and solutions to reshape the future of biomedical research. The objective of Applications of Parallel Data Processing for Biomedical Imaging is to bring together researchers from both the computer science and biomedical research communities. By showcasing state-of-the-art deep learning and large data analysis technologies, the book provides a platform for the cross-pollination of ideas between AI-based and traditional methodologies. The collaborative effort seeks to have a substantial impact on data mining, AI, computer vision, biomedical research, healthcare engineering, and other related fields. This interdisciplinary approach positions the book as a cornerstone for scholars, professors, and professionals working in software and medical fields, catering to both graduate and undergraduate students eager to explore the evolving landscape of parallel computing, artificial intelligence, and their applications in biomedical research.
Author: D. Jude Hemanth
Publisher: Academic Press
ISBN: 0128156430
Category : Computers
Languages : en
Pages : 297
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Book Description
Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases. - Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection - Contains an analysis of medical databases to provide diagnostic expert systems - Addresses the integration of intelligent data analysis techniques within biomedical information systems
Author: Arun Kumar Sangaiah
Publisher: Academic Press
ISBN: 0128172932
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Author: Arora, Geeta
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 356
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Book Description
In today's data-driven world, understanding and interpreting statistical information is more critical than ever, especially in medicine, where statistical methods are used to design and analyze clinical trials, study the distribution of disease in populations, and develop new treatments. In the era of evidence-based medicine, Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology addresses the critical need for a grasp of statistical concepts. This book delves into biostatistics, clinical trials, and epidemiology, offering a robust foundation for understanding and interpreting statistical information in medicine. It explores biostatistics, elucidating fundamental elements such as probability, sampling, and hypothesis testing. The section on clinical trials covers the entire spectrum from trial design to ethical considerations, providing an invaluable resource for researchers navigating the complexities of medical research. Epidemiology, a cornerstone of public health, is examined in the book, offering insights into the distribution and determinants of diseases in populations. The application-focused section further extends the utility of medical statistics, encompassing public health, healthcare policy, and drug development.
Author: Stefan O. Schönberg
Publisher: Springer Science & Business Media
ISBN: 354068879X
Category : Medical
Languages : en
Pages : 548
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Book Description
This book presents the first in-depth introduction to parallel imaging techniques and, in particular, to the application of parallel imaging in clinical MRI. It will provide readers with a broader understanding of the fundamental principles of parallel imaging and of the advantages and disadvantages of specific MR protocols in clinical applications in all parts of the body at 1.5 and 3 Tesla.
Author: D.J. Hemanth
Publisher: IOS Press
ISBN: 1643682199
Category : Computers
Languages : en
Pages : 450
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Book Description
Recent developments in parallel computing mean that the use of machine learning techniques and intelligence to handle the huge volume of available data have brought the faster solutions offered by advanced technologies to various fields of application. This book presents the proceedings of the Virtual International Conference on Advances in Parallel Computing Technologies and Applications (ICAPTA 2021), hosted in Justice Basheer Ahmed Sayeed College for women (formerly "S.I.E.T Women's College"), Chennai, India, and held online as a virtual event on 15 and 16 April 2021. The aim of the conference was to provide a forum for sharing knowledge in various aspects of parallel computing in communications systems and networking, including cloud and virtualization solutions, management technologies, and vertical application areas. It also provided a platform for scientists, researchers, practitioners and academicians to present and discuss the most recent innovations and trends, as well as the concerns and practical challenges encountered in this field. Included here are 52 full length papers, selected from over 100 submissions based on the reviews and comments of subject experts. Topics covered include parallel computing in communication, machine learning intelligence for parallel computing and parallel computing for software services in theoretical and practical aspects. Providing an overview of the latest developments in the field, the book will be of interest to all those whose work involves the use of parallel computing technologies.
Author:
Publisher: IOS Press
ISBN:
Category :
Languages : en
Pages : 4947
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Book Description
Author: Ervin Sejdic
Publisher: CRC Press
ISBN: 149877346X
Category : Medical
Languages : en
Pages : 624
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Book Description
Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
Author: Lucian Mihai Itu
Publisher: Springer
ISBN: 3319568531
Category : Medical
Languages : en
Pages : 232
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Book Description
Hemodynamic computations represent a state-of-the-art approach for patient-specific assessment of cardiovascular pathologies. The book presents the development of reduced-order multiscale hemodynamic models for coronary artery disease, aortic coarctation and whole body circulation, which can be applied in routine clinical settings for personalized diagnosis. Specific parameter estimation frameworks are introduced for calibrating the parameters of the models and high performance computing solutions are employed to reduce their execution time. The personalized computational models are validated against patient-specific measurements. The book is written for scientists in the field of biomedical engineering focusing on the cardiovascular system, as well as for research-oriented physicians in cardiology and industrial players in the field of healthcare technologies.
Author: Paolo Di Giamberardino
Publisher: CRC Press
ISBN: 0203075374
Category : Computers
Languages : en
Pages : 494
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Book Description
Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications III contains all contributions presented at the International Symposium CompIMAGE 2012 - Computational Modelling of Object Presented in Images: Fundamentals, Methods and Applications (Rome, Italy, 5-7 September 2012). The contributions cover the state-o
