Federated Learning For Neural Disorders In Healthcare 6 0
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Federated Learning For Neural Disorders In Healthcare 6 0
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Author : Kishor Kumar Reddy C
language : en
Publisher: CRC Press
Release Date : 2025-05-14
Federated Learning For Neural Disorders In Healthcare 6 0 written by Kishor Kumar Reddy C and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-14 with Technology & Engineering categories.
This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders. This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.
Federated Learning For Neural Disorders In Healthcare 6 0
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Author : Kishor Kumar Reddy C
language : en
Publisher: CRC Press
Release Date : 2025-05-14
Federated Learning For Neural Disorders In Healthcare 6 0 written by Kishor Kumar Reddy C and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-14 with Computers categories.
This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer's disease using neural networks and ensuring data privacy and security in federated learning for neural disorders. This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2021-06-11
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Technology & Engineering categories.
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
Federated Learning And Privacy Preserving In Healthcare Ai
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Federated Learning And Privacy Preserving In Healthcare Ai written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Medical categories.
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.
Big Data In Psychiatry And Neurology
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Author : Ahmed Moustafa
language : en
Publisher: Academic Press
Release Date : 2021-06-11
Big Data In Psychiatry And Neurology written by Ahmed Moustafa and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Medical categories.
Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. - Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders - Analyzes methods in using big data to treat psychiatric and neurological disorders - Describes the role machine learning can play in the analysis of big data - Demonstrates the various methods of gathering big data in medicine - Reviews how to apply big data to genetics
Computational Intelligence Algorithms For The Diagnosis Of Neurological Disorders
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Author : S. N. Kumar
language : en
Publisher: CRC Press
Release Date : 2025-08-06
Computational Intelligence Algorithms For The Diagnosis Of Neurological Disorders written by S. N. Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-06 with Computers categories.
This book delves into the transformative potential of artificial intelligence (AI) and machine learning (ML) as game-changers in diagnosing and managing neurodisorder conditions. It covers a wide array of methodologies, algorithms, and applications in depth. Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders equips readers with a comprehensive understanding of how computational intelligence empowers healthcare professionals in the fight against neurodisorders. Through practical examples and clear explanations, it explores the diverse applications of these technologies, showcasing their ability to analyze complex medical data, identify subtle patterns, and contribute to the development of more accurate and efficient diagnostic tools. The authors delve into the exciting possibilities of AI-powered algorithms, exploring their ability to analyze various data sources like neuroimaging scans, genetic information, and cognitive assessments. They also examine the realm of ML for pattern recognition, enabling the identification of early disease markers and facilitating timely intervention. Finally, the authors also address the critical challenges of data privacy and security, emphasizing the need for robust ethical frameworks to safeguard sensitive patient information. This book aims to spark a conversation and foster collaboration among researchers, clinicians, and technologists, and will assist radiologists and neurologists in making precise diagnoses with enhanced accuracy.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Machine Learning For Brain Disorders
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Author : Olivier Colliot
language : en
Publisher: Springer Nature
Release Date : 2023-07-24
Machine Learning For Brain Disorders written by Olivier Colliot and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-24 with Medical categories.
This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.
Neural Information Processing
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Author : Mohammad Tanveer
language : en
Publisher: Springer Nature
Release Date : 2023-04-14
Neural Information Processing written by Mohammad Tanveer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-14 with Computers categories.
The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Deep Learning With Azure
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Author : Mathew Salvaris
language : en
Publisher:
Release Date : 2018
Deep Learning With Azure written by Mathew Salvaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Microsoft Azure (Computing platform) categories.
Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI?Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll LearnBecome familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AIUse pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more)Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolvingDiscover the options for training and operationalizing deep learning models on Azure Who This Book Is ForProfessional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.