Download or read book Deep Learning in Aging Neuroscience written by Javier Ramírez and published by Frontiers Media SA. This book was released on 2020-12-28 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Download or read book Methods and applications in aging neuroscience written by Yang Jiang and published by Frontiers Media SA. This book was released on 2023-07-10 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book BrainAGE written by Katja Franke and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2014-09-09 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the widespread but well-ordered brain tissue loss that occurs with healthy aging into senescence, this work presents a novel magnetic resonance imaging (MRI)-based biomarker, which identifies normal and abnormal aging-related brain atrophy. The novel BrainAGE approach is based on a database of structural MRI data, aggregating the complex, multidimensional aging patterns across the whole brain to one single value, i.e. the estimated brain age. Consequently, subtle deviations in "normal" brain atrophy can be directly quantified in terms of years by analyzing one standard MRI scan per subject. Various neuro-degenerative diseases - especially Alzheimer's disease (AD) - are widely linked to advanced brain aging. The BrainAGE approach is applied to identify advanced brain aging in subjects with mild cognitive impairment and AD, to predict conversion to AD, to relate individual BrainAGE scores with disease severity and prospective worsening of cognitive functions. Furthermore, BrainAGE identifies various risk factors of pathological brain aging that may precede the onset of clinical symptoms (e.g., diabetes mellitus type 2, metabolic syndrome).

Download or read book Deep learning techniques and their applications to the healthy and disordered brain during development through adulthood and beyond written by Amir Shmuel and published by Frontiers Media SA. This book was released on 2023-02-07 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases written by Rodriguez, Raul Villamarin and published by IGI Global. This book was released on 2024-02-14 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the context of global health challenges posed by intractable neurodegenerative diseases like Alzheimer's and Parkinson's, the significance of early diagnosis is critical for effective intervention, and scientists continue to discover new methods of detection. However, actual diagnosis goes beyond detection to include a significant analysis of combined data for many cases, which presents a challenge of several complicated calculations. Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases stands as a groundbreaking work at the intersection of artificial intelligence and neuroscience. The book orchestrates a symphony of cutting-edge techniques and progressions in early detection by assembling eminent experts from the domains of deep learning and neurology. Through a harmonious blend of research areas and pragmatic applications, this monumental work charts the transformative course to revolutionize the landscape of early diagnosis and management of neurodegenerative disorders. Within the pages, readers will embark through the intricate landscape of neurodegenerative diseases, the fundamental underpinnings of deep learning, the nuances of neuroimaging data acquisition and preprocessing, the alchemy of feature extraction and representation learning, and the symphony of deep learning models tailored for neurodegenerative disease diagnosis. The book also delves into integrating multimodal data to augment diagnosis, the imperative of rigorously evaluating and validating deep learning models, and the ethical considerations and challenges entwined with deep learning for neurodegenerative diseases.

Download or read book Machine Learning and Deep Learning in Neuroimaging Data Analysis written by Anitha S. Pillai and published by CRC Press. This book was released on 2024-02-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Download or read book Machine Learning in Neuroscience Volume II written by Reza Lashgari and published by Frontiers Media SA. This book was released on 2022-11-14 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine learning in neuroscience written by Hamid R. Rabiee and published by Frontiers Media SA. This book was released on 2023-01-27 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning in Personalized Healthcare and Decision Support written by Harish Garg and published by Elsevier. This book was released on 2023-07-20 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies

Download or read book Diagnosis of Neurological Disorders Based on Deep Learning Techniques written by Jyotismita Chaki and published by CRC Press. This book was released on 2023-05-15 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders. Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis. Explores data preprocessing techniques involved in diagnosis. Includes real-time case studies and examples. This book is aimed at graduate students and researchers in biomedical imaging and machine learning.

Download or read book Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments written by Raj, Alex Noel Joseph and published by IGI Global. This book was released on 2020-12-25 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Download or read book Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications written by Om Prakash Jena and published by CRC Press. This book was released on 2022-02-25 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Download or read book Neuroscience in the 21st Century New Tools and Techniques Driving Exciting Discoveries written by Aliasghar Tabatabaei Mohammadi and published by Nobel Sciences. This book was released on with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Fractal Based Image Analysis written by Soumya Ranjan Nayak and published by Elsevier. This book was released on 2024-06-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis. Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlight the relevance of related application areas for advanced as well as novice-user application. The book presents an in-depth look at core concepts, methodological aspects, and advanced feature opportunities, focusing on major real time applications in engineering science and health science. The book will appeal to researchers, data scientists, industry professionals, and graduate students in the fields of fractal graphics and its related applications. Investigates advanced fractal theories spanning neural networks, fuzzy logic, machine learning, deep learning, and hybrid intelligent systems in solving pattern recognition problems Explores the application of fractal theories to a wide range of medical image processing modalities Presents case studies that illustrate the application and integration of fractal theories into intelligent computing in the resolution of important pattern recognition and machine vision problems

Download or read book Convolutional Neural Network Based Age Estimation from Facial Image and Depth Prediction from Single Image written by Jiayan Qiu and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional neural network (CNN), one of the most commonly used deep learning methods, has been applied to various computer vision and pattern recognition tasks, and has achieved state-of-the-art performance. Most recent research work on CNN focuses on the innovations of the structure. This thesis explores both the innovation of structure and final label encoding of CNN. To evaluate the performance of our proposed network structure and label encoding method, two computer vision tasks are conducted, namely age estimation from facial image and depth estimation from a single image. For age estimation from facial image, we propose a novel hierarchical aggregation based deep network to learn aging features from facial images and apply our encoding method to transfer the discrete aging labels into a possibility label, which enables the CNN to conduct a classification task rather than regression task. In contrast to traditional aging features, where identical filter is applied to the en- tire facial image, our deep aging feature can capture both local and global cues in aging. Under our formulation, convolutional neural network (CNN) is employed to extract region specific features at lower layers. Then, low layer features are hierarchically aggregated by using fully connected way to consecutive higher layers. The resultant aging feature is of dimensionality 110, which achieves both good discriminative ability and efficiency. Experimental results of age prediction on the MORPH-II and the FG-NET databases show that the proposed deep aging feature outperforms state-of-the-art aging features by a margin. Depth estimation from a single image is an essential component toward understanding the 3D geometry of a scene. Compared with depth estimation from stereo images, depth map estimation from a single image is an extremely challenging task. This thesis addresses this task by regression with deep features, combined with surface normal constrained depth refinement. The proposed framework consists of two steps. First, we implement a convolutional neural network (CNN) to learn the mapping from multi-scale image patches to depth on the super-pixel level. In this step, we apply the proposed label encoding method to transfer the continuous depth labels to be possibility vectors, which reformulates the regression task to a classification task. Second, we refine predicted depth at the super-pixel level to the pixel level by exploiting surface normal constraints on depth map. Experimental results of depth estimation on the NYU2 dataset show that the proposed method achieves a promising performance and has a better performance compared with methods without the proposed label encoding. The above tasks show the proposed label encoding method has promising performance, which is another direction of CNN structure optimization.

Download or read book Big Data Analytics for Cloud IoT and Cognitive Computing written by Kai Hwang and published by John Wiley & Sons. This book was released on 2017-03-17 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.