Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
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Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
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Author : Shadi N. M. Albarqouni
language : en
Publisher:
Release Date : 2017
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning written by Shadi N. M. Albarqouni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
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Author : Shadi Albarqouni
language : en
Publisher:
Release Date : 2017
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning written by Shadi Albarqouni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Deep Learning And Data Labeling For Medical Applications
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Author : Gustavo Carneiro
language : en
Publisher: Springer
Release Date : 2016-10-07
Deep Learning And Data Labeling For Medical Applications written by Gustavo Carneiro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-07 with Computers categories.
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Internet Of Things Enabled Machine Learning For Biomedical Application
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Author : Neha Goel
language : en
Publisher: CRC Press
Release Date : 2024-11-13
Internet Of Things Enabled Machine Learning For Biomedical Application written by Neha Goel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-13 with Technology & Engineering categories.
The text begins by highlighting the benefits of the Internet of Things-enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and analyzes security and privacy issues in the healthcare systems using the Internet of Things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images. This book: · Covers the procedure to recognize emotions using image processing and the Internet of Things-enabled machine learning. · Highlights security and privacy issues in the healthcare system using the Internet of Things. · Discusses classification and implementation techniques of image segmentation. · Explains different algorithms of machine learning for image processing in a comprehensive manner. · Provides computational intelligence on the Internet of Things for future biomedical applications including lung cancer. It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
Artificial Intelligence Internet Of Things Iot And Smart Materials For Energy Applications
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Author : Mohan Lal Kolhe
language : en
Publisher: CRC Press
Release Date : 2022-10-12
Artificial Intelligence Internet Of Things Iot And Smart Materials For Energy Applications written by Mohan Lal Kolhe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-12 with Computers categories.
This reference text offers the reader a comprehensive insight into recent research breakthroughs in blockchain, the Internet of Things (IoT), artificial intelligence and material structure and hybrid technologies in their integrated platform, while also emphasizing their sustainability aspects. The text begins by discussing recent advances in energy materials and energy conversion materials using machine learning, as well as recent advances in optoelectronic materials for solar energy applications. It covers important topics including advancements in electrolyte materials for solid oxide fuel cells, advancements in composite materials for Li-ion batteries, progression of materials for supercapacitor applications, and materials progression for thermochemical storage of low-temperature solar thermal energy systems. This book: Discusses advances in blockchain, the Internet of Things, artificial intelligence, material structure and hybrid technologies Covers intelligent techniques in materials progression for sensor development and energy material characterization using signal processing Examines the integration of phase change materials in construction for thermal energy regulation in new buildings Explores the current happenings in technology in conjunction with basic laws and mathematical models Connecting advances in engineering materials with the use of smart techniques including artificial intelligence, machine learning and Internet of Things (IoT) in a single volume, this text will be especially useful for graduate students, academic researchers and professionals in the fields of electrical engineering, electronics engineering, materials science, mechanical engineering and computer science.
Deep Learning In Biomedical And Health Informatics
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Author : M. A. Jabbar
language : en
Publisher: CRC Press
Release Date : 2021-09-27
Deep Learning In Biomedical And Health Informatics written by M. A. Jabbar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Computers categories.
This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.
Deep Learning For Biomedical Data Analysis
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Author : Mourad Elloumi
language : en
Publisher: Springer Nature
Release Date : 2021-07-13
Deep Learning For Biomedical Data Analysis written by Mourad Elloumi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Medical categories.
This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Deep Learning And Computer Vision Models And Biomedical Applications
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Author : Uma N. Dulhare
language : en
Publisher: Springer Nature
Release Date : 2025-07-18
Deep Learning And Computer Vision Models And Biomedical Applications written by Uma N. Dulhare and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Computers categories.
This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Mobile Crowdsourcing
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Author : Jie Wu
language : en
Publisher: Springer Nature
Release Date : 2023-07-16
Mobile Crowdsourcing written by Jie Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-16 with Computers categories.
This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computationtasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted. This book will be of value to researchers and students targeting this topic as a reference book. Practitioners, government officials, business organizations and even customers -- working, participating or those interested in fields related to crowdsourcing will also want to purchase this book.
Machine Learning Big Data And Iot For Medical Informatics
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Author : Pardeep Kumar
language : en
Publisher: Academic Press
Release Date : 2021-06-13
Machine Learning Big Data And Iot For Medical Informatics written by Pardeep Kumar and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-13 with Computers categories.
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. - Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. - Includes several privacy preservation techniques for medical data. - Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. - Offers case studies and applications relating to machine learning, big data, and health care analysis.