Deep Learning for Biomedical Applications

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  • Author : Utku Kose
  • Publisher : CRC Press
  • Pages : 364 pages
  • ISBN : 1000406423
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Deep Learning for Biomedical Applications

Download or Read online Deep Learning for Biomedical Applications full in PDF, ePub and kindle. this book written by Utku Kose and published by CRC Press which was released on 20 July 2021 with total page 364 pages. We cannot guarantee that Deep Learning for Biomedical Applications book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Deep Learning for Biomedical Applications

Deep Learning for Biomedical Applications
  • Author : Utku Kose,Omer Deperlioglu,D. Jude Hemanth
  • Publisher : CRC Press
  • Release : 20 July 2021
GET THIS BOOK Deep Learning for Biomedical Applications

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series,

Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications
  • Author : Maria Deprez,Emma C. Robinson
  • Publisher : Academic Press
  • Release : 15 June 2022
GET THIS BOOK Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning, where concepts are presented in short descriptions followed by solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical

Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
  • Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
  • Publisher : Academic Press
  • Release : 23 November 2020
GET THIS BOOK Handbook of Deep Learning in Biomedical Engineering

Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in

Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 29 May 2020
GET THIS BOOK Deep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
  • Author : Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
  • Publisher : Apple Academic Press
  • Release : 15 December 2020
GET THIS BOOK Handbook of Artificial Intelligence in Biomedical Engineering

"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the

Data Mining and Machine Learning for Biomedical Applications

Data Mining and Machine Learning for Biomedical Applications
  • Author : Erin Teeple
  • Publisher : Academic Press
  • Release : 15 March 2022
GET THIS BOOK Data Mining and Machine Learning for Biomedical Applications

Data Mining and Machine Learning for Biomedical Applications is a rigorous practical introduction to the fundamentals of data science. It discusses topics such as data integration and management; statistical methods of data science; methodological approaches used for data mining and knowledge discovery with biomedical domain examples; the core principles and methods of hypothesis-driven statistical analyses; differences and relative benefits of machine learning approaches; predictive model performance assessment; and concepts of bias and variance with respect to the design and evaluation

Biomedical Applications Based on Natural and Artificial Computing

Biomedical Applications Based on Natural and Artificial Computing
  • Author : José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli
  • Publisher : Springer
  • Release : 10 June 2017
GET THIS BOOK Biomedical Applications Based on Natural and Artificial Computing

The two volumes LNCS 10337 and 10338 constitute the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, held in Corunna, Spain, in June 2017. The total of 102 full papers was carefully reviewed and selected from 194 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on natural and artificial computation for biomedicine and neuroscience, addressing topics such as theoretical neural computation; models; natural computing in bioinformatics; physiological computing in affective smart

Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications
  • Author : Hemanth D. Jude,Deepak Gupta,Valentina Emilia Balas
  • Publisher : Academic Press
  • Release : 15 March 2019
GET THIS BOOK Intelligent Data Analysis for Biomedical Applications

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,

Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications
  • Author : Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
  • Publisher : Academic Press
  • Release : 29 November 2019
GET THIS BOOK Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes

From Bioinspired Systems and Biomedical Applications to Machine Learning

From Bioinspired Systems and Biomedical Applications to Machine Learning
  • Author : José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli
  • Publisher : Springer
  • Release : 09 May 2019
GET THIS BOOK From Bioinspired Systems and Biomedical Applications to Machine Learning

The two volume set LNCS 11486 and 11487 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, held in Almería, Spain,, in June 2019. The total of 103 contributions was carefully reviewed and selected from 190 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on understanding the brain function and emotions, addressing topics such as new tools for analyzing neural data, or detection emotional states, or interfacing with physical

Development and Evaluation of Machine Learning Algorithms for Biomedical Applications

Development and Evaluation of Machine Learning Algorithms for Biomedical Applications
  • Author : Turki Talal Turki
  • Publisher : Unknown
  • Release : 24 January 2022
GET THIS BOOK Development and Evaluation of Machine Learning Algorithms for Biomedical Applications

Gene network inference and drug response prediction are two important problems in computational biomedicine. The former helps scientists better understand the functional elements and regulatory circuits of cells. The latter helps a physician gain full understanding of the effective treatment on patients. Both problems have been widely studied, though current solutions are far from perfect. More research is needed to improve the accuracy of existing approaches.

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
  • Author : Ervin Sejdic,Tiago H. Falk
  • Publisher : CRC Press
  • Release : 04 July 2018
GET THIS BOOK Signal Processing and Machine Learning for Biomedical Big Data

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

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics

Deep Learning  Machine Learning and IoT in Biomedical and Health Informatics
  • Author : Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
  • Publisher : CRC Press
  • Release : 11 February 2022
GET THIS BOOK Deep Learning Machine Learning and IoT in Biomedical and Health Informatics

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions

Deep Learning for Medical Applications with Unique Data

Deep Learning for Medical Applications with Unique Data
  • Author : Deepak Gupta,Utku Kose,Ashish Khanna,Valentina Emilia Balas
  • Publisher : Academic Press
  • Release : 15 March 2022
GET THIS BOOK Deep Learning for Medical Applications with Unique Data

Deep Learning for Medical Applications with Unique Data informs readers about the most recent Deep Learning-based medical applications in which only unique data gathered in real cases is used. The editors provide examples of how Deep Learning can be used in different problem scopes and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The editors discuss not only positive findings but also negative