Computational Learning Approaches to Data Analytics in Biomedical Applications

• Author : Donald Wunsch,Tayo Obafemi-Ajayi,Gayla Olbricht,Khalid Al-Jabery
• Publisher : Academic Press
• Release : 15 September 2019

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

• Author : Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
• Publisher : Academic Press
• Release : 29 November 2019

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

• Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
• Publisher : Academic Press
• Release : 02 July 2020

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

• Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
• Publisher : CRC Press
• Release : 07 October 2020

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system

• Author : Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
• Publisher : Academic Press
• Release : 23 October 2020

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction

• Author : Hemanth D. Jude,Deepak Gupta,Valentina Emilia Balas
• Publisher : Academic Press
• Release : 15 March 2019

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,

• Author : Mourad Elloumi
• Publisher : Springer
• Release : 09 May 2021

This book is the first overview on Deep Learning (DL) and 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

• Author : Valentina E Balas,Vijender Kumar Solanki,Raghvendra Kumar Mishra,Manju Khari
• Publisher : Academic Press
• Release : 15 November 2019

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress in alignment with the latest technologies of Big Data and the Internet of Things. The book includes the most current research developments in the field of biomedical engineering applications based on IoT and Big data, including various real-time and offline medical applications that directly or indirectly rely on medical and information technology. The book also includes case studies in the field of

• Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
• Publisher : Academic Press
• Release : 23 November 2020

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

• Author : Ivo D. Dinov
• Publisher : Springer
• Release : 07 April 2018

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is

• Author : Robert Hoyt,Robert Muenchen
• Publisher : Lulu.com
• Release : 25 November 2019

Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback

• Author : Maria Deprez,Emma C. Robinson
• Publisher : Academic Press
• Release : 15 April 2022

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

• 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

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

• Author : Siddhartha Bhattacharyya,Vaclav Snasel,Deepak Gupta,Ashish Khanna
• Publisher : Academic Press
• Release : 05 March 2020

Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of

• Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
• Publisher : Academic Press
• Release : 11 August 2016

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging