Practical Guide For Biomedical Signals Analysis Using Machine Learning Techniques

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2019-05-15
  • Total pages : 350
  • ISBN : 0128174447
  • File Size : 24,9 Mb
  • Total Download : 897
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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to apply machine learning techniques to EEG, ECG and EMG signals Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2019-03-16
  • Total pages : 456
  • ISBN : 9780128176733
  • File Size : 19,9 Mb
  • Total Download : 963
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Download Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques in PDF, Epub, and Kindle

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to apply machine learning techniques to EEG, ECG and EMG signals Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2020-06-21
  • Total pages : 534
  • ISBN : 9780128213797
  • File Size : 38,7 Mb
  • Total Download : 196
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Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2020-06-05
  • Total pages : 534
  • ISBN : 9780128213803
  • File Size : 50,8 Mb
  • Total Download : 322
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Download Practical Machine Learning for Data Analysis Using Python in PDF, Epub, and Kindle

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Applications of Artificial Intelligence in Medical Imaging

Applications of Artificial Intelligence in Medical Imaging
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2022-11-28
  • Total pages : 381
  • ISBN : 9780443184512
  • File Size : 31,9 Mb
  • Total Download : 887
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Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis. Discusses new deep learning algorithms for image analysis and how they are used for medical images Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes

Sub-Terahertz Sensing Technology for Biomedical Applications

Sub-Terahertz Sensing Technology for Biomedical Applications
  • Author : Shiban Kishen Koul,Priyansha Kaurav
  • Publisher : Springer Nature
  • Release Date : 2022-08-20
  • Total pages : 289
  • ISBN : 9789811931406
  • File Size : 25,9 Mb
  • Total Download : 238
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This book offers the readers an opportunity to acquire the concepts of artificial intelligence (AI) enabled sub-THz systems for novel applications in the biomedical field. The readers will also be inspired to contextualize these applications for solving real life problems such as non-invasive glucose monitoring systems, cancer detection and dental imaging. The introductory section of this book focuses on existing technologies for radio frequency and infrared sensing in biomedical applications, and their limited use in sensing applications, as well as the advantages of using THz technology in this context. This is followed by a detailed comparative analysis of THz electronics technology and other conventional electro optic THz setups highlighting the superior efficiency, affordability and portability of electronics-based THz systems. The book also discusses electronic sub-THz measurement systems for different biomedical applications. The chapters elucidate two major applications where sub-THz provides an edge over existing state of the art techniques used for non-invasive measurement of blood glucose levels and intraoperative assessment of tumor margins. There is a detailed articulation of an application of leveraging machine learning for measurement systems for non-invasive glucose concentration measurement. This helps the reader relate to the output in a more user-friendly format and understand the possible use cases in a more lucid manner. The book is intended to help the reader learn how to build tissue phantoms and characterize them at sub-THz frequencies in order to test the measurement systems. Towards the end of the book, a brief introduction to system automation for biomedical imaging is provided as well for quick analysis of the data. The book will empower the reader to understand and appreciate the immense possibilities of using electronic THz systems in the biomedical field, creating gateways for fueling further research in this area.​

Practical Biomedical Signal Analysis Using MATLAB®

Practical Biomedical Signal Analysis Using MATLAB®
  • Author : Katarzyn J. Blinowska,Jaroslaw Zygierewicz
  • Publisher : CRC Press
  • Release Date : 2011-09-12
  • Total pages : 326
  • ISBN : 9781439812020
  • File Size : 28,5 Mb
  • Total Download : 823
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Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data. The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice. A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.

Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment

Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment
  • Author : Malarvili Balakrishinan,Teo Aik Howe,Santheraleka Ramanathan,Mushikiwabeza Alexie,Om Prakash Singh
  • Publisher : Academic Press
  • Release Date : 2022-11-18
  • Total pages : 210
  • ISBN : 9780323915670
  • File Size : 11,8 Mb
  • Total Download : 465
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Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment provides a comprehensive overview of groundbreaking tools and techniques for the diagnosis and monitoring of asthma. Sections cover an introduction to the human respiratory system and the pathophysiology of asthma before analyzing current assessment concepts, tools and techniques. The book describes spirometry and the peak flow meter as existing tools in assessing asthma along with their limitations. In addition, a detailed description of capnography as a new approach is included with various studies conducted on its analysis. Academicians and researchers in biomedical engineering, particularly in the course of biomedical signal processing and biomedical instrumentation will find the book useful. Introduces a new concept of monitoring the severity of Asthma using a new index extracted from CO2 waveforms Describes a newly designed device, including the significance of CO2 features, selection of infrared CO2 sensors, different components of the device, feature extraction, computation and transmission algorithms, data collection, and analysis and performance evaluations Includes types of capnographs, differentiating normal from abnormal capnograms

Mechano-Electric Correlations in the Human Physiological System

Mechano-Electric Correlations in the Human Physiological System
  • Author : A. Bakiya,K. Kamalanand,R. L. J. De Britto
  • Publisher : CRC Press
  • Release Date : 2021-04-16
  • Total pages : 112
  • ISBN : 9781000374735
  • File Size : 45,5 Mb
  • Total Download : 606
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Download Mechano-Electric Correlations in the Human Physiological System in PDF, Epub, and Kindle

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features: Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanics Explores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniques Provides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical models Explains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological system Presents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle Mechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

Brain and Behavior Computing

Brain and Behavior Computing
  • Author : Mridu Sahu,G R Sinha
  • Publisher : CRC Press
  • Release Date : 2021-06-24
  • Total pages : 428
  • ISBN : 9781000387155
  • File Size : 30,9 Mb
  • Total Download : 422
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Brain and Behavior Computing offers insights into the functions of the human brain. This book provides an emphasis on brain and behavior computing with different modalities available such as signal processing, image processing, data sciences, statistics further it includes fundamental, mathematical model, algorithms, case studies, and future research scopes. It further illustrates brain signal sources and how the brain signal can process, manipulate, and transform in different domains allowing researchers and professionals to extract information about the physiological condition of the brain. Emphasizes real challenges in brain signal processing for a variety of applications for analysis, classification, and clustering. Discusses data sciences and its applications in brain computing visualization. Covers all the most recent tools for analysing the brain and it’s working. Describes brain modeling and all possible machine learning methods and their uses. Augments the use of data mining and machine learning to brain computer interface (BCI) devices. Includes case studies and actual simulation examples. This book is aimed at researchers, professionals, and graduate students in image processing and computer vision, biomedical engineering, signal processing, and brain and behavior computing.

Emerging Technologies in Healthcare

Emerging Technologies in Healthcare
  • Author : Matthew N. O. Sadiku,Rotimi A. K. Jaiyesimi,Joyce B. Idehen,Sarhan M. Musa
  • Publisher : AuthorHouse
  • Release Date : 2021-10-05
  • Total pages : 316
  • ISBN : 9781665528429
  • File Size : 19,8 Mb
  • Total Download : 683
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Health is regarded as one of the global challenges for mankind. Healthcare is a complex system that covers processes of diagnosis, treatment, and prevention of diseases. It constitutes a fundamental pillar of the modern society. Modern healthcare is technological healthcare. Technology is everywhere. This book focuses on twenty-one emerging technologies in the healthcare industry. An emerging technology is one that holds the promise of creating a new economic engine and is trans-industrial. Emerging technological trends are rapidly transforming businesses in general and healthcare in particular in ways that we find hard to imagine. Artificial intelligence (AI), machine learning, robots, blockchain, cloud computing, Internet of things (IoT), and augmented & virtual reality are some of the technologies at the heart of this revolution and are covered in this book. The convergence of these technologies is upon us and will have a huge impact on the patient experience

Disruptive Trends in Computer Aided Diagnosis

Disruptive Trends in Computer Aided Diagnosis
  • Author : Rik Das,Sudarshan Nandy,Siddhartha Bhattacharyya
  • Publisher : CRC Press
  • Release Date : 2021-09-29
  • Total pages : 218
  • ISBN : 9781000414691
  • File Size : 31,9 Mb
  • Total Download : 262
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Disruptive Trends in Computer Aided Diagnosis collates novel techniques and methodologies in the domain of content based image classification and deep learning/machine learning techniques to design efficient computer aided diagnosis architecture. It is aimed to highlight new challenges and probable solutions in the domain of computer aided diagnosis to leverage balancing of sustainable ecology. The volume focuses on designing efficient algorithms for proposing CAD systems to mitigate the challenges of critical illnesses at an early stage. State-of-the-art novel methods are explored for envisaging automated diagnosis systems thereby overriding the limitations due to lack of training data, sample annotation, region of interest identification, proper segmentation and so on. The assorted techniques addresses the challenges encountered in existing systems thereby facilitating accurate patient healthcare and diagnosis. Features: An integrated interdisciplinary approach to address complex computer aided diagnosis problems and limitations. Elucidates a rich summary of the state-of-the-art tools and techniques related to automated detection and diagnosis of life threatening diseases including pandemics. Machine learning and deep learning methodologies on evolving accurate and precise early detection and medical diagnosis systems. Information presented in an accessible way for students, researchers and medical practitioners. The volume would come to the benefit of both post-graduate students and aspiring researchers in the field of medical informatics, computer science and electronics and communication engineering. In addition, the volume is also intended to serve as a guiding factor for the medical practitioners and radiologists in accurate diagnosis of diseases.

Advances on Intelligent Informatics and Computing

Advances on Intelligent Informatics and Computing
  • Author : Faisal Saeed,Fathey Mohammed,Fuad Ghaleb
  • Publisher : Springer Nature
  • Release Date : 2022-03-29
  • Total pages : 787
  • ISBN : 9783030987411
  • File Size : 38,9 Mb
  • Total Download : 543
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This book presents emerging trends in intelligent computing and informatics. This book presents the papers included in the proceedings of the 6th International Conference of Reliable Information and Communication Technology 2021 (IRICT 2021) that was held virtually, on Dec. 22-23, 2021. The main theme of the book is “Advances on Intelligent Informatics and Computing”. A total of 87 papers were submitted to the conference, but only 66 papers were accepted and published in this book. The book presents several hot research topics which include health informatics, artificial intelligence, soft computing, data science, big data analytics, Internet of Things (IoT), intelligent communication systems, cybersecurity, and information systems.

Biomedical Engineering and its Applications in Healthcare

Biomedical Engineering and its Applications in Healthcare
  • Author : Sudip Paul
  • Publisher : Springer Nature
  • Release Date : 2019-11-08
  • Total pages : 738
  • ISBN : 9789811337055
  • File Size : 12,9 Mb
  • Total Download : 254
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This book illustrates the significance of biomedical engineering in modern healthcare systems. Biomedical engineering plays an important role in a range of areas, from diagnosis and analysis to treatment and recovery and has entered the public consciousness through the proliferation of implantable medical devices, such as pacemakers and artificial hips, as well as the more futuristic technologies such as stem cell engineering and 3-D printing of biological organs. Starting with an introduction to biomedical engineering, the book then discusses various tools and techniques for medical diagnostics and treatment and recent advances. It also provides comprehensive and integrated information on rehabilitation engineering, including the design of artificial body parts, and the underlying principles, and standards. It also presents a conceptual framework to clarify the relationship between ethical policies in medical practice and philosophical moral reasoning. Lastly, the book highlights a number of challenges associated with modern healthcare technologies.

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
  • Author : Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
  • Publisher : Academic Press
  • Release Date : 2018-11-30
  • Total pages : 345
  • ISBN : 9780128160879
  • File Size : 30,5 Mb
  • Total Download : 534
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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume I

Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume I
  • Author : Honghao Gao,Ying Li,Zijian Zhang,Wenbing Zhao
  • Publisher : Frontiers Media SA
  • Release Date : 2021-06-17
  • Total pages : 118
  • ISBN : 9782889669325
  • File Size : 47,9 Mb
  • Total Download : 345
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PDF book entitled Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume I written by Honghao Gao,Ying Li,Zijian Zhang,Wenbing Zhao and published by Frontiers Media SA which was released on 2021-06-17 with total hardcover pages 118, the book become popular and critical acclaim.

Statistics, Data Mining, and Machine Learning in Astronomy

Statistics, Data Mining, and Machine Learning in Astronomy
  • Author : Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
  • Publisher : Princeton University Press
  • Release Date : 2014-01-12
  • Total pages : 550
  • ISBN : 9780691151687
  • File Size : 50,9 Mb
  • Total Download : 426
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As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

InECCE2019

InECCE2019
  • Author : Ahmad Nor Kasruddin Nasir,Mohd Ashraf Ahmad,Muhammad Sharfi Najib,Yasmin Abdul Wahab,Nur Aqilah Othman,Nor Maniha Abd Ghani,Addie Irawan,Sabira Khatun,Raja Mohd Taufika Raja Ismail,Mohd Mawardi Saari,Mohd Razali Daud,Ahmad Afif Mohd Faudzi
  • Publisher : Springer Nature
  • Release Date : 2020-03-23
  • Total pages : 905
  • ISBN : 9789811523175
  • File Size : 54,7 Mb
  • Total Download : 303
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This book presents the proceedings of the 5th International Conference on Electrical, Control & Computer Engineering 2019, held in Kuantan, Pahang, Malaysia, on 29th July 2019. Consisting of two parts, it covers the conferences’ main foci: Part 1 discusses instrumentation, robotics and control, while Part 2 addresses electrical power systems. The book appeals to professionals, scientists and researchers with experience in industry.The conference provided a platform for professionals, scientists and researchers with experience in industry.

2nd International Conference for Innovation in Biomedical Engineering and Life Sciences

2nd International Conference for Innovation in Biomedical Engineering and Life Sciences
  • Author : Fatimah Ibrahim,Juliana Usman,Mohd Yazed Ahmad,Norhamizan Hamzah,Swe Jyan Teh
  • Publisher : Springer
  • Release Date : 2017-12-06
  • Total pages : 297
  • ISBN : 9789811075544
  • File Size : 15,8 Mb
  • Total Download : 927
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This volume presents the proceedings of ICIBEL 2017, organized by the Centre for Innovation in Medical Engineering (CIME) under Innovative Technology Research Cluster, University of Malaya. It was held in George Town, Penang, Malaysia, from 10-13 December 2017. The ICIBEL 2017 conference promotes the latest research and developments related to the integration of the Engineering technology in medical fields and life sciences. This includes the latest innovations, research trends and concerns, challenges and adopted solution in the field of medical engineering and life sciences.

Biomedical Signal Analysis

Biomedical Signal Analysis
  • Author : Rangaraj M. Rangayyan
  • Publisher : John Wiley & Sons
  • Release Date : 2015-04-24
  • Total pages : 720
  • ISBN : 9781119067931
  • File Size : 35,9 Mb
  • Total Download : 603
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The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications 800 mathematical expressions and equations Practical questions, problems and laboratory exercises Includes fractals and chaos theory with biomedical applications

An Introduction to Machine Learning

An Introduction to Machine Learning
  • Author : Miroslav Kubat
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • Total pages : 458
  • ISBN : 9783030819354
  • File Size : 29,9 Mb
  • Total Download : 820
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This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.