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Author: Nilanjan Dey
Publisher: Academic Press
ISBN: 012816087X
Category : Science
Languages : en
Pages : 348
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Book Description
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
Author: Nilanjan Dey
Publisher: Academic Press
ISBN: 012816087X
Category : Science
Languages : en
Pages : 348
Get Book Here
Book Description
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
Author: Nilanjan Dey
Publisher: Springer
ISBN: 3319659812
Category : Technology & Engineering
Languages : en
Pages : 453
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Book Description
This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges. Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization. In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.
Author: Nilanjan Dey
Publisher: Academic Press
ISBN: 0128180056
Category : Science
Languages : en
Pages : 220
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Book Description
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. - Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges - Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications - Introduces several techniques for medical image processing and analysis for CAD systems design
Author: Hayit Greenspan
Publisher: Springer Nature
ISBN: 3031439902
Category : Computers
Languages : en
Pages : 841
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Book Description
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Author: B. Vinoth Kumar
Publisher: Springer Nature
ISBN: 3031205413
Category : Technology & Engineering
Languages : en
Pages : 359
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Book Description
This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field.
Author: Archana Mire
Publisher: John Wiley & Sons
ISBN: 111979241X
Category : Computers
Languages : en
Pages : 436
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Book Description
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
Author: Bilgaiyan, Saurabh
Publisher: IGI Global
ISBN: 1668449714
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Recently, there has been an increase in the number of e-commerce users. This has caused online shopping to become a new and challenging market for e-commerce vendors. Security, inventory management, reliability, and performance of e-commerce websites are a few of the challenges associated with the rising popularity of e-commerce. On a daily basis, millions of e-commerce transactions are taking place. This generates a huge amount of data that can be used to solve the various challenges of e-commerce. Further study on how this data can be used to address these issues is required to propel businesses forward. Empirical Research for Futuristic E-Commerce Systems: Foundations and Applications shares experiences and research outcomes on all aspects of intelligent software solutions such as machine learning, nature-inspired computing, and data science for business-to-consumer (B2C) e-commerce. By looking at the exponential growth of the e-commerce market and its popularity, this book also focuses on the current issues, solutions, and future possibilities in the B2C model of e-commerce. Covering a range of critical topics such as online shopping, supply chain management, and blockchain, this reference work is ideal for academic scientists, data scientists, software developers, business experts, researchers, scholars, practitioners, academicians, instructors, and students.
Author: Lalit Garg
Publisher: Springer Nature
ISBN: 3031131509
Category : Technology & Engineering
Languages : en
Pages : 589
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Book Description
This multidisciplinary book delves into information systems’ concepts, principles, methods and procedures and their innovative applications in management science and other domains, including business, industry, health care and education. It will be valuable to students, researchers, academicians, developers, policymakers and managers thriving to improve their information and management systems, develop new strategies to solve complex problems and implement novel techniques to utilise the massive data best. This book of Information Systems and Management Science (proceedings of ISMS 2021) is intended to be used as a reference by scholars, scientists and practitioners who collect scientific and technical contributions concerning models, tools, technologies and applications in the field of information systems and management science. This book shows how to exploit information systems in a technology-rich management field.
Author: Amit Kumar
Publisher: Springer Nature
ISBN: 9811514208
Category : Technology & Engineering
Languages : en
Pages : 2010
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Book Description
This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
Author: Walid A. Zgallai
Publisher: Elsevier
ISBN: 0323954634
Category : Science
Languages : en
Pages : 437
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Book Description
Artificial Intelligence and Image Processing in Medical Imaging deals with the applications of processing medical images with a view of improving the quality of the data in order to facilitate better decision- making. The book covers the basics of medical imaging and the fundamentals of image processing. It explains spatial and frequency domain applications of image processing, introduces image compression techniques and their applications, and covers image segmentation techniques and their applications. The book includes object detection and classification applications and provides an overall background to statistical analysis in biomedical systems. The role of Machine Learning, including Neural Networks, Deep Learning, and the implications of the expansion of artificial intelligence is also covered. With contributions from prominent researchers worldwide, this book provides up-to-date and comprehensive coverage of AI applications in image processing where readers will find the latest information with clear examples and illustrations. - Provides the latest comprehensive coverage of the developments of AI techniques and the principles of medical imaging - Covers all aspects of medical imaging, from acquisition, the use of hardware and software, image analysis and implementation of AI in problem solving - Provides examples of medical imaging and how they're processed, including segmentation, classification, and detection
