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Book Synopsis Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications by : Nguyen Hoang Phuong
Download or read book Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications written by Nguyen Hoang Phuong and published by Springer Nature. This book was released on with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies. Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques). The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.
Book Synopsis Deep Learning and Other Soft Computing Techniques by : Nguyen Hoang Phuong
Download or read book Deep Learning and Other Soft Computing Techniques written by Nguyen Hoang Phuong and published by Springer Nature. This book was released on 2023-06-26 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies. Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques). The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.
This book describes current and potential use of artificial intelligence and computational intelligence techniques in biomedicine and other application areas. Medical applications range from general diagnostics to processing of X-ray images to e-medicine-related privacy issues. Medical community understandably prefers methods that have been successful other on other application areas, where possible mistakes are not that critical. This book describes many promising methods related to deep learning, fuzzy techniques, knowledge graphs, and quantum computing. It also describes the results of testing these new methods in communication networks, education, environmental studies, food industry, retail industry, transportation engineering, and many other areas. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.
Book Synopsis Biomedical and Other Applications of Soft Computing by : Nguyen Hoang Phuong
Download or read book Biomedical and Other Applications of Soft Computing written by Nguyen Hoang Phuong and published by Springer Nature. This book was released on 2022-11-22 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes current and potential use of artificial intelligence and computational intelligence techniques in biomedicine and other application areas. Medical applications range from general diagnostics to processing of X-ray images to e-medicine-related privacy issues. Medical community understandably prefers methods that have been successful other on other application areas, where possible mistakes are not that critical. This book describes many promising methods related to deep learning, fuzzy techniques, knowledge graphs, and quantum computing. It also describes the results of testing these new methods in communication networks, education, environmental studies, food industry, retail industry, transportation engineering, and many other areas. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.
This book presents innovative intelligent techniques, with an emphasis on their biomedical applications. Although many medical doctors are willing to share their knowledge – e.g. by incorporating it in computer-based advisory systems that can benefit other doctors – this knowledge is often expressed using imprecise (fuzzy) words from natural language such as “small,” which are difficult for computers to process. Accordingly, we need fuzzy techniques to handle such words. It is also desirable to extract general recommendations from the records of medical doctors’ decisions – by using machine learning techniques such as neural networks. The book describes state-of-the-art fuzzy, neural, and other techniques, especially those that are now being used, or potentially could be used, in biomedical applications. Accordingly, it will benefit all researchers and students interested in the latest developments, as well as practitioners who want to learn about new techniques.
Book Synopsis Soft Computing for Biomedical Applications and Related Topics by : Vladik Kreinovich
Download or read book Soft Computing for Biomedical Applications and Related Topics written by Vladik Kreinovich and published by Springer Nature. This book was released on 2020-06-29 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents innovative intelligent techniques, with an emphasis on their biomedical applications. Although many medical doctors are willing to share their knowledge – e.g. by incorporating it in computer-based advisory systems that can benefit other doctors – this knowledge is often expressed using imprecise (fuzzy) words from natural language such as “small,” which are difficult for computers to process. Accordingly, we need fuzzy techniques to handle such words. It is also desirable to extract general recommendations from the records of medical doctors’ decisions – by using machine learning techniques such as neural networks. The book describes state-of-the-art fuzzy, neural, and other techniques, especially those that are now being used, or potentially could be used, in biomedical applications. Accordingly, it will benefit all researchers and students interested in the latest developments, as well as practitioners who want to learn about new techniques.
This book lists current and potential biomedical uses of computational intelligence methods. These methods are used in diagnostics and treatment of such diseases as cancer, cardiac diseases, pneumonia, stroke, and COVID-19. Many biomedical problems are difficult; so, often, the current methods are not sufficient, new methods need to be developed. To confidently apply the new methods to critical life-and-death medical situations, it is important to first test these methods on less critical applications. The book describes several such promising new methods that have been tested on problems from agriculture, computer networks, economics and business, pavement engineering, politics, quantum computing, robotics, etc. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.
Book Synopsis Soft Computing: Biomedical and Related Applications by : Nguyen Hoang Phuong
Download or read book Soft Computing: Biomedical and Related Applications written by Nguyen Hoang Phuong and published by Springer Nature. This book was released on 2021-06-16 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lists current and potential biomedical uses of computational intelligence methods. These methods are used in diagnostics and treatment of such diseases as cancer, cardiac diseases, pneumonia, stroke, and COVID-19. Many biomedical problems are difficult; so, often, the current methods are not sufficient, new methods need to be developed. To confidently apply the new methods to critical life-and-death medical situations, it is important to first test these methods on less critical applications. The book describes several such promising new methods that have been tested on problems from agriculture, computer networks, economics and business, pavement engineering, politics, quantum computing, robotics, etc. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.
As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It consists of 11 contributions that features illumination change detection, generator of electronic educational publications, intelligent call triage system, recognition of rocks at uranium deposits, graphics processing units, mathematical model of hit phenomena, selection and mutation in genetic algorithm, hands and arms motion estimation, application of wavelet network, Kanizsa triangle illusion, and support vector machine regression. Also, it describes how to apply the machine learning for the intelligent systems. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and verifications.
Book Synopsis Soft Computing in Machine Learning by : Sang-Yong Rhee
Download or read book Soft Computing in Machine Learning written by Sang-Yong Rhee and published by Springer. This book was released on 2014-07-08 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It consists of 11 contributions that features illumination change detection, generator of electronic educational publications, intelligent call triage system, recognition of rocks at uranium deposits, graphics processing units, mathematical model of hit phenomena, selection and mutation in genetic algorithm, hands and arms motion estimation, application of wavelet network, Kanizsa triangle illusion, and support vector machine regression. Also, it describes how to apply the machine learning for the intelligent systems. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and verifications.
This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.
Book Synopsis Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing by : Sujata Dash
Download or read book Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing written by Sujata Dash and published by Springer Nature. This book was released on 2021-11-05 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.
Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through deep learning and soft-computing techniques. Readers are given a fresh perspective on the impact on the outcomes for healthcare professionals who are assisted by advanced computing algorithms. Key Features: - Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context - Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms - Introduces readers to the concept of telemedicine and electronic healthcare systems - Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer's disease - Summarizes key information for learners - Includes references for advanced readers The book serves as an essential reference for academic readers, as well as computer science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.
Book Synopsis Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems by : Parvathaneni Naga Srinivasu
Download or read book Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems written by Parvathaneni Naga Srinivasu and published by Bentham Science Publishers. This book was released on 2022-10-05 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through deep learning and soft-computing techniques. Readers are given a fresh perspective on the impact on the outcomes for healthcare professionals who are assisted by advanced computing algorithms. Key Features: - Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context - Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms - Introduces readers to the concept of telemedicine and electronic healthcare systems - Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer's disease - Summarizes key information for learners - Includes references for advanced readers The book serves as an essential reference for academic readers, as well as computer science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.
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 are used. The book provides examples of how deep learning can be used in different problem areas 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 volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
Book Synopsis Deep Learning for Medical Applications with Unique Data by : Deepak Gupta
Download or read book Deep Learning for Medical Applications with Unique Data written by Deepak Gupta and published by Academic Press. This book was released on 2022-02-15 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 are used. The book provides examples of how deep learning can be used in different problem areas 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 volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
Focusing on soft computing techniques and application in various engineering research domains, this book presents the state-of-the-art outcomes from ongoing research works being conducted in various research laboratories and educational institutions. The included research works deal with estimated models and give resolutions to complex real-life issues. In the field of evolutionary computing and other domains of applications, such as, data mining and fuzzy logic, soft computing techniques play an incomparable role, where it successfully handles contemporary computationally intensive and complex problems that have usually appeared to be inflexible to traditional mathematical methods. Comprising the concepts and applications of soft computing with other emerging research domains, this book cherishes varieties of modern applications in the fields of natural language processing, image processing, biomedical engineering, communication, control systems, circuit design etc.
Book Synopsis Soft Computing Techniques and Applications by : Samarjeet Borah
Download or read book Soft Computing Techniques and Applications written by Samarjeet Borah and published by Springer Nature. This book was released on 2020-11-27 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on soft computing techniques and application in various engineering research domains, this book presents the state-of-the-art outcomes from ongoing research works being conducted in various research laboratories and educational institutions. The included research works deal with estimated models and give resolutions to complex real-life issues. In the field of evolutionary computing and other domains of applications, such as, data mining and fuzzy logic, soft computing techniques play an incomparable role, where it successfully handles contemporary computationally intensive and complex problems that have usually appeared to be inflexible to traditional mathematical methods. Comprising the concepts and applications of soft computing with other emerging research domains, this book cherishes varieties of modern applications in the fields of natural language processing, image processing, biomedical engineering, communication, control systems, circuit design etc.