Advanced Soft Computing Techniques In Data Science Iot And Cloud Computing
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Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing
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.
Bioinformatics and Beyond
This book is a comprehensive exploration of the dynamic interplay between bioinformatics and artificial intelligence (AI) within the healthcare landscape. This book introduces readers to the foundational principles of bioinformatics and AI, elucidating their integration and collaborative potential. Bioinformatics and Beyond: AI Applications in Healthcare explores the transformative impact of data-driven insights, showcasing the applications of machine learning in diagnostics, personalized medicine, and genomic advancements. The book unveils the pivotal role AI plays in accelerating pharmaceutical research. Moreover, it addresses the practical implementation of AI in clinical decision support systems, while also critically examining challenges and ethical considerations associated with these technologies. Finally, the book looks toward the future, envisioning emerging trends and technologies that promise to reshape the future of healthcare. Aimed at professionals, researchers, and students across diverse disciplines, this book serves as an invaluable guide to understanding and navigating the evolving landscape of AI applications in healthcare. This book is tailored to meet the needs of scientists, researchers, practitioners, professionals, and educators actively engaged in the realms of bioinformatics, artificial intelligence, and healthcare. It will be an indispensable resource for those seeking advanced strategies to address challenges and harness opportunities in the rapidly evolving fields of medical and biomedical research.
Mining Biomedical Text, Images and Visual Features for Information Retrieval
Mining Biomedical Text, Images and Visual Features for Information Retrieval provides the reader with a broad coverage of the concepts, themes, and instrumentalities of the important and evolving area of biomedical text, images, and visual features towards information retrieval. It aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research.The book discusses topics such as internet of things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications.It is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work. - Describes many biomedical imaging techniques to detect diseases at the cellular level i.e., image segmentation, classification, or image indexing using a variety of computational intelligence and image processing approaches - Discusses how data mining techniques can be used for noise diminution and filtering MRI, EEG, MEG, fMRI, fNIRS, and PET Images - Presents text mining techniques used for clinical documents in the areas of medicine and Biomedical NLP Systems