Green AI-Powered Intelligent Systems for Disease Prognosis

By exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, this book captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields.

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Experts in Medicine are under new pressures of advancing their studies while also reducing the impact they leave on the environment. Researchers within the fields of bio-neuro informatics, healthcare, engineering, and medical sciences require a dynamic platform that bridges the realms of academia, science, industry, and innovation. Green AI-Powered Intelligent Systems for Disease Prognosis facilitates a crossroads for a diverse audience interested in these two seldom coalesced concepts. Academicians, scientists, researchers, professionals, decision-makers, and even aspiring scholars all find a space to contribute, collaborate, and learn within the platform that this book provides. The book's thematic coverage is unequivocally compelling; by exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, it captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields. Two prominent tracks form the backbone of the book's content. The first covers the Bioinformatics and Data Mining of Biological Data (BiDMBD), and unravels the intricacies of biomedical computation, signal analysis, clinical decision support, and health data mining. This approach holds a treasure trove of insights into the mechanisms of health data acquisition, clinical informatics, and the representation of healthcare knowledge. The second covers Biomedical Informatics and is a symposium of computational modeling, genomics, and proteomics. Here, the fusion of data science with medical sciences takes center stage. This track's journey traverses machine learning, pattern recognition, and cutting-edge applications in bioinformatics. From gene expression array analysis to translational bioinformatics, it maps the transformative potential of data-driven medical research. In a world where sustainability and innovation intersect, the notion of Green AI-Powered Intelligent Systems for Disease Prognosis underscores an eco-conscious approach to technology. This holistic perspective encapsulates not only the advancement of healthcare technologies but also their harmonization with nature. This forward-looking ethos is an overarching theme that binds the various tracks and topics explored in the book.