04650nam a22005535i 4500 978-3-031-62316-5 DE-He213 20251030112336.0 cr nn 008mamaa 240627s2024 sz | s |||| 0|eng d 9783031623165 978-3-031-62316-5 10.1007/978-3-031-62316-5 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Advances in Artificial Intelligence-Empowered Decision Support Systems [electronic resource] : Papers in Honour of Professor John Psarras / edited by George A. Tsihrintzis, Maria Virvou, Haris Doukas, Lakhmi C. Jain. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 438 p. 191 illus., 134 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Learning and Analytics in Intelligent Systems, 2662-3455 ; 39 1. Introduction to advances in artificial intelligence-empowered decision support systems -- 2. Artificial Intelligence in Breast Cancer Diagnosis: A Review -- 3. Classification of H&E stained Liver Histopathology Images Using Ensemble Learning Techniques for detection of the level of malignancy of Hepatocellular Carcinoma (HCC) -- 4. Performance Analysis of Deep Learning Models on Chemokines Protein Group Using Structure-Based Pattern Detection -- 5. Dynamic and Personalized Access Control to Electronic Health Records. Decision Support Systems (DSSs) are Software and Information Systems which make use of various data and business models, employ advanced data analytics procedures, and access extensive databases and data warehouses to facilitate with a decision process or with organizational issues. DSSs have proven to be particularly useful at the strategic level, while they usually require only limited computer-proficiency skills from their users. Although DSSs have been under development and use for several decades, recent advances in both Software Engineering technologies and Artificial Intelligence (AI) methodologies have heralded new avenues for research and development in this field. This book exposes its readers to some of the most significant Advances in Artificial Intelligence-Empowered Decision Support Systems. It consists of an editorial note and an additional sixteen (16) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. The chapters are organized into five parts, namely (i) AI-Empowered DSS in Medical Diagnosis and Biology, (ii) AI-Empowered DSS in Healthcare and Health Insurance, (iii) AI-Empowered DSS in Urban Matters, (iv) Various Applications of AI-Empowered DSS, and (v) Novel AI-Empowered Methodologies in Decision Making. Targeted toward academics, researchers, practitioners, and students in Computer Science, Artificial Intelligence, and Management, this book is also accessible to individuals from other disciplines interested in the cutting-edge developments of AI-empowered DSS technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into the application areas of interest to them. Computational intelligence. 64052 Machine learning. 55059 Computational Intelligence. 64052 Machine Learning. 55059 Tsihrintzis, George A. editor. edt http://id.loc.gov/vocabulary/relators/edt 79430 Virvou, Maria. editor. edt http://id.loc.gov/vocabulary/relators/edt 70348 Doukas, Haris. editor. edt http://id.loc.gov/vocabulary/relators/edt 69952 Jain, Lakhmi C. editor. edt http://id.loc.gov/vocabulary/relators/edt 79431 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031623158 Printed edition: 9783031623172 Printed edition: 9783031623189 Learning and Analytics in Intelligent Systems, 2662-3455 ; 39 69955 https://doi.org/10.1007/978-3-031-62316-5 ZDB-2-INR ZDB-2-SXIT 61075 61075