LEADER 04470nam 22006015 450 001 9911047821403321 005 20251127082722.0 010 $a3-031-94386-4 024 7 $a10.1007/978-3-031-94386-7 035 $a(MiAaPQ)EBC32340636 035 $a(Au-PeEL)EBL32340636 035 $a(CKB)41603697900041 035 $a(OCoLC)1545643832 035 $a(DE-He213)978-3-031-94386-7 035 $a(EXLCZ)9941603697900041 100 $a20251127d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFeature Fusion for Next-Generation AI $eBuilding Intelligent Solutions from Medical Data /$fedited by Anindya Nag, Md. Mehedi Hassan, Anupam Kumar Bairagi 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (405 pages) 225 1 $aSustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development,$x3005-1770 311 08$a3-031-94385-6 327 $aFundamental Principles of Feature Fusion in Medical AI -- Data Preprocessing for Feature Synthesis in Medical AI -- Techniques for Selecting Features in Medical Data -- Dimensionality Reduction Techniques: Foundations and Applications in Medical Data Analysis -- Meta-Heuristic Algorithms for High-Dimensional Feature Selection. 330 $aThis book delves into the fundamental concepts, methodologies, and practical implementations of feature fusion, providing valuable perspectives on how merging several data aspects might augment the decision-making skills of artificial intelligence. Feature fusion is inherently connected to the advancement of intelligent solutions from medical data as it enables the incorporation of various and complementary data sources to construct more advanced AI models. Within the medical domain, data manifests in diverse formats, including electronic health records (EHRs), medical imaging, genomic data, and real-time sensor metrics. Although each of these data kinds offers distinct perspectives, they may have limitations in terms of their breadth or depth when considered independently. The application of feature fusion enables the integration of diverse data sources into a unified model, hence improving the AI's capacity to detect patterns, make precise predictions, and produce significant insights. The fusion process facilitates the development of intelligent solutions that exhibit enhanced reliability and effectiveness by using a more extensive reservoir of knowledge. For example, an artificial intelligence system that combines imaging data with clinical history might enhance the precision of disease diagnosis, forecast patient outcomes, and suggest tailored treatment strategies. Feature fusion is the crucial factor in unleashing the complete capabilities of medical data, enabling artificial intelligence to provide intelligent solutions that not only enhance the provision of healthcare but also stimulate advancements in medical research and practice. The proposed book explores the advanced notion of feature fusion within the field of artificial intelligence, with a particular emphasis on its implementation in physiological data. The integration of many data sources is crucial in the development of more precise, dependable, and understandable AI models as the healthcare industry becomes more data-driven. 410 0$aSustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development,$x3005-1770 606 $aArtificial intelligence 606 $aBiomedical engineering 606 $aQuantitative research 606 $aArtificial Intelligence 606 $aMedical and Health Technologies 606 $aData Analysis and Big Data 615 0$aArtificial intelligence. 615 0$aBiomedical engineering. 615 0$aQuantitative research. 615 14$aArtificial Intelligence. 615 24$aMedical and Health Technologies. 615 24$aData Analysis and Big Data. 676 $a006.3 700 $aNag$b Anindya$0999620 701 $aHassan$b Mehedi$01833381 701 $aBairagi$b Anupam Kumar$01862395 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911047821403321 996 $aFeature Fusion for Next-Generation AI$94468645 997 $aUNINA