2026-05-09T11:42:50.218Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

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Performance Evaluation of Metaheuristics-Tuned Deep Neural Networks for HealthCare 4.0, Chapter in CCIS Communications in Computer and Information Science: ICCSST 2023: International Conference on Computational Sciences and Sustainable Technologies, Springer, volume 1973 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9773 https://link.springer.com/chapter/10.1007/978-3-031-50993-3_1 L. Jovanovic S. Golubovic N. Bacanin G. Kunjadic M. Antonijevic M. Zivkovic The emergence of novel technologies that power advanced networking, coupled with decreasing sizes and lower power demands of chips has given birth to the internet of things. This emerging technology has resulted in a revolution across many fields. A notably interesting application is healthcare where this combination has resulted in Healthcare 4.0. This has enabled better patient monitoring and resulted in more acquired patient data. Novel techniques are needed, capable of evaluating the gathered information and potentially aiding doctors in providing better outcomes. Artificial intelligence provides a promising solution. Methods such as deep neural networks (DDNs) have been used to address similarly difficult tasks with favorable results. However, like many modern algorithms DNNs present a set of control values that require tuning to ensure proper functioning. A popular approach for selecting optimal values is the use of metaheuristic algorithms. This work proposes a novel metaheuristic based on the sine cosine algorithm, that builds on the excellent performance of the original. The introduced approach is then tasked with tuning hyperparameter values of a DNN handling medical diagnostics. This novel approach has been compared to several state-of-the-art algorithms and attained excellent performance applied to three datasets consisting of real-world medical data. bookPart Springer, Cham 1 14 10.1007/978-3-031-50993-3_1 CCIS Communications in Computer and Information Science: ICCSST 2023: International Conference on Computational Sciences and Sustainable Technologies, volume 1973