2026-05-04T20:59:03.661Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11820 2026-01-26T16:11:33Z 1

Modified Metaheuristics: Hyperparameter Running Application in Air Quality Estimation 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11820 https://ieeexplore.ieee.org/abstract/document/11240740 C. Varsandán L. Jovanovic V. Zeljkovic B. Radomirovic M. Zivkovic N. Bacanin Environmental protection plays an increasingly important role in modern post-industrial society. Environmental factors affect everyday life across the world, from health to economic development. Nevertheless, certain byproducts of industry are unavoidable, especially in developing economies. It is therefore fundamental to meticulously monitor and control pollutant levels to ensure the future of our planet. Accurate forecasting of pollution can help mitigate and plan industrial production in a way that reduces environmental impact in a timely and effective manner. This work explores the potential of artificial intelligence (AI) algorithms, specifically, long shortterm memory (LSTM) neural networks augmented with attention mechanisms-for pollutant forecasting using historical time series data. To ensure optimal performance, a modified metaheuristic algorithm based on the Bat Algorithm (BA) is introduced and used to tune the models’ hyperparameters. Simulations conducted on real-world data have demonstrated promising results, with R2 scores as high as 0.926548 achieved by models optimized using the proposed method. conferenceObject 34 37 10.1109/TELSIKS65061.2025.11240740 2025 17th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia