The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere

Bacanin, Nebojsa; Perisic, Mirjana; Jovanovic, Gordana; Damaševičius, Robertas; Stanisic, Svetlana; Simic, Vladimir; Zivkovic, Miodrag; Stojic, Andreja

doi:10.1016/j.scitotenv.2024.172195

Cited by 5 publications

(1 citation statement)

References 64 publications

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“…Moreover, hybrid methods between machine/deep learning and metaheuristics excel in other application domains as well, as evidenced by numerous successful recent applications including medicine [22,13,8,27,32,6,24], agriculture [25], environmental monitoring [5,20], economy [13,41,38] and power grids [29,14,3,39,45]. Other notable applications include weather forecasting [21], cloud computing [7,33,4,9], wireless sensor networks [46,11,44] and intrusion detection [35,36,23,15].…”

Section: Related Workmentioning

confidence: 99%

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

Petrovic,

Bacanin,

Jovanovic

et al. 2024

Int. J. Robot. Autom. Technol.

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This work aims to test the performance of the you only look once version 8 (YOLOv8) model for the problem of drone detection. Drones are very slightly regulated and standards need to be established. With a robust system for detecting drones the possibilities for regulating their usage are becoming realistic. Five different sizes of the model were tested to determine the best architecture size for this problem. The results indicate high performance across all models and that each model is to be used for a specific case. Smaller models are suited for lightweight system approaches where some false identification is tolerable, while the largest models are to be used with stationary systems that require the best precision.

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