Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm

Bacanin, Nebojsa; Simic, Vladimir; Zivkovic, Miodrag; Alrasheedi, Melfi; Petrovic, Aleksandar

doi:10.1007/s10479-023-05745-0

Cited by 8 publications

(1 citation statement)

References 103 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]. Also, it is worth mentioning that there are also many applications of enhanced metaheuristics methods [40,42].…”

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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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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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

Jovanovic,

Zivkovic,

Bacanin

et al. 2024

Neural Comput & Applic

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This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ($$R^2$$ R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach.

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Spatial-temporal memory enhanced multi-level attention network for origin-destination demand prediction

Lu,

Pan,

Ren

2024

Complex Intell. Syst.

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Origin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%.

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Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm

Cited by 8 publications

References 103 publications

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

Spatial-temporal memory enhanced multi-level attention network for origin-destination demand prediction

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