Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0310296 - e0310296

Published: Jan. 14, 2025

Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock due to its ability address long-term dependence and transmission of historical time signals series data. However, manual tuning LSTM parameters significantly impacts model performance. PSO-LSTM leveraging PSO’s efficient swarm intelligence strong optimization capabilities proposed this article. experimental results on six global indices demonstrate that effectively fits real data, achieving high accuracy. Moreover, increasing PSO iterations lead gradual loss reduction, which indicates PSO-LSTM’s good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance PSO-LSTM. Furthermore, impact different retrospective periods accuracy finding consistent across varying spans are. Conducted experiments.

Language: Английский

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Scheduling optimization based on particle swarm optimization algorithm in emergency management of long-distance natural gas pipelines

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317737 - e0317737

Published: Feb. 10, 2025

This paper aims to solve the scheduling optimization problem in emergency management of long-distance natural gas pipelines, with goal minimizing total time. To this end, objective function minimum time is established, and relevant constraints are set. A model based on particle swarm (PSO) algorithm proposed. In view high-dimensional complexity local optimal problems, neighborhood adaptive constrained fractional (NACFPSO) used it. The experimental results show that compared traditional algorithm, NACFPSO performs well both convergence speed time, an average 81.17 iterations 200.00 minutes; while 82.17 207.49 minutes. addition, increase pipeline complexity, can still maintain its advantages especially which further verifies effect management.

Language: Английский

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Artificial Neural Networks with Soft Attention: Natural Language Processing for Phishing Email Detection Optimized with Modified Metaheuristics

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 421 - 438

Published: Jan. 1, 2025

Language: Английский

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Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 2, 2024

Language: Английский

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Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 122377 - 122400

Published: Jan. 1, 2024

Language: Английский

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Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

International Journal of Robotics and Automation Technology, Journal Year: 2024, Volume and Issue: 11, P. 1 - 12

Published: May 22, 2024

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

Language: Английский

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

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(24), P. 14727 - 14756

Published: May 10, 2024

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

Language: Английский

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Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 233, P. 104048 - 104048

Published: Nov. 7, 2024

Language: Английский

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A user demand acquisition method for cloud services based on user sentiment analysis and long- and short-term preferences

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103122 - 103122

Published: Jan. 24, 2025

Language: Английский

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Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 383 - 383

Published: March 3, 2025

Cloud computing offers scalable and adaptable resources on demand, has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud because of dynamic changes in load requirement. Existing forecasting approaches are unable the intricate temporal symmetries nonlinear patterns workload data, leading degradation prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques Accurate Workload Resource Time Series Prediction Computing Systems (MASNN-WL-RTSP-CS) proposed. Here, input data from Google cluster trace dataset were preprocessed using Multi Window Savitzky–Golay Filter (MWSGF) remove noise while preserving important maintaining structural symmetry time series trends. Then, (MASNN) effectively models symmetric fluctuations predict resource series. To enhance accuracy, Secretary Bird Algorithm (SBOA) was utilized optimize MASNN parameters, ensuring accurate predictions. Experimental results show that MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, 28.93% Square (MSE), 24.54%, 23.65%, 23.62% Absolute (MAE) compared other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, DCRNN-RUP-RP-CCE, respectively. These advances emphasize utility achieving more forecasts, thereby facilitating effective management.

Language: Английский

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