Cited by Enhancing Air Pollution Forecasting with LSTM and a Binary Chimp Optimization Algorithm

Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification DOI

Anurag Barthwal, Amit Kumar Goel

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(2), P. 2935 - 2955

Published: Feb. 2, 2024

Language: Английский

Citations

An Improved Chaotic Game Optimization Algorithm and Its Application in Air Quality Prediction DOI

Yanping Liu,

Ruili Zheng,

Bohao Yu

et al.

Axioms, Journal Year: 2025, Volume and Issue: 14(4), P. 235 - 235

Published: March 21, 2025

Air pollution poses significant threats to public health and ecological sustainability, necessitating precise air quality prediction facilitate timely preventive measures policymaking. Although Long Short-Term Memory (LSTM) networks demonstrate effectiveness in prediction, their performance critically depends on appropriate hyperparameter configuration. Traditional manual parameter tuning methods prove inefficient prone suboptimal solutions. While conventional swarm intelligence algorithms have been proved be effective optimizing the hyperparameters of LSTM models, they still face challenges accuracy model generalizability. To address these limitations, this study proposes an improved chaotic game optimization (ICGO) algorithm incorporating multiple improvement strategies, subsequently developing ICGO-LSTM hybrid for Chengdu’s prediction. The experimental validation comprises two phases: First, comprehensive benchmarking 23 mathematical functions reveals that proposed ICGO achieves superior mean values across all test optimal variance metrics 22 functions, demonstrating enhanced global convergence capability algorithmic robustness. Second, comparative analysis with seven swarm-optimized models six machine learning benchmarks dataset shows model’s performance. Extensive evaluations show minimal error metrics, MAE = 3.2865, MAPE 0.720%, RMSE 4.8089, along exceptional coefficient determination (R2 0.98512). These results indicate significantly outperforms predictive reliability, suggesting substantial practical implications urban environmental management.

Language: Английский

Citations

Ensemble Learning-Based Approach for Forecasting Inventory Data in Prefabricated Component Warehousing DOI

Shuo Lin,

Xianyu Huang, Shunchao Zhang

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1443 - 1443

Published: May 8, 2025

Accurately predicting the storage area of prefabricated components facilitates transshipment scheduling and prevents waste space. Due to influence numerous factors, precise prediction remains challenging. Currently, limited research has addressed areas for components, effective solutions are lacking. To address this issue, a GRU model with an attention mechanism based on ensemble learning was proposed. The employed Bo-Bi-ATT-GRU approach time series areas. A Bayesian optimization algorithm utilized enhance parameter tuning training efficiency, while framework improved stability. In study, port container dataset used experimentation, root mean square error (RMSE) absolute percentage (MAPE) as evaluation metrics. Compared GM model, R2 proposed by 3.38%. Experimental results demonstrated that learning-based offered superior performance in forecasting components.

Language: Английский

Citations

Feasibility Study for Construction Projects in Uncertainty Environment with Optimization Approach DOI

Ali Bagheri Khoulenjani,

Mohammadamin Talebi,

Elham Karim Zadeh

et al.

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

Applications of Machine Learning in Financial Accounting for Industrial Engineering: A Case Study on Cost Estimation and Forecasting DOI

Farzaneh Shoushtari,

Mojdeh Sadat Najafi Zadeh,

Hossein Ghafourian

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks DOI

Mehrdad Shoeibi,

Anita Ershadi Oskouei,

Masoud Kaveh

et al.

Future Internet, Journal Year: 2024, Volume and Issue: 16(12), P. 460 - 460

Published: Dec. 6, 2024

The rapid advancement of Internet Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT expand, the demand for energy-efficient, batteryless becomes increasingly critical sustainable future networks. These play a pivotal role in next-generation applications reducing dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder widespread adoption devices, including limited transmission range, constrained resources, low spectral efficiency receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer promising solution dynamically manipulating wireless propagation environment to enhance signal strength improve In this paper, we propose novel deep reinforcement learning (DRL) algorithm that optimizes phase shifts RISs maximize network’s achievable rate while satisfying devices’ constraints. Our DRL framework leverages six-dimensional chimp optimization (6DChOA) fine-tune hyper-parameters, ensuring efficient adaptive learning. proposed 6DChOA-DRL RIS received power mitigating interference from direct RIS-cascaded links. simulation results demonstrate our optimized design significantly improves data rates under system configurations. Compared benchmark algorithms, approach achieves higher gains harvested power, an improvement at transmit 20 dBm, lower root mean square error (RMSE) 0.13 compared 3.34 standard RL 6.91 DNN, indicating more precise shifts.

Language: Английский

Citations

Leveraging AI-Driven Predictive Analytics for Enhancing Resource Optimization and Efficiency in Agile Construction Project Management DOI

C.W. Lan

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

Integrating Artificial Intelligence and Predictive Analytics for Feasibility Studies in Construction Projects: An Optimization Approach in Uncertainty Environments DOI

C.W. Lan

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024