Cited by Walrus Optimization Algorithm for Panchromatic and Multispectral Image Fusion

Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City DOI

Qingchun Guo,

Zhenfang He,

Zhaosheng Wang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 25, 2025

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization urbanization, Liaocheng has experienced increasing ozone concentration over several years. Therefore, become a major environmental problem in City. Long short-term memory (LSTM) artificial neural network (ANN) models are established predict concentrations City from 2014 2023. The results show general improvement accuracy LSTM model compared ANN model. Compared ANN, an increase determination coefficient (R2), value 0.6779 0.6939, decrease root mean square error (RMSE) 27.9895 μg/m3 27.2140 absolute (MAE) 21.6919 20.8825 μg/m3. prediction is superior terms R, RMSE, MAE. In summary, promising technique for predicting concentrations. Moreover, by leveraging historical data enables accurate predictions future on global scale. This will open up new avenues controlling mitigating pollution.

Language: Английский

Citations

A spatiotemporal CNN-LSTM deep learning model for predicting soil temperature in diverse large-scale regional climates DOI

Vahid Farhangmehr, Hanifeh Imanian, Abdolmajid Mohammadian

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 968, P. 178901 - 178901

Published: Feb. 22, 2025

Language: Английский

Citations

Prediction of Crystalline Structure Evolution During Solidification of Aluminum at Different Cooling Rates Using a Hybrid Neural Network Model DOI

Rafi Bin Dastagir,

Saptaparni Chanda, Farsia Kawsar Chowdhury

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104578 - 104578

Published: March 1, 2025

Language: Английский

Citations

Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism DOI

Xinyong Xu,

Zhongkui Zhu,

Xiaonan Chen

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

Citations

Study on the Factors Influencing CO2 Hydrate Sequestration in CH4 Hydrate-Bearing Reservoirs DOI

Beù Ca Ùi Lôù,

Shuxia Li, H. Sun

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135777 - 135777

Published: March 1, 2025

Language: Английский

Citations

A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City DOI

Zhenfang He,

Qingchun Guo, Zhaosheng Wang

et al.

Toxics, Journal Year: 2025, Volume and Issue: 13(4), P. 254 - 254

Published: March 28, 2025

Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Language: Английский

Citations

A machine learning computational approach for the mathematical anthrax disease system in animals DOI

Zulqurnain Sabir, Eman Simbawa

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320327 - e0320327

Published: April 1, 2025

Objectives The current research investigations present the numerical solutions of anthrax disease system in animals by designing a machine learning stochastic procedure. mathematical is classified into susceptible, infected, recovered and vaccinated. Method A Runge-Kutta solver applied to collect dataset, which decreases mean square error dividing training as 78%, testing 12% verification 10%. proposed computing technique performed through logistic sigmoid activation function, single hidden layer construction, twenty-seven numbers neurons, optimization Bayesian regularization for animals. Finding designed procedure’s correctness authenticated results overlapping reducible absolute error, are calculated around 10 -05 -08 each case model. best performances -10 -12 Moreover, statistical terms regression coefficient, histogram, state transition values enhance reliability approach. Novelty scheme not before get

Language: Английский

Citations

CucuNetCNNs: Application of novel ensemble deep neural networks for classification of cucumber leaf disease DOI

Muhammet Emin Şahin, Umut Özkaya, Çağrı Arısoy

et al.

Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(5), P. 103380 - 103380

Published: April 1, 2025

Language: Английский

Citations

BSANet: A Bilateral Segregation and Aggregation Network for real-time cloud segmentation DOI

Yijie Li, Hewei Wang, Shaofan Wang

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101536 - 101536

Published: April 1, 2025

Language: Английский

Citations

Walrus Optimization Algorithm for Panchromatic and Multispectral Image Fusion DOI

R. Dileep,

J. Jayanth,

A.L. Choodarathnakar

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101562 - 101562

Published: April 1, 2025

Language: Английский

Citations