Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data DOI

Lakshmanaprakash Sanmugaraja,

Pandiaraj Annamalai

Network Computation in Neural Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 25

Published: Oct. 21, 2024

The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because the difficulty in obtaining annotated training data. This paper aims to enhance Twitter data by developing a robust model that improves accuracy computational efficiency. proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm Sentiment Classification Data (SCTD-THDCNN-SFOA) utilizes Stanford Treebank dataset. process begins pre-processing steps including Tokenization, Stop words Elimination, Filtering, Hashtag Removal, Multiword Grouping. Gray Level Co-occurrence Matrix Window Adaptive is employed extract features, such as emoticon counts, punctuation gazetteer word existence, n-grams, part speech tags. These features are selected using Entropy–Kurtosis-based Feature Selection approach. Finally, enhanced used categorize positive, negative, neutral sentiments. SCTD-THDCNN-SFOA demonstrates superior performance, achieving higher lesser computation time than existing models, respectively. framework significantly efficiency

Language: Английский

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

9
A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models DOI Open Access
Qingchun Guo,

Zhenfang He,

Zhaosheng Wang

et al.

Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2870 - 2870

Published: Oct. 9, 2024

Climate change affects the water cycle, resource management, and sustainable socio-economic development. In order to accurately predict climate in Weifang City, China, this study utilizes multiple data-driven deep learning models. The data for 73 years include monthly average air temperature (MAAT), minimum (MAMINAT), maximum (MAMAXAT), total precipitation (MP). different models artificial neural network (ANN), recurrent NN (RNN), gate unit (GRU), long short-term memory (LSTM), convolutional (CNN), hybrid CNN-GRU, CNN-LSTM, CNN-LSTM-GRU. CNN-LSTM-GRU MAAT prediction is best-performing model compared other with highest correlation coefficient (R = 0.9879) lowest root mean square error (RMSE 1.5347) absolute (MAE 1.1830). These results indicate that method a suitable model. This can also be used surface modeling. will help flood control management.

Language: Английский

Citations

16
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

2
A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City DOI Creative Commons

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

1
Prediction of Crystalline Structure Evolution During Solidification of Aluminum at Different Cooling Rates Using a Hybrid Neural Network Model DOI Creative Commons

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

0
Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards DOI

Sameera Kuppam,

P. Swarnalatha

Network Computation in Neural Systems, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 37

Published: March 24, 2025

Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar in apple plants using Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads simulated Internet Things (IoT) networks are selected Fractional Lion Optimization (FLION), images pre-processed with Gaussian filter segmented DeepJoint model. The TSSCA, combining Algorithm (TSA) (SCA), enhances classifier's effectiveness. Moreover, Pathology 2020 - FGVC7 dataset is used this work. designed classification trees. TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, 0.0442J maximal energy, significant improvements over existing approaches. Additionally, proposed model demonstrates superior outperforming methods 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, 0.64% Multidimensional Feature Compensation neural network (MDFC ResNet), Convolutional Neural (CNN), Multi-Context Fusion (MCFN), Advanced Segmented Dimension Extraction (ASDE), DRN, fuzzy convolutional (FCDCNN), ResNet9-SE, Capsule (CapsNet), IoT-based scrutinizing model, Multi-Model (MMF-Net).

Language: Английский

Citations

0
CucuNetCNNs: Application of novel ensemble deep neural networks for classification of cucumber leaf disease DOI Creative Commons
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

0
BSANet: A Bilateral Segregation and Aggregation Network for real-time cloud segmentation DOI Creative Commons
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

0
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

0
The application of integrated deep learning models with the Assistance of meteorological factors in forecasting major tobacco diseases DOI
Chen Yuan, Changcheng Li, Can Wang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110429 - 110429

Published: April 24, 2025

Language: Английский

Citations

0