FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior by Xiang-Rui Huang, Liang-Bi Chen

“…Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. …”
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A Hybrid Strategy-Improved SSA-CNN-LSTM Model for Metro Passenger Flow Forecasting by Jing Liu, Qingling He, Zhikun Yue, Yulong Pei

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Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian, Chongchong Yu

“…Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time–spectral features. …”
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Automatic Number Plate Detection and Recognition System for Small-Sized Number Plates of Category L-Vehicles for Remote Emission Sensing Applications by Hafiz Hashim Imtiaz, Paul Schaffer, Paul Hesse, Martin Kupper, Alexander Bergmann

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An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model by Rajesh Kannan Megalingam, Kariparambil Sudheesh Sankardas, Sakthiprasad Kuttankulangara Manoharan

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Artificial intelligence in acoustic ecology: Soundscape classification in the Cerrado by Bruno Daleffi da Silva, Linilson Rodrigues Padovese

“…The conclusion is that it is possible to classify different Cerrado formations through their acoustic landscape, and the choice of the optimal model for classification should consider a balance between accuracy, operational complexity, and efficiency. …”
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Origin-destination prediction from road average speed data using GraphResLSTM model by Guangtong Hu, Jun Zhang

“…Using this generated dataset, carefully designed comparative experiments are conducted to compare various different models and data types. The results clearly demonstrate that both the GraphResLSTM model and the road average speed data markedly outperform alternative models and data types in OD prediction.…”
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Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living by Mustufa Haider Abidi, Hisham Alkhalefah, Zeyad Almutairi

“…Lastly, the efficacy of the suggested strategy is validated with different measures. From the experiments, the proposed system attains standard results in terms of improved system performance and accuracy that can aid in significantly recognizing human movements.…”
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UniLF: A novel short-term load forecasting model uniformly considering various features from multivariate load data by Shiyang Zhou, Qingyong Zhang, Peng Xiao, Bingrong Xu, Geshuai Luo

“…Experiments conducted on three load datasets from Australia, Panama and Austria show that UniLF achieves superior forecasting accuracy with competitive practical efficiency under different prediction lengths, providing a new solution for STLF.…”
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Research on the Application of Deep Learning Algorithm in the Damage Detection of Steel Structures by Qingyun Ge, Caimei Li, Fulian Yang

“…Transfer learning strategies were successfully implemented to adapt the model to different structural contexts, addressing the challenge of limited labeled data. …”
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Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions by S. Abinaya, K. S. Ashwin, A. Sherly Alphonse

“…This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. …”
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CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv... by Xiaozong Qiu, Guohua Yan, Lihua Yin

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Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation by Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…Here, a customized Convolutional Neural Network (CNN) model is proposed for cervical cancerous cell detection. …”
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A spatio-temporal fusion-based approach for multi-dimensional classification of Parkinson’s disease progression using multi-modal dataset by Vinay Kukreja, Vandana Ahuja, Modafar Ati, Hariharan Shanmugasundaram, Murugaperumal Krishnamoorthy, Rishabh Sharma, Abhishek Bhattacherjee

“…Context: The progressive neurodegenerative disorder Parkinson’s disease (PD) features diverse symptom presentation that progresses at different speeds and demands effective disease classification with precise patient management. …”
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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

“…Despite challenges like missing data and non-stationary WQ patterns, the dual-path LSTM tuning approach effectively transfers and fine-tunes knowledge from historical records to improve prediction accuracy across different temporal domains. The model maintains a MAPE below 5 % and KGE values between 0.36 and 0.67, demonstrating robust performance in multi-step WQI forecasting. …”
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Hybrid Multi-Branch Attention–CNN–BiLSTM Forecast Model for Reservoir Capacities of Pumped Storage Hydropower Plant by Yu Gong, Hao Wu, Junhuang Zhou, Yongjun Zhang, Langwen Zhang

“…In order to better distinguish the effects of different data types on the reservoir capacity, the correlation between data and reservoir capacity is analyzed using the Spearman coefficient, and a multi-branch forecast model is established based on the correlation. …”
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HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification by Wanying Song, Yifan Cong, Shiru Zhang, Yan Wu, Peng Zhang

“…Fusing features from different feature descriptors or different convolutional layers can improve the understanding of scene and enhance the classification accuracy. …”
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Boosting Arabic text classification using hybrid deep learning approach by Eman Alnagi, Rawan Ghnemat, Qasem Abu Al-Haija

“…Lastly, comparing with the state-of-the-art models revealed the superiority of our hybrid model, which outperformed the other architectures in the same area of study, the accuracies have been improved by 1% to 30% for the different datasets.…”
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From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading by Andreas Kardoulias, Alexandros Arailopoulos, Panagiotis Seventekidis

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Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network by Shaoyang Yin, Zhaohui Chen, Wanyuan Liu, Zhiwen Su

“…The experimental results show that compared with single models, the proposed model performs better in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared in three different scenarios, proving that the model has high prediction accuracy and good robustness in ultra-short-term charging load prediction.…”
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