Incorporating Attention Mechanism Into CNN-BiGRU Classifier for HAR by Ohoud Nafea, Wadood Abdul, Ghulam Muhammad

“…The proposed methodology uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to extract the spatial and temporal features. …”
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Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review by G. H. Mohmad Dar, Radhakrishnan Delhibabu

“…But the development of deep learning techniques has completely changed the field. Models like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have shown that they are better at capturing the complex temporal and spectral features of speech. …”
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Artificial intelligence assisted wearable flexible sensors for sports: research progress in technology integration and application by Jie Wu, Zhiqi Mo, Xing Gao, Wanru Xin, Weiquan Shi, Jaeyoung Park

“…This article provides a comprehensive review of the latest advancements in artificial intelligence-assisted wearable flexible sensors for motion detection, focusing on the operational mechanisms, performance enhancements, and algorithm optimization of convolutional neural networks (CNN), temporal data modeling, multimodal fusion technology, and natural language generation. …”
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Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

“…In this paper, we propose an advanced hybrid model for Convolutional and Long Short-Term Memory (CNN-LSTM), which exploits the main advantages of convoluted neural networks and LSTM networks. …”
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Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis by Zhengzheng Jiang, YaWen Shen

“…Our method introduces a hybrid model combining convolutional neural networks for spatial feature extraction and transformer-based temporal attention for sequential pattern recognition. …”
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Innovative Approaches to Traffic Anomaly Detection and Classification Using AI by Borja Pérez, Mario Resino, Teresa Seco, Fernando García, Abdulla Al-Kaff

“…This review provides a comprehensive analysis of recent advancements in artificial intelligence methods applied to traffic anomaly detection, including convolutional and recurrent neural networks (CNNs and RNNs), autoencoders, Transformers, generative adversarial networks (GANs), and multimodal large language models (MLLMs). …”
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Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

“…This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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Research and analysis of the TCN-Multihead-Attention prediction model of landslide deformation in the Three Gorges Reservoir area, China by Huan Chen, Huan Chen, Yixuan Li, Yimin Liu

“…This paper proposes a TCN-Multihead-Attention prediction model for landslide deformation based on temporal convolutional networks (TCNs). We collected 8 years of monitoring data from the Huangniba Dengkan landslide in the Three Gorges Reservoir area, including surface deformation (horizontal displacement and elevation), rainfall, and reservoir levels. …”
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Integration of Deep Learning Architectures With GRU for Automated Leukemia Detection in Peripheral Blood Smear Images by Amit Kumar Bairwa, Anita Shrotriya, Priyanka Mathur, Sandeep Joshi, Sakshi Shringi, Ambika Kumari

“…This exceptional result underscores the efficacy of combining Convolutional Neural Networks (CNNs) with RNNs, particularly GRUs, in accurately detecting Leukemia from PBS images. …”
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Deep learning-based research on fault warning for marine dual fuel engines by Lingkai Meng, Huibing Gan, Haisheng Liu, Daoyi Lu

“…The model integrated convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and Kolmogorov-Arnold networks (KAN) to perform feature extraction from multi-dimensional time series data, autonomously identify temporal patterns within the data, and directly learn parameterized nonlinear activation functions, respectively. …”
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Real-Time Player Engagement Measurement Using Nonintrusive Game Telemetry by Ammar Rashed, Shervin Shirmohammadi, Mohamed Hefeeda

“…Our approach combines graph convolutional networks for modeling player interactions with Transformer networks for temporal processing, enabling indirect measurement of both player skill and game challenge, which in turn are used to classify player engagement. …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…The model integrates Convolutional Neural Networks (CNNs) to extract spatial features, Long Short-Term Memory (LSTM) networks to model sequential dependencies, Transformer-based attention mechanisms to dynamically weigh environmental factors, and a Multi-Layer Perceptron (MLP) for incorporating vessel-specific and other residual features. …”
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Intention Recognition of AAV Swarm Based on GAT-EPool-BiGRU Model by Jiajun Yuan, Xiang Jia, Yu An, Liang Geng, Lei Shu

“…Addressing the limitations of existing methods—such as the low feature transfer efficiency of stacked autoencoders (SAE) and the tendency of panoramic convolutional long short-term memory networks (PC-LSTM) to lose tactical details—this paper proposes a novel deep learning model called GAT-EPool-BiGRU, which integrates Graph Attention Networks (GAT), Edge Pooling (EPool), and Bidirectional Gated Recurrent Units (BiGRU). …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Research on freeze-thaw displacement prediction model of sandy soil based on attention mechanism CNN-BiGRU by Zecheng Wang, Dongwei Li, Zhengbin Dong, Zhiwen Jia, Chaochao Zhang

“…This study develops an attention-based CNN-BiGRU model that synergizes convolutional neural networks for spatial feature extraction, bidirectional gated recurrent units for temporal dependency modeling, and attention mechanisms for critical time-step weighting. …”
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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The proposed approach (AM-CNN-LSTM) combined a one-dimensional convolutional neural network (1D-CNN) to capture local dependencies in sequences, the temporal data processing capability of long short-term memory (LSTM), and the interpretability of the AM. …”
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Simulation and Identification of the Habitat of Antarctic Krill Based on Vessel Position Data and Integrated Species Distribution Model: A Case Study of Pumping-Suction Beam Trawl... by Heng Zhang, Yuyan Sun, Hanji Zhu, Delong Xiang, Jianhua Wang, Famou Zhang, Sisi Huang, Yang Li

“…The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. …”
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A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals by Sana Yasin, Muhammad Adeel, Umar Draz, Tariq Ali, Mohammad Hijji, Muhammad Ayaz, Ashraf M. Marei

“…In this study, we propose a CNN-Transformer fusion model that leverages the complementary strengths of Convolutional Neural Networks (CNNs) and Transformer-based architectures to process electroencephalogram (EEG) signals enriched with clinical and sentiment-derived features. …”
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Deep Learning for Video Fluoroscopic Swallowing Study Analysis: A Survey on Classification, Detection, and Segmentation Techniques by Ahmed Fakhry, Sarah Mary Antony, Eunhee Park, Jong Taek Lee

“…Classification methods utilizing convolutional neural networks achieve high accuracy, ranging from 91.7% to 95.98%, and Area Under the ROC Curve scores between 0.71 and 0.97, thus enhancing the consistency and reliability of swallowing phase identification. …”
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