Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Further augmenting its capability, TGAMTSA utilizes a Hybrid Architecture that synergistically combines 1D Convolutional Neural Networks (CNNs) and a dual arrangement of Quad Long Short-Term Memory (LSTM) and Quad Gated Recurrent Units (GRU) networks. …”
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Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism by Hanieh Mohammadi, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari

“…Our proposed model leverages a hybrid architecture of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) layers, and an attention mechanism, enabling refined spatial and temporal feature extraction to enhance BP estimation accuracy. …”
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LMD²F-Net: Layered Multi-Scale Dual-Branch Dual-Temporal Fusion Network for Medical Image Segmentation by Siyuan Ye, Yan Wei

“…Image segmentation techniques play a crucial role in medical image analysis, directly impacting disease diagnosis, treatment planning, and efficacy evaluation. Although Convolutional Neural Networks (CNNs) and transformer-based approaches have made significant progress in this area, the inherent complexity of medical images, which include features such as low contrast, fuzzy boundaries, and noise, makes automated segmentation tasks challenging. …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…Through comprehensive experiments on the LC25000 dataset, DC-CAD achieves 99.52% accuracy, significantly outperforming baseline models such as standard Capsule Networks and Convolutional Neural Networks. The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. …”
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SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory by Yanzhao Ren, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, Ruijun Liu

“…In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…As such, our model uses an “atrous” convolution operation to extract multi-scale temporal information and a cascade network structure that progressively improves the attribute representations across layers. …”
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Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…However, existing neural networks based on electroencephalogram (EEG) decoding primarily focus on temporal and spatial characteristics while neglecting frequency characteristics. …”
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Bio-inspired motion detection models for improved UAV and bird differentiation: a novel deep learning framework by Najiba Said Hamed Al-Zadjali, Sundaravadivazhagan Balasubaramanian, Charles Savarimuthu, Emanuel O. Rances

“…The model consists of three core components: a Bio-Inspired Convolutional Neural Network (Bio-CNN) for spatial feature extraction, Gated Recurrent Units (GRUs) for capturing temporal motion dynamics, and a novel Bio-Response Layer that adjusts attention based on movement intensity, object proximity, and velocity consistency. …”
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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. …”
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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification by Xin Deng, Huaxiang Huo, Lijiao Ai, Daijiang Xu, Chenhui Li

“…Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. …”
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A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…Additionally, long-term temporal dependence is modeled by a temporal convolution network (TCN) to enhance the extraction capability of temporal features of EEG signals. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.…”
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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…The performance of the proposed CGRU model is compared with that of other state-of-the-art models such as Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Neural Network (DNN). …”
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Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network by Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen

“…Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. …”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

“…At the same time, the framework uses wavelet-based multi-frequency decomposition to clearly divide signals into trend, periodic, and noise components, and enhances feature representation via frequency-domain specific convolutions. Lastly, a gating network dynamically balances temporal and frequency-domain features to achieve cross-domain information integration. …”
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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. …”
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Medium density EMG armband for gesture recognition by Eisa Aghchehli, Eisa Aghchehli, Milad Jabbari, Chenfei Ma, Matthew Dyson, Kianoush Nazarpour

“…To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. …”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. …”
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A Novel Maximum Power Point Inference Method for Distributed Marine Photovoltaic Monitoring by Yujie Chen, Jianan Wang, Lele Peng, Jiachen Qiao

“…Second, Multilayer Convolutional Neural Networks (CNN) are constructed to extract the local high-frequency motion characteristics, Squeeze and Excitation Attention (SE-Attention) is employed to capture the global low-frequency motion characteristics, and Long Short-Term Memory (LSTM) is utilized to perform temporal modeling of the motion characteristics. …”
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Emotion recognition with a Randomized CNN-multihead-attention hybrid model optimized by evolutionary intelligence algorithm by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Filippo Sanfilippo

“…To address these challenges, we propose an innovative emotion recognition framework that integrates a Randomised Convolutional Neural Network (RCNN) with a Multi-Head Attention model, further optimized by the Football Team Training Algorithm (FTTA) metaheuristic to enhance network parameters effectively. …”
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