A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification. by Hong-Jie Liang, Ling-Long Li, Guang-Zhong Cao

“…These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. …”
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Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou, Yanchao Zhang

“…To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. …”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…The proposed model effectively learns the intricate mapping relationship between sensory data and melt density by amalgamating these multi-source sensory inputs and using the feature extraction capabilities of convolutional neural networks and the temporal dependencies modeling capabilities of LSTM networks.Results and Discussions The application of the proposed method demonstrates significant efficiency in real-time monitoring of polymer melt density by monitoring the melt density during the PC/ABS blending extrusion process. …”
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Attention-based multi-scale convolution and conformer for EEG-based depression detection by Ze Yan, Ze Yan, Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng

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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…However, two challenges still exist in the existing methods: 1) Most of the existing spatio-temporal prediction tasks focus on extracting temporal information using recurrent neural networks and using convolution networks to extract spatial information, but ignore the fact that the forgetting of historical information still exists as the input sequence length increases. 2) Spatio-temporal sequence data have complex non-smoothness in both temporal and spatial, such transient changes are difficult to be captured by existing models, while such changes are often particularly important for the detail reconstruction in the image prediction task. …”
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EOST-LSTM: Long Short-Term Memory Model Combined with Attention Module and Full-Dimensional Dynamic Convolution Module by Guangxin He, Wei Wu, Jing Han, Jingjia Luo, Lei Lei

“…Aiming to address the limitations of existing deep learning methods in radar echo extrapolation, this paper proposes a spatio-temporal long short-term memory (LSTM) network model that integrates an attention mechanism and the full-dimensional dynamic convolution technique. …”
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Daily insider threat detection with hybrid TCN transformer architecture by Xiaoyun Ye, Huangrongbin Cui, Faqin Luo, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

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Tracking shoreline change using minimum convolution of Gaussian weight and squared differences by Hojun Yoo, Hyoseob Kim, Tae Soon Kang, Jin Young Park, Jong Beom Kim

“…Detecting and responding appropriately to temporal changes in the shoreline is an important task for protecting coasts. …”
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Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

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Research on Short-term Load Forecasting Algorithm Based on VMD and TCN by WANG Qing, CHEN Zhiru, LI Guimin, JING Zhen, ZHANG Zhi, WANG Pingxin, CUI Qi

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Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention by Sijia Fu, Rui Zhu, Feiyang Yu

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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience by Ibrahim Alrashdi, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Mohammed Farsi

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A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics by Xinle Zhang, Guowei Zhang, Shengqi Zhang, Hongfu Ai, Yongqi Han, Chong Luo, Huanjun Liu

“…In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). …”
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CAs-Net: A Channel-Aware Speech Network for Uyghur Speech Recognition by Jiang Zhang, Miaomiao Xu, Lianghui Xu, Yajing Ma

“…The proposed model consists of two key components: (1) the Channel Rotation Module (CIM), which reconstructs each frame’s channel vector into a spatial structure and applies a rotation operation to explicitly model the local structural relationships within the channel dimension, thereby enhancing the encoder’s contextual modeling capability; and (2) the Multi-Scale Depthwise Convolution Module (MSDCM), integrated within the Transformer framework, which leverages multi-branch depthwise separable convolutions and a lightweight self-attention mechanism to jointly capture multi-scale temporal patterns, thus improving the model’s perception of compact articulation and complex rhythmic structures. …”
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Rolling Based on Multi-Source Time–Frequency Feature Fusion with a Wavelet-Convolution, Channel-Attention-Residual Network-Bearing Fault Diagnosis Method by Tongshuhao Feng, Zhuoran Wang, Lipeng Qiu, Hongkun Li, Zhen Wang

“…Meanwhile, an efficient and lightweight deep learning model (WaveCAResNet) is constructed based on residual networks by integrating multi-scale analysis via a wavelet convolutional layer (WTConv) with the dynamic feature optimization properties of channel-attention-weighted residuals (CAWRs) and the efficient temporal modeling capabilities of weighted residual efficient multi-scale attention (WREMA). …”
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Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models by Ali Mohammadjafari, Maohua Lin, Min Shi

“…<b>Methods:</b> We compared multiple deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and transformer-based models BERT and BioBERT. …”
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Stressed Vegetation Identification Under Natural Gas Microleakage From Hyperspectral Images Using Stacked Autoencoder and Multiscale Three-Dimensional Convolutional Neural Network by Kangni Xiong, Jinbao Jiang, Weiwei Ran, Kangning Li, Yingyang Pan, Xinda Wang

“…Leveraging the abundant spectral and spatial information in hyperspectral imagery, a novel approach combining stacked autoencoder (SAE) and multiscale three-dimensional convolutional neural network (MS3D CNN) was proposed for stress identification of grass, soybean, corn, and wheat. …”
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