State of health prediction of lithium-ion batteries in charging chambers based on multi-modal deep learning by ZHAO Yinghua, CHEN Anbi, ZHANG Zengyu, LI Wenzhong, HAN Yu

“…A multi-modal deep learning network model, TCN-BiLSTM-Transformer, was constructed, leveraging a multi-level feature extraction mechanism for efficient processing of temporal signals. The Temporal Convolutional Network (TCN), utilizing dilated convolutional kernels with an exponential expansion rate, captured multi-scale local features while preserving temporal integrity. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…Accordingly,this study proposed a prediction model for water inflow along the mining face in the studied mine based on the temporal convolutional network (TCN), long short-term memory (LSTM), and support vector machine (SVM)—the TCN-LSTM-SVM model. …”
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Intelligent identification of ballastless track subgrade settlement based on vehicle-rail vibration data by Chong Li, Yu Guo

“…In this study, we propose a novel deep learning approach based on a convolutional neural network and long short-term memory (CNN-LSTM) model to accurately identify uneven subgrade settlement by analyzing vehicle-rail dynamic response data. …”
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LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones by Ruokun Qu, Zhiyuan Wang, Yelu Liu, Chenglong Li, Hui Jiang, Chen Fang

“…To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a robust feature extraction network tailored for degraded environments, and a lightweight pose estimation algorithm that fuses geometric and convolutional features. Extensive evaluations on both real-world and synthetic low-light datasets reveal significant improvements in accuracy, noise resilience, and adaptability to dynamic lighting. …”
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MHS-Net: A multi-scale heterogeneous synergistic network for single image deraining by Lingfeng Yuan, Minghong Xie

“…The second is the Dynamic Perception Adaptive Fusion (DPAF) strategy, which utilizes learnable masks to spatially separate features, reducing fusion artifacts and improving color consistency. …”
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SE-DeepLabV3+: Cervical Cell Segmentation and Classification Using a Novel SE-Based DeepLabV3+ and Ensemble Method by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

“…The classification model utilizes an ensemble approach by combining features from multiple pre-trained convolutional neural networks (CNNs) and introducing learnable weights (dense layers applied to the global average pooling layer) to dynamically adjust the importance of each model’s feature and improve prediction accuracy. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
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KSC-Net: a biologically inspired spatio-temporal correlation network for video-based human action recognition by Hui Ma, Xuelian Ma

“…Abstract Video-based action recognition remains a challenging task due to the difficulty in accurately modeling spatio-temporal dynamics and distinguishing foreground motion from static background clutter. …”
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Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

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Multi-Scale Long- and Short-Range Structure Aggregation Learning for Low-Illumination Remote Sensing Imagery Enhancement by Yu Cao, Yuyuan Tian, Xiuqin Su, Meilin Xie, Wei Hao, Haitao Wang, Fan Wang

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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

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Research on Breast Cancer Detection Methods Based on ODMV-MulDyHead-YOLO by Yanhong Zhang, Pei Li, Yihua Lan, Xiao Jia, Yingjie Lv

“…In this paper, we proposed a method of breast cancer detection based on full-dimensional dynamic convolution and multiple attention mechanism to solve the problems of missing detection and low detection accuracy caused by breast tumor occlusion by breast muscle, poor contrast between tumor and surrounding glandular tissue and indistinct features of small tumor. …”
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Intelligent Detection Algorithm for Concrete Bridge Defects Based on SATH–YOLO Model by Lanlin Zou, Ao Liu

“…Lastly, a lightweight TDMDH detection head with shared convolution and dynamic feature selection further reduced computational costs. …”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. …”
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HIDS-RPL: A Hybrid Deep Learning-Based Intrusion Detection System for RPL in Internet of Medical Things Network by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…The suggested model, designated HIDS-RPL, results from the hybridization of the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM), typically employed for sequence data prediction. …”
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Surface Deformation Monitoring and Prediction of Longtantian Open-Pit Mine Based on SBAS-InSAR and CNN-BiLSTM Techniques by Xiaoxiao Zhang, Qi Chen, Mengshi Yang, Zhifang Zhao, Yu Zheng, Qixue Dai, Yang He, Dayu Cai, Ting Xu

“…Considering the spatiotemporal heterogeneity, dynamic time warping based K-means clustering analysis was applied to divide the area into subspaces with similar deformation trends. …”
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A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency by Jinwoo Song, Seong Hee Cho, Seokgoo Kim, Jongwhoa Na, Joo-Ho Choi

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DBFormer: A Dual-Branch Adaptive Remote Sensing Image Resolution Fine-Grained Weed Segmentation Network by Xiangfei She, Zhankui Tang, Xin Pan, Jian Zhao, Wenyu Liu

“…This approach integrates the following techniques: (1) a dynamic context aggregation branch (DCA-Branch) with adaptive downsampling attention to model long-range dependencies and suppress background noise, and (2) a local detail enhancement branch (LDE-Branch) leveraging depthwise-separable convolutions with residual refinement to preserve and sharpen small weed edges. …”
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Modeling Marine Geoid in the China Seas and Its Adjacent Ocean Based on Satellite Altimeter-Derived Gravity Anomaly Model by Huiying Zhang, Xin Liu, Zhen Li, Xiaotao Chang, Heping Sun, Hui Li, Jinyun Guo

“…Compared with the mean dynamic topography of DTU22MDT, the difference between the two mean dynamic topography was basically within the range of centimeters. …”
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Tomato leaf disease detection method based on improved YOLOv8n by Ming Chen, Chunping Wang, Chengwei Liu, Ying Yu, Yuan Yuan, Jiaxuan Ma, Kaisheng Zhang

“…To address this issue, this paper proposes an optimized YOLOv8n algorithm, incorporating a C2f-DynamicConv optimization module. By dynamically adjusting the weights of convolutional kernels, the model can adapt to the characteristics of different input data, thereby enhancing its ability to represent diverse features. …”
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