Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model by Xiaodong Huang, Xiaojian Hao, Baowu Pan, Shaogang Chen, Shenxiang Feng, Pan Pei

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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…Firstly, the Spearman correlation coefficient is used to analyze the spatial-temporal correlation of power load in different regions and construct the Spearman adjacency matrix. …”
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3D densely connected CNN with multi-scale receptive fields and hybrid loss for brain tumor segmentation by Fatemehzahra Adib, Maryam Amirmazlaghani, Mohammad Rahmati

“…This paper presents a 3D convolutional neural network (CNN) model to automatically segment brain tumors from MRI scans. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…The core of the model combines temporal convolutional networks (TCNs) and bidirectional gated recurrent units (BiGRUs). …”
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Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…This paper presents a prediction method of ship heave motion based on the particle swarm optimization (PSO) and convolutional neural network–long short-term memory (CNN-LSTM) hybrid prediction model. …”
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Deep learning-based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study: A case study: Part A: Deep learning-ba... by Thankappan Sasilatha, Doss Hebciba Mary Suvakeen Amala, Rajendran Susai Santhammal, Časlav Lačnjevac, Gurmeet Singh

“…And also we compare the performance result by given the input images in different validation level. In first input image is initially preprocessed and that images is given to the KFCM-Segmentation. …”
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A Novel Electrical Load Forecasting Model for Extreme Weather Events Based on Improved Gated Spiking Neural P Systems and Frequency Enhanced Channel Attention Mechanism by Yuanshuo Guo, Jun Wang, Yan Zhong, Tao Wang, Zeyuan Sui

“…In this study, we develop an innovative membrane computing model, termed frequency attention temporal convolutional network-load forecasting-frequency attention gated spiking neural P (FATCN-LF-FAGSNP) model. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…In addition, experiments are conducted not only on the main dataset but also extended to January and August data, which represent seasonal differences, for generalization to verify the re-liability and broad applicability of the model. …”
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Research on series arc fault detection method household loads based on voltage signals by Bin Li, Jiahui Shu, Feifan Cui

“…Abstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution operation, and combines bidirectional long short-term memory recurrent network (BiLSTM) with attention mechanism. …”
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Detection of Apple Leaf Diseases using Faster R-CNN by Yunus Özen, Melike Sardoğan, Adem Tuncer

“…In this study, anapple leaf disease detection system was proposed using Faster Region-BasedConvolutional Neural Network (Faster R-CNN) with Inception v2 architecture. …”
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Detection of Mild Moldy-Core Disease in Apples by Fusing Acoustic-Vibration Signals and Visible-Near-Infrared Transmission Spectroscopy by GU Jiahui, LAI Lisi, WANG Kai, ZHANG Hui

“…For the near-infrared spectral signals, the impacts of different preprocessing and feature extraction methods on modeling outcomes were analyzed to select the spectral feature bands. …”
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A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage by Xueyong Li, Xiaoqing Yang

“…Using this approach, machine learning models may be taught to see patterns, fix errors, and make wise predictions under different conditions.   Doi: 10.28991/HIJ-2025-06-01-011 Full Text: PDF…”
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Bringing Intelligence to the Edge for Structural Health Monitoring: The Case Study of the Z24 Bridge by Ali Dabbous, Riccardo Berta, Matteo Fresta, Hadi Ballout, Luca Lazzaroni, Francesco Bellotti

“…To this end, we study the application of two convolutional neural network architectures that have emerged in the literature for efficient feature extraction from time series, namely WaveNet and MINImally RandOm Convolutional KErnel Transform (MiniRocket). …”
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Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning by Runze Liu, Ziwei Wang, Yanbo Zhang, Xulong Yao, Shaohong Yan, Zhiyuan Chen, Shuai Wang, Hua Li, Qi Wang

“…To address these challenges, this study develops a deep learning model based on an adaptive moment estimation optimized convolutional long short-term memory neural network (Adam-ConvLSTM) to predict the evolution of rock fractures. …”
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Monitoring and Analyzing Driver Physiological States Based on Automotive Electronic Identification and Multimodal Biometric Recognition Methods by Shengpei Zhou, Nanfeng Zhang, Qin Duan, Xiaosong Liu, Jinchao Xiao, Li Wang, Jingfeng Yang

“…Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring.…”
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Extraction of typical operating scenarios of new power system based on deep time series aggregation by Zhaoyang Qu, Zhenming Zhang, Nan Qu, Yuguang Zhou, Yang Li, Tao Jiang, Min Li, Chao Long

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Short-Term Wind Speed Forecasting With Deep Learning by Zeynep Mine Alçin, Fatih Karaaslan, Muzaffer Aslan

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Detection and recognition of unsafe behaviors of underground coal miners based on deep learning by GUO Xiaoyuan, ZHU Meiqiang, TIAN Jun, ZHU Beibei

“…Subsequently, a spatiotemporal graph convolutional network (ST-GCN) was utilized for behaviour recognition. …”
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Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping by Xiaochuan Wu, Zengyi Ling, Xin Zhang, Zhanchao Ma, Weibo Deng

“…Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. …”
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Defect Detection and Classification on Wind Turbine Blades Using Deep Learning with Fuzzy Voting by Reed Pratt, Clark Allen, Mohammad A. S. Masoum, Abdennour Seibi

“…Three Mask R-CNN models, leveraging different convolutional neural network (CNN) backbones—VGG19, Xception, and ResNet-50—were constructed and trained on a novel dataset of 3000 RGB images (size 300 × 300 pixels) annotated with defects, including cracks, holes, and edge erosion. …”
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