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

“…First, optimized variational mode decomposition (VMD) is used to decompose the load series and the sub-sequences are combined with relevant features, to form the different input sequences of the prediction model. …”
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Signal Processing for Transient Flow Rate Determination: An Analytical Soft Sensor Using Two Pressure Signals by Faras Brumand-Poor, Tim Kotte, Enrico Gaspare Pasquini, Katharina Schmitz

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Intelligent recognition algorithm and application of coal mine overhead passenger device based on multiscale feature fusion by Beijing XIE, Heng LI, Hang DONG, Zheng LUAN, Ben ZHANG, Xiaoxu LI

“…This enhancement increased the dynamic adjustment capability of the target receptive field for cmopd with different passenger-carrying statuses, allowing the model to capture different scale information and better learn the coupled features of cmopd and miners. …”
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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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Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs by Guoqiang Hou, Qiwen Yu, Fan Chen, Guang Chen

“…To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). …”
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Feature enhanced cascading attention network for lightweight image super-resolution by Feng Huang, Hongwei Liu, Liqiong Chen, Ying Shen, Min Yu

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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…Moreover, qualitative analysis reveals the model’s excellent generalization ability in handling different scenarios.…”
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OE-YOLO: An EfficientNet-Based YOLO Network for Rice Panicle Detection by Hongqing Wu, Maoxue Guan, Jiannan Chen, Yue Pan, Jiayu Zheng, Zichen Jin, Hai Li, Suiyan Tan

“…First, oriented bounding boxes (OBB) replace horizontal bounding boxes (HBB) to precisely capture features of rice panicles across different heights and growth stages. Second, the backbone network is redesigned with EfficientNetV2, leveraging its compound scaling strategy to balance multi-scale feature extraction and computational efficiency. …”
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Detection of Apple Leaf Diseases using Faster R-CNN by Yunus Özen, Melike Sardoğan, Adem Tuncer

“…Leaf images were obtained from different apple orchards for two years. Inour observations, it was determined that apple trees of Yalova had black spot(venturia inaequalis) disease. …”
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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

“…Considering the spatiotemporal correlations among different rock fractures, one dataset was used to train the Adam-ConvLSTM model, yielding an initial model that accurately predicts fracture propagation for a single rock dataset. …”
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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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FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images by Bochao Chen, Yapeng Wang, Xu Yang, Xiaochen Yuan, Sio Kei Im

“…FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. …”
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Forensic of video object removal tamper based on 3D dual-stream network by Lizhi XIONG, Mengqi CAO, Zhangjie FU

“…In order to solve the problems of inaccurate temporal detection and location of the object removal tampered video， a video tamper forensics method based on 3D dual-stream network was proposed.Firstly， the spatial rich model （SRM） layer was used to extract the high-frequency information from video frames.Secondly， the improved 3D convolution （C3D） network was used as the feature extractor of the dual-stream network to extract the high-frequency information and low-frequency information from the high-frequency frame and the original video frame respectively.Finally， through compact bilinear pooling （CBP） layer， two sets of different feature vectors were fused into one set of feature vectors for classification prediction.The experimental results demonstrate that the classification accuracy of the proposed method in all video frames has an advantage in SYSU-OBJFORG dataset， which makes the temporal detection and location of object removal tampered video more accurate.…”
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Quadrature Solution for Fractional Benjamin–Bona–Mahony–Burger Equations by Waleed Mohammed Abdelfattah, Ola Ragb, Mokhtar Mohamed, Mohamed Salah, Abdelfattah Mustafa

“…The novelty of these methods is based on the generalized Caputo sense, classical differential quadrature method, and discrete singular convolution methods based on two different kernels. …”
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DTCNet: finger flexion decoding with three-dimensional ECoG data by Fufeng Wang, Zihe Luo, Wei Lv, XiaoLin Zhu

“…Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. …”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…In this paper, we propose LSEVGG, a novel and efficient CNN architecture that enhances the classic VGG structure through the integration of lightweight convolution techniques and channel attention mechanisms. …”
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