Multilevel Feature Cross-Fusion-Based High-Resolution Remote Sensing Wetland Landscape Classification and Landscape Pattern Evolution Analysis by Sijia Sun, Biao Wang, Zhenghao Jiang, Ziyan Li, Sheng Xu, Chengrong Pan, Jun Qin, Yanlan Wu, Peng Zhang

“…To address these issues, this study proposes the multilevel feature cross-fusion wetland landscape classification network (MFCFNet), which combines the global modeling capability of Swin Transformer with the local detail-capturing ability of convolutional neural networks (CNNs), facilitating discerning intraclass consistency and interclass differences. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…In order to improve the feature extraction ability and fault diagnosis accuracy of fault diagnosis models, based on the lightweight convolutional neural network MobileNetV2, the convolutional block attention mechanism CBAM is integrated into the deep feature mapping of MobileNetV2, and an attention mechanism convolutional classification network CBAM-MobileNetV2 is constructed. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The Dilated Encoder network is introduced to cover different-scale targets by differentiating receptive fields, and the feature weight allocation is optimized by combining it with a Convolutional Block Attention Module (CBAM). …”
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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…To be more specific, when dealing with different datasets, its MAE values are 0.0093, 0.0137, 0.0081, 0.0055, and 0.0101 respectively; the MSE values are 0.0002, 0.0002, 0.0001, 0.0001, and 0.0002 respectively; the MAPE values are 1.3630, 1.7456, 1.1905, 0.8456, and 1.7567 respectively; and the R2 values are 0.9891, 0.9888, 0.9943, 0.9955, and 0.9933 respectively. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…SIG-Shapeformer consists of three core components: (1) a Shapelet-based module that captures discriminative and interpretable local temporal patterns; (2) a multi-scale Inception module combining 1D convolutions and Transformer encoders to extract temporal features across different scales; and (3) a differentially enhanced Gramian Angular Summation Field (GASF) module that converts time series into 2D texture representations, significantly improving the recognition of cloud internal structures. …”
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Foreign object recognition for mine conveyor belt iron separators based on transfer learning with EfficientNet by Hailong YANG, Yiping YUAN, Panpan FAN, Lu XIAO, Feiyang ZHAO, Shaoke YUAN

“…This allowed for the stacking and analysis of feature maps at different levels, extracting deep feature signals from the images. …”
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Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG by Mingjie Yan, Zhe Chen, Jianmin Li, Jinhua Li, Lizhi Pan

“…Ten able-bodied individuals were instructed to complete wrist isokinetic flexion and extension tasks with different movement patterns, and the HD-sEMG signals were collected. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. …”
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Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System by Muhenad Bilal, Ranadheer Podishetti, Daniel Grossmann, Markus Bregulla

“…It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. …”
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Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…The combination of fluctuating consumer demand patterns and high variability across different energy sources presents significant challenges in maintaining a reliable balance between supply and demand. …”
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Predicting the Evolution of the Supercontinuum Generation With CNN-LSTM Model by Yi Feng, Ruiyuan Liu, Xinyue Chang, Xiangzhen Huang, Yuan He, Ning Li, Tiantian Zhou, Chujun Zhao

“…We propose a hybrid deep learning model, namely convolutional neural network–long short-term memory (CNN-LSTM) approach to investigate the evolution of the supercontinuum (SC) generation numerically. …”
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AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients by Qiuyue Li, Kewei Li, Cong Fu, Yiyuan Zhang, Huan Yu, Chen Chen, Wei Chen

“…Traditional single-event detection methods often overlook the dynamic interactions between SDB and LM, failing to capture their temporal overlap and differences in duration. To address this, we propose Attention-enhanced CRF with U-Net (AttenCRF-U), a novel joint detection framework that integrates multi-head self-attention (MHSA) within an encoder–decoder architecture to model long-range dependencies between overlapping events and employs multi-scale convolutional encoding to extract discriminative features across different temporal scales. …”
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Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…For the various tasks, the models are trained using datasets with incomplete labels, leading to a comparison of different training strategies. Our model, YOLOv8, achieved an F1-score of 95.96% for the polyp detection task, 85.24% F1-score for the polyp classification task, and 78.41% macro F1-score for the polyp size estimation task. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products by Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang

“…Then, we investigate the generalization ability of the models across three different data products. Finally, we fairly compare the forecasting performance of iTransformer with that of the currently advanced NASA/CCMC models. …”
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Vision Foundation Model Guided Multimodal Fusion Network for Remote Sensing Semantic Segmentation by Chen Pan, Xijian Fan, Tardi Tjahjadi, Haiyan Guan, Liyong Fu, Qiaolin Ye, Ruili Wang

“…The fusion of multimodal data presents challenges due to discrepancies in image acquisition mechanisms among different sensors, leading to misalignment issues. …”
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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. …”
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Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities by Wei Chen, Xinghua Lu, Wei Fan, Xiaochao Wang

“…During the evaluation of experimental waveforms, the model can obtain different denoised waveforms with contrast greater than 200:1. …”
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Hybrid-KANet: a hyperspectral remote sensing crop classification method based on the Kolmogorov–Arnold network by Weizhen Zhang, Zhihui Li, Tong Zhen

“…The ablation experiments demonstrate the advantages of RBF kernel function in modeling complex nonlinear relationships by systematically comparing the differences in classification performance and boundary modeling ability of different kernel functions, which improves the classification accuracy and spatial consistency. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…This paper begins with a review of the literature on the definitions of fake news and deepfakes, their different types and major differences, and the ways they spread. …”
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