SFD-YOLO: A novel framework for subsidence funnels detection in China based on large-scale SAR interferograms by Jing Guo, Zhengjia Zhang, Peifeng Ma, Mengmeng Wang, Xuefei Zhang, Dongdong Li, Bing Sui

“…To address these challenges, this study proposes a deep learning network based on the YOLO architecture—SFD-YOLO (Sinking Funnel Detection-YOLO). …”
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A Hybrid Large Language Model for Context-Aware Document Ranking in Telecommunication Data by Abhay Bindle, Preeti Singla, Sachin Sharma, Abdukodir Khakimov, Reem Ibrahim Alkanhel, Ammar Muthanna

“…This paper presents hybrid document retrieval and ranking approach that integrates statistical, probabilistic, and neural network-based retrieval models to enhance information retrieval performance in telecommunication domain. …”
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KERF: a knowledge-enhanced relearning framework for tailings pond detection from high-resolution remote sensing images by Bingjie Liu, Wei Wu, Hao Wu, Ailong Ma, Shaohua Hu, Nianchun Du, Xuan Cui, Junyang Xie

“…This framework comprises a multi-stage positioning optimization module and an adaptive knowledge-supported relearning module for recognition refinement. An augmented feature pyramid network and multi-stage detectors are introduced to generate proposals with feature enhancement and location optimization. …”
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Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men by Torben Brøchner Pedersen, Mads Hvid Poulsen, Martin Lund, Søren Feddersen, Maher Albitar, Charlotte Aaberg Poulsen, Lars Lund

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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…This research focuses on enhancing the efficiency and accuracy of tomato leaf disease detection by modifying mobile-based Convolutional Neural Networks (CNNs). This model employs two parallel network streams based on the core principles of MobileNetv3, utilizing inverted residual blocks (IRBs) to improve accuracy at both low and high-level features, operating across different image dimensions. …”
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Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning by Hitomi Nakamura, Motohisa Fukuda, Akiko Matsuda, Naohiko Makino, Hirohito Kimura, Yu Ohtaki, Yoshihito Nawa, Soushi Oyama, Yuya Suzuki, Toshikazu Kobayashi, Tetsuya Ishizawa, Yasuharu Kakizaki, Yoshiyuki Ueno

“…This finding may contribute to sample‐efficient dataset preparation to train convolutional neural networks for endoscopic ultrasonography‐imaging diagnosis.…”
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Design and experimental research on the rocker arm walking mechanism of the wheeled inspection robot for the main transportation roadway of coal mines by YANG Rui, BAO Jiusheng, BAO Zhouyang, YIN Yan, ZHANG Lei, PAN Guoyu, YANG Jiao, GE Shirong

“…The Delphi method and network analysis were employed for a comprehensive performance evaluation of the walking mechanisms. …”
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Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique by Hasnain Iftikhar, Moiz Qureshi, Paulo Canas Rodrigues, Muhammad Usman Iftikhar, Javier Linkolk Lopez-Gonzales, Hasnain Iftikhar

“…The proposed hybrid technique combines the features of various regression, time series, and machine learning models to improve forecast accuracy. …”
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Normalizing flows for high-dimensional detector simulations by Florian Ernst, Luigi Favaro, Claudius Krause, Tilman Plehn, David Shih

“…In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. …”
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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study by Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

“…Three models were developed and compared: a conventional artificial neural network (ANN), a basic convolutional neural network (CNN), and a transfer learning–based Visual Geometry Group (VGG)16 model. …”
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A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform by Manohar Mishra, Debadatta Amaresh Gadanayak, Abha Pragati, Jai Govind Singh

“…The approach integrates the wavelet scattering transform (WST) to extract low-variance feature vectors and a newly developed variable batch size long-short-term-memory (VB-LSTM) network for accurate fault detection. …”
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Research on Multi-Factor Coastal Waterway Depth Prediction and Application Based on Attention-Enhanced LSTM Model by LING Ganzhan, HAN Yu, WANG Jiawei, JIE Weiwei, TANG Ruikai, HU Jiakai, LIU Xiang, LIANG Guangyue, CAO Lu, LIANG Ming

“…By integrating key hydrological features, the model adapts effectively to varying waterway conditions, offering improved precision in depth forecasting. …”
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Two-Stage Video Violence Detection Framework Using GMFlow and CBAM-Enhanced ResNet3D by Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud Salaheldin Kasem, Hyun-Soo Kang

“…In the second stage, we integrate these optical flow images with RGB frames and feed them into a CBAM-enhanced ResNet3D network to capture complementary spatiotemporal features. …”
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Data Analytics and Machine Learning Models on COVID-19 Medical Reports Enhanced with XAI for Usability by Oliver Lohaj, Ján Paralič, Zuzana Paraličová, Daniela Javorská, Elena Zagorová

“…<b>Methods</b>—We used random forest, LightGBM, XGBoost, CatBoost, KNN, SVM, logistic regression, and MLP neural network models in this work. The models are evaluated depending on the type of prediction by relevant metrics, especially accuracy, F1-score, and ROC AUC score. …”
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Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration by Shaofeng Tan, Jie Pei, Yaopeng Zou, Huajun Fang, Tianxing Wang, Jianxi Huang

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Player versus Player: A systematic review of cyberbullying in multiplayer online games by Hu Yunhao, Evelyn Sophie, Clancy Elizabeth M., Klettke Bianca

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Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep Learning by Junjian Hou, Bingyu Zhang, Yudong Zhong, Wenbin He

“…Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). …”
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Joint Extraction of Hazard Source Knowledge in Integrated Utility Corridor Based on Knowledge Graph by Shanshan Wan, Houchen Lv, Yuhan Zhu, Yiran Zhao

“…The joint extraction includes using the BERT model to obtain sentence representations, employing a relation-specific attention network to capture relation-based sentence features, followed by sequence labeling to extract entity pairs. …”
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Encrypted traffic classification encoder based on lightweight graph representation by ZhenWei Chen, XiaoXu Wei, YongSheng Wang

“…Abstract In recent years, traffic encryption technology has been widely adopted for user information protection, leading to a substantial increase in encrypted traffic in communication networks. To address issues such as unclear local key features and low classification accuracy in traditional malicious traffic detection and normal application classification, this paper introduces an encrypted traffic classification encoder based on lightweight graph representation. …”
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Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification by Zhuozheng Wang, Yunlong Wang

“…The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. …”
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