Real-time prediction model of public safety events driven by multi-source heterogeneous data by Quanlong Fan, Gang Xu

“…The model integrates Graph Attention Networks (GAT), Spatiotemporal Transformers, and Proximal Policy Optimization (PPO) to achieve effective data fusion, spatiotemporal feature extraction, and real-time decision support. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…This model integrates pre-processing techniques and employs the Tuna Swarm Optimization (TSO) Algorithm for feature selection in executing multi-label disease prediction. …”
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Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models by Damjan Bujak, Hanna Miličević, Goran Lončar, Dalibor Carević

“…This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. …”
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PlantHealthNet: Transformer-Enhanced Hybrid Models for Disease Diagnosis and Severity Estimation in Agriculture by Abid Iqbal

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PIMTABSA: A Personality influenced Multitask model for Aspect Based Sentiment Analysis using LSTM by M. Priadarsini, J. Akilandeswari

“…And then, model uses the LSTM component to model the sequential character of the text, which makes the extraction accurate in terms of the aspect terms and sentiment polarities. …”
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A Study of Corrosion-Grade Recognition on Metal Surfaces Based on Improved YOLOv8 Model by Hao Chen, Ying Cao, Shengxian Cao, Heng Piao

“…Finally, based on ablation experiments and comparison experiments, performance analyses of multiple algorithmic models are conducted for horizontal and vertical comparisons in order to verify the effectiveness of the improved method and the superiority of the models in this paper. …”
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An EMG-Based GRU Model for Estimating Foot Pressure to Support Active Ankle Orthosis Development by Praveen Nuwantha Gunaratne, Hiroki Tamura

“…Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. …”
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RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer by Yuan Liu, Haipeng Li, Zhu Zhu, Chen Chen, Xiaojing Zhang, Gongsheng Jin, Hongtao Li

“…This integration aims to reduce model parameters while enhancing key feature extraction capabilities, thereby achieving both lightweight design and high efficiency. …”
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Lightweight detection and segmentation of crayfish parts using an improved YOLOv11n segmentation model by Wei Shi, Jun Zhang, YunFan Fu, DanWei Chen, JianPing Zhu, ChunFeng Lv

“…Third, the CARAFE contentaware upsampling module replaces nearestneighbor interpolation to preserve highresolution information, boosting smalltarget segmentation (e.g., claws and legs) without significant model size growth. On our selfconstructed dataset, YOLOv11nDHBC achieves 96.8% detection mAP@0.5 and 96.0% segmentation mAP@0.5—surpassing the YOLOv11nSeg baseline—while maintaining realtime inference at 65.8 FPS and a model size of 4.2 MB (26.3% smaller). …”
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A study on the prediction of mountain slope displacement using a hybrid deep learning model by Yuyang Ma, Xiangxiang Hu, Yuhang Liu, Yaya Shi, Zhiyuan Yu, Xinmin Wang, Liangbai Hu, Shuailing Liu, Dongdong Pang

“…The method employs an Improved Whale Optimization Algorithm (IWOA) to fine-tune parameters for GNSS data fitting, ensuring accurate signal feature extraction. These parameters are then fed into a Long Short-Term Memory (LSTM) network to model spatiotemporal dependencies through deep temporal pattern learning. …”
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Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation by Rio Arifando, Shinji Eto, Tibyani Tibyani, Chikamune Wada

“…The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. …”
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The MacqD deep-learning-based model for automatic detection of socially housed laboratory macaques by Genevieve Jiawei Moat, Maxime Gaudet-Trafit, Julian Paul, Jaume Bacardit, Suliann Ben Hamed, Colline Poirier

“…To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. …”
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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…This study proposes a hybrid convolutional neural network-spiking neural network (CNN-SNN) architecture to classify AD stages using structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The model synergizes CNNs for hierarchical spatial feature extraction and SNNs for biologically inspired temporal dynamics processing. …”
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Cognitive model of the national financial market: creating process and prospects for assessing the security of its functioning by E. A. Loktionova

“…A feature of the study is that when building a cognitive model of the national financial market, the author uses as the main elements not individual actors of the financial market, but macroeconomic aggregates that characterize the state of the financial market and the economy as a whole. …”
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An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors by Damaris Mulwa, Benedicto Kazuzuru, Gerald Misinzo, Benard Bett

“…The correlation matrix revealed a modest linear dependence between different climatic variables and RVF cases, with the highest correlation, a mere 0.02903, observed for rainfall. The XGBoost model was trained using these climate variables and achieved outstanding performance measures including an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. …”
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Advancing Cyber Incident Timeline Analysis Through Retrieval-Augmented Generation and Large Language Models by Fatma Yasmine Loumachi, Mohamed Chahine Ghanem, Mohamed Amine Ferrag

“…Traditional approaches rely on processing structured artefacts, such as logs and filesystem metadata, using multiple specialised tools for evidence identification, feature extraction, and timeline reconstruction. This paper introduces an innovative framework, GenDFIR, a context-specific approach powered via large language model (LLM) capabilities. …”
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Research on Prediction model of Carotid‐Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm by Minghui Chen, Jing Xiong, Moran Li, Tao Hu, Yi Zhang

“…The Pearson correlation coefficient was employed for correlation analysis in feature selection. The linear regression models demonstrated low root mean square error (RMSE), error term (ε), and R2 values, indicating good predictive performance. …”
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SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase by Yu Zhao, Fei Liu, Qiang He, Fang Liu, Xiaohu Sun, Jiyong Zhang

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SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation by Mohsen Shahraki, Ahmed Elamin, Ahmed El-Rabbany

“…The proposed SAMNet++ model demonstrates high segmentation performance, achieving accuracy, precision, recall, and F1-score values above 0.97 across all classes on our experimental datasets. …”
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