A GPT-Based Approach for Cyber Threat Assessment by Fahim Sufi

“…The fully deployed automated model also identified 11 correlated factors and five unique clusters associated with high-rated cyber incidents. …”
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Data-Driven Diagnostics for Pediatric Appendicitis: Machine Learning to Minimize Misdiagnoses and Unnecessary Surgeries by Deborah Maffezzoni, Enrico Barbierato, Alice Gatti

“…Furthermore, by employing advanced feature selection techniques, the models were able to reduce the number of unnecessary surgical interventions by up to 17%, highlighting their potential for clinical application. …”
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Artificial Intelligence Approaches for the Detection of Normal Pressure Hydrocephalus: A Systematic Review by Luis R. Mercado-Diaz, Neha Prakash, Gary X. Gong, Hugo F. Posada-Quintero

“…Evaluation metrics like Dice similarity coefficients and ROC-AUC were the most typical metrics of model performance. Challenges in implementing AI in clinical practice were identified, and the authors suggested that a hybrid deep-traditional ML framework could enhance NPH diagnosis. …”
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Enterprise fission path optimization and dynamic capability construction based on the soft actor-critic algorithm by Hengsheng Gu

“…The SAC algorithm’s entropy regularization feature prompts the model to strike a balance between exploration and utilization to optimize the dynamic capability construction. …”
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Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation by Min Huang, Zifeng Xie, Bo Sun, Ning Wang

“…Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. …”
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Explainable AI for time series prediction in economic mental health analysis by Ying Yang, Ying Yang, Lifen Wen, Li Li

“…Our approach systematically incorporates feature attribution, causal reasoning, and human-centric explanation generation using an interpretable model architecture.ResultsExperimental results demonstrate that our method maintains competitive accuracy while significantly improving interpretability. …”
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Enhancing action recognition in educational settings using AI-driven information systems for public health monitoring by Changchun Lu, Han Ruijuan

“…Traditional methods, which primarily rely on rule-based algorithms or handcrafted feature extraction, face significant challenges in adaptability, scalability, and real-time processing. …”
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DCWM-LSTM: A Novel Attack Detection Framework for Robotic Arms by Esra Nergis Yolacan, Hande Cavsi Zaim

“…Compared to simple LSTM models, our proposed framework achieves a significant enhancement in accuracy, with performance gains observed across both replay and subscriber flood attack scenarios. …”
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Turbulence-Resilient Object Classification in Remote Sensing Using a Single-Pixel Image-Free Approach by Yin Cheng, Yusen Liao, Jun Ke

“…Compared with existing image-based and image-free methods, our model achieves superior performance in classification accuracy, computational efficiency, and robustness, which is important for potential low-resource real-time remote sensing applications.…”
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Lightweight Infrared Small Target Detection Method Based on Linear Transformer by Bingshu Wang, Yifan Wang, Qianchen Mao, Jingzhuo Cao, Han Zhang, Laixian Zhang

“…With the flourish of deep learning, transformer models have achieved remarkable performance in dealing with many computer vision tasks. …”
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HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification by Shivani Agarwal, Anand Kumar Dohare, Pranshu Saxena, Jagendra Singh, Indrasen Singh, Umesh Kumar Sahu

“…The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. …”
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Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification by Eshika Jain, Pratham Kaushik, Vinay Kukreja, Modafar Ati, Shanmugasundaram Hariharan, Vandana Ahuja, Abhishek Bhattacherjee, Rajesh Kumar Kaushal

“…This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. …”
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Machine learning prediction of severity and duration of hypoglycemic events in type 1 diabetes patients by Annie Wu, Eashan Singh, Ivy Zhang, Anika Bilal, Anna Casu, Richard Pratley

“…Results find that XGBoost delivers the best performance in all cases.  Examination of the XGBoost feature importance scores show that glucose rate of change is the most used feature in the models generated by XGBoost.…”
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Boundary-enhanced dual-stream network for semantic segmentation of high-resolution remote sensing images by Xinghua Li, Linglin Xie, Caifeng Wang, Jianhao Miao, Huanfeng Shen, Liangpei Zhang

“…Compared with 11 state-of-the-art methods, BEDSN exhibits higher-level performance in both quantitative and visual assessments with low model complexity. …”
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Research on Underwater Small Target Detection Technology Based on Single-Stage USSTD-YOLOv8n by Weiguo Yi, Jinwei Yang, Lingwei Yan

“…To verify the robustness and accuracy of the model, a new experimental strategy is used to perform one set of ablation experiments and three sets of comparison experiments on the URPC2018 and URPC2020 datasets, the mAP @ 0.5 was 0.7670,0.7910 and 0.7044, compared to the YOLOv8n algorithm, map@0.5 increased 0.0260, 0.008 and 0.007. …”
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WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images by Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li

“…The research introduces a new infrared visual odometry that integrates feature-based methods with optical flow techniques, enhancing image processing capabilities to mitigate the issues of high time overhead and cumulative errors in traditional feature-based odometry. …”
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LET-SE2-VINS: A Hybrid Optical Flow Framework for Robust Visual–Inertial SLAM by Wei Zhao, Hongyang Sun, Songsong Ma, Haitao Wang

“…By integrating Lightweight Neural Network (LET-NET) for high-quality feature extraction and Special Euclidean Group in 2D (SE2) optical flow tracking, the system achieves superior performance in challenging scenarios such as low lighting and rapid motion. …”
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An Improved MAE-Based Pretraining Method for Urban Public Space Monitoring With Optical Remote Sensing Imagery by Wentao Wei, Huan Chen, Yu Jiang, Li Fu, Ping Yao

“…Different from most existing image encoders of vision transformer pretrained with MAE method on natural images, which have difficulty in transferring to small-scale remote sensing dataset because of its large domain gap relative to natural images and limit the fine-tuning performance on downstream tasks, our method performs pretraining directly on target remote sensing imagery to better capture its feature distribution. …”
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CANGuard: An Enhanced Approach to the Detection of Anomalies in CAN-Enabled Vehicles by Damilola Oladimeji, Razaq Jinad, Amar Rasheed, Mohamed Baza

“…While the Multi-Layer Perceptron (MLP) model demonstrated strong attack detection performance, it struggled with node identification, achieving less than 50% accuracy. …”
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A review of portability of fault diagnosis methods from PEM fuel cells to PEM water electrolyzers by Ruyi Wang, Jiangong Zhu, Haoyu Zhang, Chao Wang, Xiaoping Wu, Ming Wu, Jianqiang Xu, Hao Yuan, Wei Liu, Haifeng Dai, Xuezhe Wei

“…Specifically, data-driven black-box model methods, feature extraction, feature transformation, and data preprocessing techniques developed for PEM fuel cells can serve as valuable references for PEM electrolyzers, enhancing diagnostic accuracy and real-time performance. …”
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