Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment by Yihao Zhao BSc, Cuiyun Yuan MSc, Ying Liang PhD, Yang Li MSc, Chunxia Li MSc, Man Zhao MSc, Jun Hu PhD, Ningze Zhong Bsc, Wei Liu PhD, Chenbin Liu PhD

“…Conclusion Our proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. …”
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Advanced AI techniques for root disease classification in dental X-rays using deep learning and metaheuristic approach by Prem Enkvetchakul, Surajet Khonjun, Rapeepan Pitakaso, Thanatkij Srichok, Peerawat Luesak, Chutchai Kaewta, Sarayut Gonwirat, Chawis Boonmee, Matus Noowattana, Thitinon Srisuwandee

“…This dual-phase approach enhances feature diversity, classification robustness, and computational efficiency. …”
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Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai by Guoqing Zhang, Wei Luo, Yongxiang Zhao, Quanqin Shao, Lin Li, Keyu Mei, Guohong Li

“…Background-suppressed images can effectively minimize most background pixels, while regions with high vector values in optical flow–processed images indicate object positions. The model extracts raw feature data, object details, and movement information from these inputs to improve detection performance. …”
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Improving the navigation optimization of hospital logistics robots under complex lighting changes by using improved ORB-SLAM3 and deep learning visual SLAM algorithm by Feng Xiao, Youhai Zhang, Jie Fang, Xing Guo, Rubing Huang

“…The improved ORB-SLAM3 algorithm extracts feature points of different scales by constructing an image pyramid to ensure that effective visual information can be stably obtained under different lighting conditions; the strategy of combining adaptive threshold and dual threshold methods is used to optimize the accuracy of feature point extraction, and the feature points are efficiently managed through a four-tree model to ensure the uniform distribution of feature points. …”
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HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images by Zhicheng Zhao, Qing Gao, Jinquan Yan, Chenglong Li, Jin Tang

“…HSFMamba leverages the long-range modeling capability of the state space model with linear complexity and incorporates optical images through two progressive cross-selection scan mechanisms to perform high-quality reconstruction of SAR images corrupted by speckle noise. …”
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Monitoring of greenhouse gas emission drivers in Atlantic Canadian Potato production: A robust explainable intelligent glass-box by Mehdi Jamei, Muhammad Hassan, Aitazaz A. Faroouqe, Mumtaz Ali, Masoud Karbasi, Gurjit S. Randhawa, Zaher Mundher Yaseen, Ross Dwyer

“…Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO2|R = 0.8431, SquD=17.1759, WASPAS=1.88E-07; N2O|R = 0.8431, SquD=17.1759, WASPAS=1.88E-07; H2O| R = 0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. …”
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Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion by Jinyin Pan, Shidong Wang, Huachao Li, Zhanliang Yuan, Binbin Yuan, Jinyan Peng, Yuanyuan Liu

“…The effectiveness of the proposed IFNPMH is demonstrated by extensive testing in comparison to other sophisticated approaches. This model has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.…”
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MSM-TDE: multi-scale semantics mining and tiny details enhancement network for retinal vessel segmentation by Hongbin Zhang, Jin Zhang, Xuan Zhong, Ya Feng, Guangli Li, Xiong Li, Jingqin Lv, Donghong Ji

“…Extensive experimental results on four public datasets validate the superiority of MSM-TDE, which obtains competitive performance with satisfactory model complexity. Notably, this study provides an innovative idea of multi-scale semantics mining by diverse methods.…”
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Prediction of depressive disorder using machine learning approaches: findings from the NHANES by Thien Vu, Research Dawadi, Masaki Yamamoto, Jie Ting Tay, Naoki Watanabe, Yuki Kuriya, Ai Oya, Phap Ngoc Hoang Tran, Michihiro Araki

“…SHAP (SHapley Additive exPlanations) values were used to identify the critical risk factors and interpret the contributions of each feature to the prediction. Results XGBoost was identified as the best-performing model, achieving the highest accuracy, sensitivity, specificity, precision, AUC, and F1 score. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

“…Among them Random Forest model performed the best, achieving an RMSE of 0.16 and an accuracy of 96.8 %. …”
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Pork-YOLO: Automated collection of pork quality traits by Jiacheng Wei, Xi Tang, Jinxiu Liu, Ting Luo, Yan Wu, Junhui Duan, Shijun Xiao, Zhiyan Zhang

“…Next, the StarNet backbone was integrated, employing star-shaped operations for high-dimensional feature representation while maintaining computational efficiency, along with a hierarchical convolutional structure to boost performance. …”
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Blockchain Integration in Islamic Finance: A New Era for Banking Sector in Pakistan by Saifullah, Muhammad Wasie Fasih Butt, Syed Babar

“…The most transformative feature of Blockchain technology, i.e., transparency, governance, and traceability, has a significant positive impact on sustainable performance. …”
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Study of Renewable Potential of the Republic of Armenia for Implementation of Hybrid Autonomous Power Supply System Using Solar Photovoltaic Modules by N. R. Kostik, S. A. Tarasov, G. Ye. Ayvasyan

“…Ten additional variants of AHRESS were modeled in order to optimize the component base of the configuration.Conclusion. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

“…Neutralizing epitope, amino acid selection pressure, and evolution dynamics analyses on VP7 and VP4 were performed using BioEdit v.7.0.9.0 and PyMOL v.2.5.2, four algorithms (MEME, SLAC, FEL, and FUBAR) in the Datamonkey online software, and the MCMC model in BEAST v. 1.10.4, respectively. …”
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Improving Measurement Sensitivity for a Displacement Sensor Based on Self-Mixing Effect by Yuxi Ruan, Bin Liu, Yanguang Yu, Jiangtao Xi, Qinghua Guo, Jun Tong

“…An SME-based displacement sensor is featured with compact physical setup and interferometric measurement resolution. …”
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High-Efficiency Lithium Niobate Electro-Optic Modulator with Barium Titanate Cladding on Quartz by Hongkang Liu, Jianping Li, Weiqin Zheng, Zixin Chen, Jianbo Zhang, Yuwen Qin

“…This theoretical model provides a novel design solution for TFLN modulators on quartz substrates. …”
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Event-Level Identification of Sleep Apnea Using FMCW Radar by Hao Zhang, Shining Bo, Xuan Zhang, Peng Wang, Lidong Du, Zhenfeng Li, Pang Wu, Xianxiang Chen, Libin Jiang, Zhen Fang

“…Crucially, we integrate blood oxygen saturation (SpO₂) prediction as an auxiliary task within a multitask-learning framework to enhance the model’s feature extraction capabilities and clinical utility by capturing physiological correlations between apnea events and oxygen levels. …”
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MSS-YOLO: Multi-Scale Edge-Enhanced Lightweight Network for Personnel Detection and Location in Coal Mines by Wenjuan Yang, Yanqun Wang, Xuhui Zhang, Le Zhu, Tenghui Wang, Yunkai Chi, Jie Jiang

“…By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…The dataset was randomly split into training and testing sets, with 80% allocated for training and 20% for testing. To evaluate the performance of each predictive model, we used a range of metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC). …”
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MLP-UNet: an algorithm for segmenting lesions in breast and thyroid ultrasound images by Tian-feng Dong, Chang-jiang Zhou, Zhen-yi Huang, Hao Zhao, Xue-long Wang, Shi-ju Yan

“…Attention module is a lightweight employed during the skip connections to enhance feature representation. Using only using 33.75 M parameters, MLP-UNet achieves state-of-the-art segmentation performance. …”
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