Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets by Saleh Nagi Alsubari, Sachin N. Deshmukh, Mosleh Hmoud Al-Adhaileh, Fawaz Waselalla Alsaade, Theyazn H. H. Aldhyani

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Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis by Antonious Hazim, Irene Riestra Guiance, Jacob Shreve, Gordon Ruan, Damian McGlothlin, Allison LeMahieu, Robert Haemmerle, Keith Mcconn, Richard C. Godby, Lisa Kottschade, Anna Schwecke, Casey Fazer-Posorske, Tobias Peikert, Eric Edell, Konstantinos Leventakos, Ashley Egan

“…Further effort is needed to produce clinical models able to provide clinician decision support when evaluating patients with ICI toxicities and considering ICI rechallenge.…”
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Modeling the cooling effect of urban green infrastructures with an ecosystem services approach ( case study: Tehran metropolis) by S. Malekzadeh, H.R. Jafari, R. Nazari, T. Blaschke, A. Hof, M. Karimi

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Predictive model based on blood cell analysis and coagulation function indicators for neuroblastic tumors staging diagnosis by Yanzi Zhang, Yanzi Zhang, Lihong Zhang, Lihong Zhang, Mengmeng Chen, Mengmeng Chen, Qin Dong, Qin Dong, Chong Hu, Chong Hu, Juan Wang, Juan Wang, Jiao Meng, Jiao Meng, Xin Lv, Xin Lv

“…Patients were stratified into localized (INSS 1-2, Group 1) and advanced (INSS 3-4, Group 2) stages according to the INSS classification, with mature ganglioneuroma serving as the control group. …”
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A New Method and Set of Parameters for Evaluating the Cushioning Effect of Shoe Heels, Revealing the Inadvertent Design of Running Shoes by Franz Konstantin Fuss, Tizian Scharl, Niko Nagengast

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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Six machine learning algorithms were employed to construct the prediction models. The performance of the prediction model was evaluated utilizing metrics including area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), precision, recall, and F1 score. …”
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Development and internal validation of a nomogram for sleep quality among Chinese medical students: a cross-sectional study by Zhen Lv, Hao Xu, Jun Chen, Handong Yang, Jishun Chen, Dongfeng Li, Ying Wang, Huailan Guo, Ningrui Zhang, Zhixin Liu, Xinwen Min, Wenwen Wu

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Modeling the Rating system of Export-Oriented Companies in the Agro-Industrial Complex of the Russian Federation. subsidy Mechanism by E. G. Semyashkin, A. M. Karminsky

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Deep Learning-Assisted Diagnostic System: Implant Brand Detection Using Improved IB-YOLOv10 in Periapical Radiographs by Yuan-Jin Lin, Shih-Lun Chen, Ya-Cheng Lu, Xu-Ming Lin, Yi-Cheng Mao, Ming-Yi Chen, Chao-Shun Yang, Tsung-Yi Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu, Chiung-An Chen

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A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study by Øyvind Ervik, Mia Rødde, Erlend Fagertun Hofstad, Ingrid Tveten, Thomas Langø, Håkon O. Leira, Tore Amundsen, Hanne Sorger

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Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture by Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis C. Tagarakis, Dimitrios Kateris, Patrizia Busato, Dionysis Bochtis

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CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study by Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu

“…Key Points Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. …”
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AdaptiveSwin-CNN: Adaptive Swin-CNN Framework with Self-Attention Fusion for Robust Multi-Class Retinal Disease Diagnosis by Imran Qureshi

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Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population by Xiaoyue Lyu, Jie Liu, Yingying Gou, Shengli Sun, Jing Hao, Yali Cui

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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

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Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models by Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke, Barbara Więckowska

“…Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. …”
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Partial Discharge Localization Method Based on UHF Wireless Sensor Array in Air-insulated Substation by Shuai YAN, Pengyu LI, Gaojie WANG, Qiang LI, Fan WU, Lingen LUO

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Detection of Brain Abnormalities in Parkinson’s Rats by Combining Deep Learning and Motion Tracking by Houchi Li, Quan He, Lei Wu

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Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis by T. Thamaraimanalan, Dhanalakshmi Gopal, S. Vignesh, K Kishore kumar

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Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform by Dahu Ren, Dahu Ren, Shuangqing Chen, Shicheng Liu, Xiaopeng Zhang, Wenfei Xue, Qingtao Zhao, Guochen Duan

“…A multi-dimensional nomogram model was constructed, and its clinical utility was evaluated using ROC curves and clinical decision curves.ResultsComparison between benign and malignant pulmonary nodule groups revealed significant differences in the risk stratification of the uAI platform (proportion of high-risk: 75.61% vs 34.29%) and in the median value of CTC quantitative detection (P<0.001). …”
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