Deep learning for predicting myopia severity classification method by WangMeiYu Xing, XiaoNa Li, JingShu Ni, YuanZhi Zhang, ZhongSheng Li, Yong Liu, YiKun Wang, Yao Huang

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Dynamic Accident Network Model for Predicting Marine Accidents in Narrow Waterways Under Variable Conditions: A Case Study of the Istanbul Strait by Serdar Yıldız, Özkan Uğurlu, Xinjian Wang, Sean Loughney, Jin Wang

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IoT BotScan: Ultra-Lightweight AI Defense Against Botnet Threats by Sapna Sadhwani, Urvi Kavan Modi, Raja Muthalagu, Pranav M. Pawar, Alavikunhu Panthakkan, Wathiq Mansoor

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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Then, we applied four machine learning methods—random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron—to develop binary classification models for N-dealkylation. …”
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Machine Learning-Based Weld Classification for Quality Monitoring by Rojan Ghimire, Rajiv Selvam

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Analysis of the Causes of Falling Accidents on Building Construction Sites in China Based on the HFACS Model by Yingchen Wang, Chaofan Liu, Hengshuo Xu, Xiaoxiao Geng, Yiran Wang, Yan Liu

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Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus) by Sollmann, Rahel, Adenot, Nathalie, Spakovszky, Peter, Windt, Jendrik, Mattsson, Brady J.

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CLASSIFICATION OF STUDENT GRADUATION STATUS USING XGBOOST ALGORITHM by Maria Welita Dwinanda, Neva Satyahadewi, Wirda Andani

“…The resulting XGBoost classification model is optimal at the number of rounds is 3 and the number of folds is 5. …”
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A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

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Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

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A Comparative Study of Drug Prediction Models using KNN, SVM, and Random Forest by Susi Eva Maria Purba

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THE INFLUENCE OF CONFLICTS ON THE STATE OF INTERNATIONAL SECURITY AND WAYS OF REGULATING GEOPOLITICAL TENSIONS by Olena A. Parshyna, Margaryta Yu. Parshyna, Tatiana V. Chumak, Liudmyla I. Yarmolenko

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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…Our proposed ResNet50* model demonstrated a classification accuracy of more than 92% for all three datasets and a remarkable 99.13% accuracy for the WCE dataset. …”
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A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-s... by Feng Wang, Minwei Bao, Bo Tao, Fugui Yang, Guangxue Wang, Lei Zhu

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Development of a Model Supporting the Analysis of Costs Related to Equipment Relocation in a Manufacturing Facility by Ostowska Weronika, Stryhunivska Olena

“…To assist in decision-making and prioritization, the model applies the ABC classification method, grouping activities based on their financial impact. …”
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Roles of Pyroptosis-Related Genes in the Diagnosis and Subtype Classification of Periodontitis by Xiaofan Cheng, Yifang Hu, Guan Gui, Xiaoya Hu, Jie Zhu, Bowei Shi, Shoushan Bu

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A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach by Wei Huang, Dalin Tang, Xin Qiao, Guojun Chen

“…On the basis of the two-stage regression analysis, a novel parsimonious statistical model is developed. Third, the developed model is evaluated with three performance indicators, namely, the mean squared error (MSE), mean absolute error (MAE), and mean relative error (MRE). …”
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A study on the classification and prediction of firefighter's operational fatigue level. by Mingwei Xu, Shangxue Yang, Ke Wang, Chengliu Yu, Guanlin Liu, Chao Dai, Ruiqi Wang

“…Firefighting operations in high-rise building fires require firefighters to navigate complex environments while undertaking physically demanding, heavy-load tasks, which often lead to severe fatigue, impairing their operational efficiency and decision-making. This study aims to develop a robust fatigue classification and prediction model to assess and forecast firefighters' fatigue levels. …”
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Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…MSCAS-Net demonstrates a breakthrough in automated DR diagnostics since it combines high diagnostic precision with interpretable abilities to become an efficient AI-powered clinical decision support system. The presented research demonstrates how fine-grained visual classification methods benefit detecting and treating DR during its early stages.…”
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Fault Detection and Classification of Power Plant Using Neural Networks by Rawaa J. Hussein, Khalaf S. Gaeid, Amin Al-Habaibeh

“…Additionally, the MATLAB/Simulink 2022a software is utilized to simulate the Samarra thermal power plant model. The model represents a three-phase power system network comprising two units. …”
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