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    Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

    Published 2025-07-01
    “…This study aimed to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 using machine learning algorithms. Data from Demographic and Health Surveys covering 147,466 men were analyzed. …”
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    Article
  3. 1123

    A Probability Integral Parameter Inversion Method Integrating a Selection-Weighted Iterative Robust Genetic Algorithm by Chuang Jiang, Wei Liu, Lei Wang, Xu Zhu, Hao Tan

    Published 2025-07-01
    “…However, the use of traditional genetic algorithms (GA) for inversion prediction has problems such as poor resistance to differences, and the accuracy of inversion parameters is affected when key monitoring points are missing. …”
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  4. 1124

    The impact of intravenous iodinated contrast agents on radiotherapy dose calculation and radiobiological effect predictions in central lung cancer by Defu Yang, Feng Shang, Ying Xu, Ying Yan

    Published 2025-08-01
    “…This study evaluates and compares dosimetric differences and predictions of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) between the Analytic Anisotropic Algorithm (AAA) and Acuros XB (AXB) algorithm in lung cancer radiotherapy, under both contrast-enhanced and non-contrast enhanced CT conditions.MethodsTwenty patients with centralized lung cancer treated with intensity-modulated radiation therapy (IMRT) technique, including two patients with small cell lung cancer and 18 with non-small cell lung cancer, were selected to undergo CT scanning with and without contrast. …”
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    Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa by Tirualem Zeleke Yehuala, Bezawit Melak Fente, Sisay Maru Wubante

    Published 2025-05-01
    “…Method This study employs machine learning algorithms to predict short birth intervals among reproductive-age women in East Africa, using a dataset from Demographic and Health Surveys. …”
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    Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort by Zhe Xu, Ruguo Zhang, Qiuhan Chen, Guoxuan Peng, Shanpeng Luo, Chen Liu, Ling Zeng, Jin Deng

    Published 2025-08-01
    “…The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms. …”
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    RETRACTED: Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm by Longjun Guo, Juan Wang, Jiaqi Teng, Yukun Chen

    Published 2022-02-01
    “…As the development of deep learning DL-based bone age prediction methods have achieved great success. However, it also faces the issue of huge computation overhead in deep features learning. …”
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    TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP_ADABOOST ALGORITHM by ZHU Xiang, XIE Feng

    Published 2019-01-01
    “…The experimental result shows that the BP <sub>A</sub>da Boost algorithm can effectively diagnose the wear state of the tool in CNC machine tools compared with the SVM algorithm.…”
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    A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

    Published 2025-08-01
    “…This study seeks to address this gap by applying a range of advanced machine learning (ML) algorithms to predict hepatitis, contributing to global efforts to enhance public health outcomes. …”
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    Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

    Published 2024-12-01
    “…First, the outliers are processed using the box chart method and filtering algorithm. Then, the decision tree (DT), support vector regression (SVR), random forest (RF), and the bagging, boosting, and stacking integration algorithms are employed to construct a flotation recovery prediction model. …”
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