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    Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output by Chethan Ramprasad, Divya Saini, Henry Del Carmen, Lev Krasnovsky, Rajat Chandra, Ryan Mcgregor, Russell T. Shinohara, Eric Eaton, Meghna Gummadi, Shivan Mehta, James D. Lewis

    Published 2025-01-01
    “…A binary classification image-based machine learning algorithm was designed. Results: In a test set of 61 images, the binary classification machine learning algorithm was able to distinguish inadequate/fair preparation from good preparation with a positive predictive value of 78.6% and a negative predictive value of 60.8%. …”
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  5. 1625

    Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Lei Wang, Jinghang Li, Wenbo Zhang, Yu Li

    Published 2022-01-01
    “…In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). …”
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  6. 1626

    Application of Extra-Trees Regression and Tree-Structured Parzen Estimators Optimization Algorithm to Predict Blast-Induced Mean Fragmentation Size in Open-Pit Mines by Madalitso Mame, Shuai Huang, Chuanqi Li, Jian Zhou

    Published 2025-07-01
    “…The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. …”
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    Hybrid-driven modeling using a BiLSTM–AdaBoost algorithm for diameter prediction in the constant diameter stage of Czochralski silicon single crystals by Yu-Yu Liu, Ding Liu, Shi-Hai Wu, Yi-Ming Jing

    Published 2025-05-01
    “…Subsequently, the prediction results of the BiLSTM network are weighted and fused by the AdaBoost algorithm to obtain the final time series prediction output, and the prediction performance is further enhanced by iterative optimization. …”
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    Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study by Yuan Liu, Songyun Zhao, Wenyi Du, Wei Shen, Ning Zhou

    Published 2025-01-01
    “…In the present study, four advanced machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbor (KNN)—were employed to develop predictive models. …”
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    Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Based on Multi-Source Information by WANG Qiang, BI Yuhao, GAO Chao, SONG Duoyang

    Published 2025-06-01
    “…[Objective] Factors such as road networks, temperature, and electric vehicle (EV) type affect the spatial and temporal distribution of EV charging loads. To improve prediction accuracy, a spatiotemporal EV charging load prediction model is developed by integrating multiparty information. …”
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    Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

    Published 2024-11-01
    “…Hence, this work employs the artificial neural network (ANN) to develop a model that accurately predicts HPR in PEMWE setups. A novel approach is introduced by employing the Levenberg–Marquardt backpropagation (LMBP) algorithm for training the ANN. …”
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