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  1. 5201

    Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

    Published 2025-04-01
    “…Performance was assessed using metrics such as coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.55, MAE = 0.17, and RMSE = 0.05). …”
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    A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

    Published 2025-09-01
    “…Our model and dataset are open sourced at https://github.com/YanJieWen/OD-STGCN.…”
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    PrediQt-Cx: post treatment health related quality of life prediction model for cervical cancer patients. by Satwant Kumar, Madhu Lata Rana, Khushboo Verma, Narayanjeet Singh, Anil Kumar Sharma, Arun Kumar Maria, Gobind Singh Dhaliwal, Harkiran Kaur Khaira, Sunil Saini

    Published 2014-01-01
    “…The objectives of the study were to compare the pre and the post treatment quality of life in cervical cancer patients and to develop a prediction model to provide an insight into the possibilities in the treatment modules.…”
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  19. 5219

    Prediction of Seawater Intrusion Run-Up Distance Based on <i>K</i>-Means Clustering and ANN Model by Jiatao Li, Zhenzhu Meng, Junkang Zhang, Yukai Chen, Jiewen Yao, Xinyue Li, Peng Qin, Xian Liu, Chunmei Cheng

    Published 2025-02-01
    “…The model’s performance is demonstrated through a high <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> value of 0.97 and low mean squared error (MSE) of 0.0092, surpassing traditional ANN models in its ability to capture complex wave dynamics.…”
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