Novel models based on machine learning to predict the prognosis of metaplastic breast cancer by Yinghui Zhang, Wenxin An, Cong Wang, Xiaolei Liu, Qihong Zhang, Yue Zhang, Shaoqiang Cheng

“…We utilized prognostic factors to develop a novel machine learning model (CatBoost) for predicting patient survival rates. …”
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Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods by JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan

Subjects: “…national unified electricity market|spot market|electricity price forecasting|machine learning methods…”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

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The Optimal Machine Learning Model for the Precise Prediction of HighPerformance Concrete Strength Property by Yufeng Qian

“…The present study employs a machine learning-based support vector regression (SVR) method to implement compressive strength prediction. …”
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Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning by Seyyed Shahabaddin Hosseini Dehshiri, Bahar Firoozabadi

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Machine learning suggests climate and seasonal definitions should change under global warming by Milton Speer, Lance Leslie

“…The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Australia, identified the dominant attributes of these precipitation and temperature events. …”
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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

Subjects: “…Machine Learning…”
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Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

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Predict the Maximum Dry Density of Soil Based on Individual and Hybrid Methods of Machine Learning by Ghanshyam Tejani, Behnam Sadaghat, Sumit Kumar

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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

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The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches by Md. Idris Ali, Md. Atikur Rahaman, Mohammed Julfikar Ali, Md. Ferdausur Rahman

“…To validate robustness, the Kernel Regularized Least Squares (KRLS) machine learning approach is employed, confirming the consistency of results. …”
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Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning by Meiyue Shen, Yine Zhang, Runqing Zhan, Tingwei Du, Peixuan Shen, Xiaochuan Lu, Shengnan Liu, Rongrong Guo, Xiaoli Shen

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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review by Anass Houdou, Imad El Badisy, Kenza Khomsi, Sammila Andrade Abdala, Fayez Abdulla, Houda Najmi, Majdouline Obtel, Lahcen Belyamani, Azeddine Ibrahimi, Mohamed Khalis

“…Abstract Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. …”
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Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete by Maryam Bypour, Mohammad Yekrangnia, Mahdi Kioumarsi

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Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. by Md Al Mehedi Hasan, Md Maniruzzaman, Jie Huang, Jungpil Shin

“…To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. …”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. …”
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