Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models by Ali Raza, Fatma Eid, Elisabeth Caro Montero, Irene Delgado Noya, Imran Ashraf

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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

“…We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. …”
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Comparative analysis of machine learning classifiers and deep learning models for categorization of Knee Osteoarthritis by Deo Arpit, Korde Manish, Khatri Amit, Jain Aman, Kumawat Ashish, Rathore Vineeta

“…The study evaluates two Machine Learning classifiers which were Support Vector Machine (SVM) and XGBoost which both are optimized through GridSearchCV for hyperparameter tuning and two deep learning models EfficientNetB6 and EfficientNetB7 which both were fine tuned. …”
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma by Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu

“…We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepatectomy, and 30% of the samples were utilized for internal validation. …”
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Precise multi-factor immediate implant placement decision models based on machine learning by Guanqi Liu, Shudan Deng, Runzhong Liu, Yuanxiang Liu, Quan Liu, Shiyu Wu, Zhuofan Chen, Runheng Liu

“…These machine learning models were evaluated and compared for their predictive accuracy and performance. …”
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Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs by Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour, Fares Ashraf El Salamony, Khalaf G. Salem

“…In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. …”
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Assessing acoustic receiver detection efficiency using autocorrelation adjusted machine learning models by Devon A. Smith, James A. Crossman, Eduardo G. Martins

“…To predict detection efficiency, we applied regression-based machine learning models in two distinct river systems: a small mountainous and a large regulated river. …”
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Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting by Bruno Castelle, David Carayon, Jeoffrey Dehez, Sylvain Liquet, Vincent Marieu, Nadia Sénéchal, Sandrine Lyser, Jean-Philippe Savy, Stéphanie Barneix

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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…Abstract This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…The approach of our proposed model innovatively uses several advanced machine learning methods. …”
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Predictive modeling of gestational weight gain: a machine learning multiclass classification study by Audêncio Victor, Hellen Geremias dos Santos, Gabriel Ferreira Santos Silva, Fabiano Barcellos Filho, Alexandre de Fátima Cobre, Liania A. Luzia, Patrícia H.C. Rondó, Alexandre Dias Porto Chiavegatto Filho

“…This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines. …”
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Parsimonious models of root zone temperature in soilless substrates through ensemble machine learning by James F. Cross, James S. Owen, Jr., Jacob H. Shreckhise, Jeb S. Fields, Lloyd Nackley, James E. Altland, Darren T. Drewry

“…These strong lagged correlations motivated the development of relatively simple (one and two predictor) models of substrate temperature using both machine learning (shallow neural networks) and linear statistical models. …”
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Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms by Bocheng Jiang, Honglong Ning, Muyun Li, Rihui Yao, Chenxiao Guo, Yucheng Huang, Zijie Guo, Dongxiang Luo, Dong Yuan, Junbiao Peng

“…Transmittance alterations under different current densities were measured to determine modulation range and time response, serving as training data for ML models. Seven regression models were employed to construct EC models and predict optimal device solutions. …”
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Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles by K. Sunil Kumar, Abdul Razak, M. K. Ramis, Shaik Mohammad Irshad, Saiful Islam, Anteneh Wogasso Wodajo

“…The novelty lies in the synergistic use of these additives for improving fuel efficiency and reducing emissions, combined with advanced statistical and machine learning models for optimization and prediction. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…Through analysis, when comparing the four distinct ML models on the test set, the MLP model performed optimally across various evaluation metrics, including the highest area under the receiver operating characteristic (ROC) curve (AUROC) (0.898), sensitivity (0.820), test specificity (0.863), and F1 score (0.849), with an accuracy of 0.840. …”
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Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability by Abid Badshah, Basem Yousef Alkazemi, Fakhrud Din, Kamal Z. Zamli, Muhammad Haris

“…Using Multivariate Imputation by Chained Equations (MICE) to tackle data restrictions, we gauge wheat production for 2014-2024 and forecast the 2025 yield using machine learning regression models. Once again, using hyper parameter tuning with K-fold cross-validation, Support Vector Regressor (SVR) stands out as the top-performing model, achieving an accuracy of 99.9% with R2 Score. …”
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Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics by Yuqing Hua, Yuqing Hua, Na Li, Jiahui Lao, Zhaoyang Chen, Shiyu Ma, Xiao Li

“…A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. …”
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Smartphone-based machine learning model for real-time assessment of medical kidney biopsy by Odianosen J. Eigbire-Molen, Clarissa A. Cassol, Daniel J. Kenan, Johnathan O.H. Napier, Lyle J. Burdine, Shana M. Coley, Shree G. Sharma

“…Further refinements and validations are warranted to optimize the model's performance.…”
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Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders by Asma ul Husna, Saman Hassanzadeh Amin, Ahmad Ghasempoor

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