MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…This study investigates the application of machine learning models, specifically Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost)and Random Forest (RF) algorithms to predict groundwater levels across six boreholes within the Sokoto Basin. …”
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Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

“…This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. …”
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Modeling vector control of the asynchronous drive of electric rolling stock auxiliary machines by Yu. M. Kulinich, S. A. Shukharev, V. K. Dukhovnikov, A. V. Gulyaev

“…The authors developed a mathematical model of an asynchronous drive of auxiliary machines of an electric locomotive in a rotating coordinate system d – q by the SimInTech application package and concerning the cross-impact influence of d and q control channels.Discussion and conclusion. …”
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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles by Wen-jie Huo, Fei Peng, Song Quan, Xiao-cong Wang

“…Ultimately, LightGBM emerged as the optimal model, due to utilizing fewer features (8 vs. 10–11 in SVM/XGBoost) and offering superior interpretability. …”
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Energy-Aware Machine Learning Models—A Review of Recent Techniques and Perspectives by Rafał Różycki, Dorota Agnieszka Solarska, Grzegorz Waligóra

“…The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. …”
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Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models by Kingsley Ifeanyi Chibueze, Nwamaka Georgenia Ezeji, Nnenna Harmony Nwobodo-Nzeribe

“…It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. …”
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Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning by María Rodríguez Martínez, Matteo Barberis, Anna Niarakis

“…This large amount of data has facilitated the emergence of statistical and machine-learning models focused on unravelling the intricate complexities of the immune system. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Cross-validation and feature selection methods were used to optimize model performance and identify key variables that most significantly impact soil resistivity. …”
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Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients by Dan Li, Han Lu, Junhui Wu, Hongbo Chen, Meidi Shen, Beibei Tong, Wen Zeng, Weixuan Wang, Shaomei Shang

“…The most important features were extracted from the optimal model on external validation. A total of 469 individuals were included, with 70% used for training and 30% for testing. …”
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Integrating experimental and theoretical approaches for enhanced machine learning modeling of solar radiation by Nader Ghareeb, Abeer Alanazi, Ahmad Sedaghat, Mohamad Hussein Farhat, Arash Mehdizadeh, Hayder Salem, Mohammad Nazififard, Ali Mostafaeipour

“…In total, 28 ML models were evaluated, encompassing linear regression, support vector machines, Gaussian process regression, and neural networks. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…<h4>Results</h4>The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…Each ML model underwent rigorous cross-validation to ensure optimal performance. …”
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Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Background: This study investigates the application of machine learning models to classify electrocardiogram signals, addressing challenges such as class imbalances and inter-class overlap. …”
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An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang, Dajian Long, Yunxiu Li, Xiaozhu Liu, Zhi Bai, Zhongjun Li

“…The optimal one was picked out from seven models according to its AUC and other indices. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. …”
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Construction of a disease risk prediction model for postherpetic pruritus by machine learning by Zheng Lin, Yuan Dou, Ru-yi Ju, Ping Lin, Yi Cao

“…The RF model performs better than other models. On the test set, the AUC of the RF model is 0.84 [(95% confidence interval (CI): 0.80–0.88], an accuracy of 0.78 (95% CI: 0.69–0.86), a precision of 0.61 (95% CI: 0.45–0.77), a recall of 0.73 (95% CI: 0.58–0.89), and a specificity of 0.79 (95% CI: 0.70–0.89).ConclusionsIn this study, five machine learning methods were used to build postherpetic itch risk prediction models by analyzing historical case data, and the optimal model was selected through comparative analysis, with the random forest model being the top performing model.…”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. …”
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Comparative Analysis of a Quantum SVM With an Optimized Kernel Versus Classical SVMs by Matheus Cammarosano Hidalgo

“…Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…Also, this research relies on feature selection to identify the most optimal features for use in the prediction model. …”
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Appraising the Pile Settlement Rates by Support Vector Regression Optimized Using the Novel Optimization Algorithms by Argyros Maris

“…Moreover, several metrics have been used to assess the overall performance of models. The R2 of the training phase for SVR-FDA was found 99.39 percent shows a great modeling process, while the RMSE of this model was calculated 0.4286 mm. …”
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