Statistical and machine learning models for predicting university dropout and scholarship impact. by Stephan Romero, Xiyue Liao

“…First, we aim to build predictive models to learn of the significant socioeconomic and academic features associated with students' dropout risk. …”
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Soft Computing Models to Predict Pavement Roughness: A Comparative Study by Panos Georgiou, Christina Plati, Andreas Loizos

“…The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

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Predicting clinical trial duration via statistical and machine learning models by Joonhyuk Cho, Qingyang Xu, Chi Heem Wong, Andrew W. Lo

“…We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…We confront the issue of class imbalance by applying dynamic re-sampling and weight adjustment techniques, ensuring balanced model performance across varied data segments. Extensive evaluations on standard datasets validate the effectiveness of our proposed model, revealing enhanced prediction accuracy and model interpretability when compared to more traditional techniques. …”
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Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making by İrem Tanyıldızı Baydili, Burak Tasci

“…This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…The proposed explainable AI models achieved an F1-score of 85%, verifying their high reliability in predicting depression. …”
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The Impact of Prediction Models on Energy-Aware Resource Management in FaaS Platforms by Shahrokh Vahabi, Francesca Righetti, Carlo Vallati, Nicola Tonellotto

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Software refactoring prediction evaluation method based on deep learning models by Yichi ZHANG, Yang ZHANG, Yanlei LI, Kun ZHENG, Wei LIU

“…Aiming at the lack of research on the performance of deep learning models in predicting software refactoring in the current field, a deep learning-based software refactoring prediction evaluation method was proposed to assess the refactoring predictive performance of these models. …”
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Soybean yield prediction leveraging advanced ensemble machine learning models by RAM MANOHAR PATEL, KAMAL BUNKAR

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Machine Learning Models on MOBA Gaming: League of Legends Winner Prediction by Kaan Arık

“…LoL (League of Legends) is one of the most popular MOBA games. Predicting winners in online games has become an essential application for machine learning models. …”
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Comparison of CatBoost and LightGBM Models for Air Humidity Prediction by Tangkas Surya Wibawa, Novita Kurnia Ningrum, Ahmad Syahreza

“…This study uses historical weather data from the Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) to evaluate the performance of two combination machine learning models, LightGBM and CatBoost, in predicting air humidity. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…Advances in machine learning (ML) and computing power, data analysis, and model generation using ML techniques, such as neural networks, enable predictive modeling for GET. …”
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Analysis and Prediction for Air Quality Using Various Machine Learning Models by To-Hieu Dao, Hoang Van Nhat, Hoang Quang Trung, Vu Hoang Dieu, Nguyen Thi Thu, Duc-Nghia Tran, Duc-Tan Tran

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Models for predicting the performance of fertilizer metering in seed cum fertilizer drill by Roger T. Spagnolo, David P. da Rosa, Marivan da S. Pinho, Cheiner S. Schiavon, Diego Schmechel

“…ABSTRACT This study aimed to establish mathematical models that predict the performance of helical fertilizer metering according to the longitudinal and transverse inclinations, angular speed, and helical pitch. …”
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Preoperative prediction of gastric cancer T-staging based on ordinal regression models by O. V. Krasko, M. Yu. Reutovich, A. L. Patseika

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Skeletal age prediction models by maturity status in male soccer players by Luis Alberto Flores, Christopher McLaren-Towlson, Lidia G. De León, Fabiana Bonito, Pedro Mil-Homens, Iván Peña-González, Maria Isabel Fragoso

“…The prediction models are a reliable and cost-effective method to estimate SA in male soccer players.…”
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Developing and validating machine learning models to predict next-day extubation by Samuel W. Fenske, Alec Peltekian, Mengjia Kang, Nikolay S. Markov, Mengou Zhu, Kevin Grudzinski, Melissa J. Bak, Anna Pawlowski, Vishu Gupta, Yuwei Mao, Stanislav Bratchikov, Thomas Stoeger, Luke V. Rasmussen, Alok N. Choudhary, Alexander V. Misharin, Benjamin D. Singer, G. R. Scott Budinger, Richard G. Wunderink, Ankit Agrawal, Catherine A. Gao, NU SCRIPT Study Investigators

“…We used three data encoding/imputation strategies and built XGBoost, LightGBM, logistic regression, LSTM, and RNN models to predict next-day extubation. We compared model predictions and actual events to examine how model-driven care might have differed from actual care. …”
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External validation of risk prediction models for post-stroke mortality in Berlin by Jessica L Rohmann, Tobias Kurth, Heinrich J Audebert, Marco Piccininni, Lukas Reitzle

“…Objectives Prediction models for post-stroke mortality can support medical decision-making. …”
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Expression‐based machine learning models for predicting plant tissue identity by Sourabh Palande, Jeremy Arsenault, Patricia Basurto‐Lozada, Andrew Bleich, Brianna N. I. Brown, Sophia F. Buysse, Noelle A. Connors, Sikta Das Adhikari, Kara C. Dobson, Francisco Xavier Guerra‐Castillo, Maria F. Guerrero‐Carrillo, Sophia Harlow, Héctor Herrera‐Orozco, Asia T. Hightower, Paulo Izquierdo, MacKenzie Jacobs, Nicholas A. Johnson, Wendy Leuenberger, Alessandro Lopez‐Hernandez, Alicia Luckie‐Duque, Camila Martínez‐Avila, Eddy J. Mendoza‐Galindo, David Cruz Plancarte, Jenny M. Schuster, Harry Shomer, Sidney C. Sitar, Anne K. Steensma, Joanne Elise Thomson, Damián Villaseñor‐Amador, Robin Waterman, Brandon M. Webster, Madison Whyte, Sofía Zorilla‐Azcué, Beronda L. Montgomery, Aman Y. Husbands, Arjun Krishnan, Sarah Percival, Elizabeth Munch, Robert VanBuren, Daniel H. Chitwood, Alejandra Rougon‐Cardoso

“…Methods Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. …”
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