University proceedings. Volga region. Technical sciences by A.A. Troshin, I.V. Balakhonova

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Prediction of mechanical behavior of friction stir welded joints of AA3003 aluminum alloy by I. Chekalil, A. Miloudi, M.P. Planche, A. Ghazi

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Predicting Residential Energy Consumption in South Africa Using Ensemble Models by David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran

“…This study presents ensemble machine learning (ML) models for predicting residential energy consumption in South Africa. …”
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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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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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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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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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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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Predicting models for work outcomes in patients with schizophrenia and its clinical application by Chika Sumiyoshi, Satsuki Ito, Junya Matsumoto, Hidenaga Yamamori, Michiko Fujimoto, Yuka Yasuda, Tomiki Sumiyoshi, Ryota Hashimoto

“…Abstract Negative symptoms and social function have been known to predict work outcomes in patients with schizophrenia. …”
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Predicting the Quality of Pasteurized Vegetables Using Kinetic Models: A Review by Muhammad Aamir, Mahmoudreza Ovissipour, Shyam S. Sablani, Barbara Rasco

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Analysis of Empirical Models for Predicting the Rupture Force in Four-Pile Caps by Raphael Saverio Spozito, André Luis Christoforo, Fernando Menezes de Almeida Filho, Rodrigo Gustavo Delalibera, Elvys Dias Reis, André Luís Lima Velame Branco

“…These experimental records are frequently utilized to evaluate analytical models focusing on less conservative than normative models to predict rupture force. …”
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Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets by Onur Çelebi, Erhan Demireli

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Temporally robust models for predicting seed yield of moist‐soil plants by Joshua M. Osborn, Heath M. Hagy, Matthew D. McClanahan, Matthew J. Gray

“…Generally, all models explained significant variation (r2 = 0.85–0.98) and accurately predicted seed production in moist‐soil plants (r2 = 0.84–0.97). …”
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Predicting breeding values in Murrah buffaloes using random regression models by ANAND JAIN, ASHISH RANJAN, I D GUPTA, H K SINGH

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Predicting prostate cancer metastasis in Ghana: Comparison of multiparametric and PSA models. by Frank Obeng, Joyce Naa Aklerh Okai, Edward Sutherland

“…This study evaluates and compares the predictive abilities of a multiparametric model and a PSA-alone model in forecasting metastasis in prostate cancer patients.…”
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A systematic review of data and models for predicting food flavor and texture by Michael Gunning, Ilias Tagkopoulos

“…This review systematically examines the current landscape of data resources and computational models for predicting food flavor and texture. Taste is the most well-defined sensory component, and molecular classification is aligned with the five basic tastes: sweet, sour, bitter, salty, and umami. …”
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Predicting the Time of Bus Arrival for Public Transportation by Time Series Models by Süleyman Mete, Erkan Çelik, Muhammet Gül

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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. …”
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Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models by Didem Güleryüz

“…Therefore, the SVR model has accurate prediction results with the highest R2 and the least root mean square error values in the testing phase. …”
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