Prediction of failures in the project management knowledge areas using optimized ensemble models in software companies by Lamia Berriche, Abderrazak Loulizi

“…CatBoost demonstrated the best accuracy, achieving 94.02%, and demonstrated the best generalization. These models showed strong performance in predicting failures related to scope and cost management but were less accurate when predicting failures in human resource management. …”
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Prediction of the need for maintenance of rigid pavements using finite element models and artificial neural networks by Lorena Jacqueline Chamorro Chamorro, Elisa Dominguez Sotelino

“…The developed rigid pavement management system uses the Artificial Neural Networks (ANN) technique for the prediction of both pavement response to fatigue accumulation and the behavior of the modeled pavement with a high degree of precision. …”
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High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. by Phaedra Agius, Aaron Arvey, William Chang, William Stafford Noble, Christina Leslie

“…Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

Subjects: “…Short-term temperature prediction…”
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Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily by Maksymilian Mądziel, Tiziana Campisi

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Smooth predictions for age-period-cohort models: a comparison between splines and random process by Connor Gascoigne, Andrea Riebler, Theresa Smith

“…Abstract Background Age-Period-Cohort (APC) models are well used in the context of modelling health and demographic data to produce smooth predictions of each time trend. …”
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Advancements in Predictive Maintenance: A Bibliometric Review of Diagnostic Models Using Machine Learning Techniques by Nontuthuzelo Lindokuhle Vithi, Colin Chibaya

“…This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. …”
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Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation by Weijie Wu, Weijie Wu, Zheng Zhang, Shuailei Wang, Ru Xin, Dong Yang, Weifeng Yao, Ziqing Hei, Chaojin Chen, Gangjian Luo

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Antibody Fab‐Fc properties outperform titer in predictive models of SIV vaccine‐induced protection by Srivamshi Pittala, Kenneth Bagley, Jennifer A Schwartz, Eric P Brown, Joshua A Weiner, Ilia J Prado, Wenlei Zhang, Rong Xu, Ayuko Ota‐Setlik, Ranajit Pal, Xiaoying Shen, Charles Beck, Guido Ferrari, George K Lewis, Celia C LaBranche, David C Montefiori, Georgia D Tomaras, Galit Alter, Mario Roederer, Timothy R Fouts, Margaret E Ackerman, Chris Bailey‐Kellogg

“…These results suggest that predictive modeling with measurements of antibody properties can provide detailed correlates with robust predictive power, suggest directions for vaccine improvement, and potentially enable discovery of mechanistic associations.…”
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Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…The SVR model had large variations from the actual values and hence was not very useful for our predictions. …”
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Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks. by Victoria Dominguez Almela, Abigail R Croker, Richard Stafford

“…Predictive models are often complex to produce and interpret, yet can offer valuable insights for management, conservation and policy-making. …”
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Going concern prediction – A horse race between traditional and regularization machine learning models by Tina Vuko, Slavko Šodan, Ivana Perica

“…Regularization machine learning (ML) methods have been increasingly applied in accounting research, offering new possibilities in predictive modeling. Their forte lies in the effective regularization methods for resolving the biggest concern of generalization, which is the risk of overfitting the training data. …”
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Shoreline dynamics prediction using machine learning models: from process learning to probabilistic forecasting by Afshar Adeli, Afshar Adeli, Ali Dastgheib, Ali Dastgheib, Dano Roelvink, Dano Roelvink, Dano Roelvink

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Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models by Y.-C. Huang, J. Padarian, B. Minasny, A. B. McBratney

“…This study introduces an innovative application of Monte Carlo conformal prediction (MC-CP) to quantify uncertainty in deep-learning models for predicting clay content from mid-infrared spectroscopy. …”
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Advanced Default Risk Prediction in Small and Medum-Sized Enterprises Using Large Language Models by Haonan Huang, Jing Li, Chundan Zheng, Sikang Chen, Xuanyin Wang, Xingyan Chen

“…However, data on the commercial bills of SMEs are scarce and challenging to gather, which has impeded research on risk prediction for these businesses. This study aims to address this gap by leveraging 38 multi-dimensional, non-financial features collected from 1972 real SMEs in China to predict bill default risk. …”
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Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data by Karolina Wanat, Klaudia Michalak, Elżbieta Brzezińska

“…Methods: Predictive models were created using the physicochemical properties of drugs, and multiple linear regression and a data mining method, i.e., MARSplines, were used to build them. …”
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Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions by Gabby B. Joseph, Charles E. McCulloch, Michael C. Nevitt, Nancy E. Lane, Sharmila Majumdar, Thomas M. Link

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Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models by Yanhua Wang, Yanhua Wang, Yanhua Wang, Yanhua Wang, Yi Wang, Yi Wang, Yi Wang, Linhong Song, Jun Li, Jun Li, Jun Li, Yuanyuan Xie, Yuanyuan Xie, Yuanyuan Xie, Lei Yan, Lei Yan, Lei Yan, Siqi Hu, Siqi Hu, Siqi Hu, Zhichun Feng, Zhichun Feng, Zhichun Feng, Zhichun Feng

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Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling by Eliott Park, Saeed Izadi

“…In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. …”
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XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. …”
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