Triadic balance and network evolution in predictive models of signed networks by Hsuan-Wei Lee, Pei-Chin Lu, Hsiang-Chuan Sha, Hsini Huang

“…To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. …”
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Machine learning proteochemometric models for Cereblon glue activity predictions by Francis J. Prael, III, Jiayi Cox, Noé Sturm, Peter Kutchukian, William C. Forrester, Gregory Michaud, Jutta Blank, Lingling Shen, Raquel Rodríguez-Pérez

“…For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). …”
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Predictive models and WTAP targeting for idiopathic pulmonary fibrosis (IPF) by Guo-Dong Li, Juan Li, Jia-Qi Fan, Jun-Yi Li, Bin Zhao, Xiao Chen

“…This research analyzed the GSE93606 dataset of 20 non-IPF and 154 IPF patients, identifying 26 m6A regulators and developing predictive models with RF and SVM, assessed via ROC curves. …”
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On Some Limitations of Current Machine Learning Weather Prediction Models by Massimo Bonavita

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Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction by Rusul L. Abduljabbar, Hussein Dia, Pei-Wei Tsai

“…This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. …”
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Reviews on Imaging-based Risk Prediction Models for Ischemic Stroke by Cui Liuping, Liu Ran, Liu Yumei, Zhou Fubo, Tao Yunlu, Xing Yingqi

“…Integrating image-based biomarkers into existing risk-prediction models may enhance risk stratification accuracy. …”
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LLM-driven semantic explanations for soil moisture prediction models by Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

“…We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. …”
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Prediction of Vapor-Liquid Equilibria Using CEOS /GE Models

“…To predict VLE data in multicomponent symmetric and asymmetric mixtures such as systems that contain light gases (nitrogen, carbon dioxide, etc.) and heavy hydrocarbons, the SRK equation of state has been combined with excess Gibbs energy models. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Alternatively, federated learning (FL) can be utilized to train models based on data located at multiple sites. Method We propose two methods for training time-to-event prediction models based on distributed data, relying on FL algorithms, for time-to-event prediction models. …”
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Integrated Machine Learning Models for Bakery Product Defect Prediction by Nataliia Zaiets, Nataliia Lutska, Lidiia Vlasenko

Subjects: “…defect prediction…”
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Satellite-Based Validation of Contrail Prediction Models for Sustainable Aviation by Baddireddi Sree Chandana, Pelleti Nandieswar Reddy, Rithvika Alapati, Sai Aswath Reddy, Radha Doraisamy, Uma Sankari

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Groundwater quality parameters prediction based on data-driven models by Mohammed Falah Allawi, Yasir Al-Ani, Arkan Dhari Jalal, Zainab Malik Ismael, Mohsen Sherif, Ahmed El-Shafie

“…According to the evaluation results, adding more input variables can sometimes increase the efficacy of the proposed models with regard to prediction accuracy. Moreover, the findings show that the PNN model provides a promising performance in predicting the groundwater’s water quality (WQ) matrices, showing superior performance compared to the RBFNN model.…”
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Guide to evaluating performance of prediction models for recurrent clinical events by Laura J. Bonnett, Thomas Spain, Alexandra Hunt, Jane L. Hutton, Victoria Watson, Anthony G. Marson, John Blakey

“…Therefore, prediction models for outcomes associated with chronic conditions should include all repeated events. …”
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Assimilating Observed Surface Pressure Into ML Weather Prediction Models by L. C. Slivinski, J. S. Whitaker, S. Frolov, T. A. Smith, N. Agarwal

“…Abstract There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium‐range forecasts, not their performance in cycling data assimilation (DA) systems. …”
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Machine learning models for prognosis prediction in regenerative endodontic procedures by Jing Lu, Qianqian Cai, Kaizhi Chen, Bill Kahler, Jun Yao, Yanjun Zhang, Dali Zheng, Youguang Lu

“…Abstract Background This study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure. …”
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Evaluating Road Crash Severity Prediction with Balanced Ensemble Models by Alexei Roudnitski

“…The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. …”
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Research on Customer Churn Prediction Using Machine Learning Models by Jia Xiaolei

“…However, in uncomplex customer churn predictions, the decision tree model gets a high prediction score due to its accuracy rate of 90.8%. …”
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Risk Prediction Models for Perioperative Hypothermia: A Systematic Review by Liu J, Liu F, Xu W, Du L, Li Y, Liang A, Li B, Zhang M

“…Data collection followed the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS). The prediction model risk of bias assessment tool (PROBAST) checklist assessed the risk of bias and applicability of the data.Results: This study included 11 papers (14 risk prediction models). …”
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Prediction of coalbed methane productivity based on neural network models by JIN Yi, ZHENG Chenhui, SONG Huibo, MA Jiaheng, YANG Yunhang, LIU Shunxi, ZHANG Kun, NI Xiaoming

“…The prediction accuracy is significantly higher than the BP model.ConclusionsThe model has good stability and high prediction accuracy. …”
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USING MACHINE LEARNING MODELS TO PREDICT THE NUMBER OF ELECTRONIC TRANSACTIONS by Andrey Valeryevich Malikov, David Mherovich Aghajanyan, Pavel Petrovich Tarasevich

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