Predicting high confidence ctDNA somatic variants with ensemble machine learning models by Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

“…We built two Random Forest (RF) models for predicting high confidence somatic ctDNA variants in low and high depth cfDNA NGS data. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Our results indicate that ML models can feasibly replace the empirical USLE model for erosion prediction. …”
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Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions by Jun Wang, Ouyang Chen, Zhenfei Zhan, Zhiwei Zhao, Huanhuan Bao

“…Finally, the prediction accuracy of the models was compared based on error functions. …”
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Neutrosophic Statistical Regression Models for Predicting the Incidence of Nosocomial Infections in Post-Trauma Patients by Nairovys Gómez Martinez, Gerardo Ramos Serpa, Riber Fabian Donoso Noroña, Gloria Rebeca Medina Naranjo

Subjects: “…regression models…”
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Leveraging Large Language Models for Predicting Postoperative Acute Kidney Injury in Elderly Patients by Hanfei Zhu, Ruojiang Wang, Jiajie Qian, Yuhao Wu, Zhuqing Jin, Xishen Shan, Fuhai Ji, Zixuan Yuan, Tingrui Pan

“…Objective: The objective of this work is to develop a framework based on large language models (LLMs) to predict postoperative acute kidney injury (AKI) outcomes in elderly patients. …”
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Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models by Abdul Rehman, Nadeem Akhtar, Omar H. Alhazmi

“…We consequently used deep learning models to predict cotton diseases, i.e., Aphids, Armyworms, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy leaf. …”
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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models by Sherif S. M. Ghoneim, Mohammed Baz, Ali Alzaed, Yohannes Tesfaye Zewdie

“…The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. …”
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Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence by Yunish Khan, Vinod Kumar, Amel Gacem, Anurag Satpathi, Parul Setiya, Kumari Surbhi, Ajeet Singh Nain, Dinesh Kumar Vishwakarma, Ahmad J. Obaidullah, Krishna Kumar Yadav, Ozgur Kisi

“…These findings highlight the superior efficiency of hybrid models in predicting soybean disease severity based on weather indices in the study region.…”
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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…This review aims to analyze various aspects of sugar production, including sugar prediction, processing techniques, and sugarcane quality parameters, and focuses on the use of sugarcane juice parameters to construct predictive models. …”
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Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts by Rachel Bennett, Stephanie L. Pierce, Talayeh Razzaghi

“…Our comparative analysis shows logistic regression to be the most accurate in predicting the need for cesareans in the nulliparous cohort, while random forest outperformed other models in the combined dataset.…”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…Conclusion Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes.…”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

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Predicting the distribution of Ixodes ricinus in Europe: integrating microclimatic factors into ecological niche models by Arda Cem Kuyucu, Olcay Hekimoglu, Ala Tabor

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Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination by Yunjeong Choi, Jaeyu Park, Hyejun Kim, Young Joo Lee, Yongbin Lee, Yong Sung Choi, Seung Geun Yeo, Jiseung Kang, Masoud Rahmati, Hayeon Lee, Dong Keon Yon, Jinseok Lee

“…We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. …”
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A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python by Li Taobei

“…Performance metrics were used to evaluate the models' predictive capacity. With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. …”
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Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models by Chaoru Lu, Jing Dong, Anuj Sharma, Tingting Huang, Skylar Knickerbocker

“…Speed-volume-density relationship and capacity are key elements in modelling traffic operations, designing roadways, and evaluating facility performance. …”
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Predicting vector distribution in Europe: at what sample size are species distribution models reliable? by Lianne Mitchel, Lianne Mitchel, Guy Hendrickx, Ewan T. MacLeod, Cedric Marsboom, Cedric Marsboom

“…IntroductionSpecies distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. …”
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MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma by Song Gao, Li Zhao, Nan Li, Xiaoming Zhou, Chongfeng Duan

“…The predictive models were built via logistic regression (LR), support vector machine (SVM) and naive Bayes. …”
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GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity by Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen

“…Results In this paper, we propose a new method, called GS-DTA, for predicting DTA based on graph and sequence models. GS-DTA takes simplified molecular input line input system (SMILES) of the drug and the protein amino acid sequence as input. …”
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Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars by Atchadeou Yranawa Katatchambo, Şinasi Bingöl

“…For the MARS, TreeNet and RF models, the TreeNet model produced the best prediction, while for the ANN_5 and ANN_10 models, the ANN_5 model produced the best prediction. …”
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