A multimodal model for protein function prediction by Yu Mao, WenHui Xu, Yue Shun, LongXin Chai, Lei Xue, Yong Yang, Mei Li

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Regression Models for Predicting Physicochemical Properties of Biochar by Chiaw Hui Chiew, Li Yee Lim, Pei Ying Ong, Chunjie Li, Yee Van Fan

“…This study evaluates the effectiveness of various regression models in predicting the physicochemical properties of biochar, essential for sustainable agriculture and environmental remediation. …”
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Model for non-periodic time series prediction by CAO Jianwen, WEI Xingbao, YANG Yi, LI Caihong, ZHAO Wenqing

“…To effectively forecast non-periodic time series, the ILTNet model was proposed based on the Informer model. The ILTNet model integrated linear prediction (AR model) and nonlinear prediction (Informer model and recurrent skip components), enabling effective capture of long-term dependencies. …”
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STVMamba: precipitation nowcasting with spatiotemporal prediction model by Maoyang Zou, Longrui Wen, Yuanyuan Huang, Yuan He, Jingzhong Xiao

“…Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. …”
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Probabilistic medical predictions of large language models by Bowen Gu, Rishi J. Desai, Kueiyu Joshua Lin, Jie Yang

“…Abstract Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. …”
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Modelling and predicting biogeographical patterns in river networks by Sabela Lois

“…The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. …”
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Prediction Model of Pipeline Push Resistance in Tunnels by Lianmin Cao, Mingxing Zhu, Dekang Zhang, Ying Zhen, Xu Zhang, Yuguang Cao

“…Accurate determination of pipeline push resistance and the selection of an appropriate pipe pusher model are crucial factors for successful construction. …”
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Enhancing Accuracy and Explainability of Recidivism Prediction Models by Tammy Babad, Soon Ae Chun

“…Automated prediction models suffer from low accuracy and are associated with criticism for biased and unexplainable decision-making. …”
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Current Genetic Models for Prediction of Primary Myelofibrosis by LB Polushkina, VA Shuvaev, MS Fominykh, YuA Krivolapov, EA Belyakova, ZP Asaulenko, V Motyko, LS Martynenko, MP Bakai, NYu Tsybakova, SV Voloshin, SS Bessmeltsev, AV Chechetkin, IS Martynkevich

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Modeling and Prediction of Ride-Sharing Utilization Dynamics by Tal Altshuler, Yaniv Altshuler, Rachel Katoshevski, Yoram Shiftan

“…This feature space is then used to model the dynamics of ride-sharing utilization over time. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. …”
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On the predictability limit of convection models of the Earth's mantle by Léa Bello, Nicolas Coltice, Tobias Rolf, Paul J. Tackley

Subjects: “…convection models…”
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AI model for predicting asthma prognosis in children by Elham Sagheb, MS, Chung-Il Wi, MD, Katherine S. King, MS, Bhavani Singh Agnikula Kshatriya, MS, Euijung Ryu, PhD, Hongfang Liu, PhD, Miguel A. Park, MD, Hee Yun Seol, MD, Shauna M. Overgaard, PhD, Deepak K. Sharma, PhD, Young J. Juhn, MD, Sunghwan Sohn, PhD

“…The prediction model at age 12 showed the highest performance. …”
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CPLLM: Clinical prediction with large language models. by Ofir Ben Shoham, Nadav Rappoport

“…We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. …”
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Prediction models for COVID-19 disease outcomes by Cynthia Y. Tang, Cheng Gao, Kritika Prasai, Tao Li, Shreya Dash, Jane A. McElroy, Jun Hang, Xiu-Feng Wan

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Air Quality Prediction Model for Monitoring AQI by Das Aritra, Gupta Himanshu

“…This research proposes a complete framework that incorporates deep learning models—specifically Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks—with ensemble learning methods, particularly Extreme Gradient Boosting (XGBoost), to predict AQI. …”
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Macroscopic Modelling of Predicted Automated Vehicle Emissions by Mohammed Obaid, Arpad Torok

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The Application of Machine Learning Models to Predict Stillbirths by Oguzhan Gunenc, Sukran Dogru, Fikriye Karanfil Yaman, Huriye Ezveci, Ulfet Sena Metin, Ali Acar

“…<i>Background and Objectives</i>: This study aims to evaluate the predictive value of comprehensive data obtained in obstetric clinics for the detection of stillbirth and the predictive ability set of machine learning models for stillbirth. …”
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Predicting fetal growth retardation: mathematical modeling by E. V. Kudryavtseva, V. V. Kovalev, A. A. Dektyarev, I. I. Baranov

“…Due to no effective treatments for FGR are available, its prevention and prognosis are of extreme relevance.Aim: development of prognostic clinical and anamnestic mathematical model for assessing a risk of developing FGR during pregnancy.Materials and Methods. …”
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An interpretable machine learning model to predict hospitalizations by Hagar Elbatanouny, Hissam Tawfik, Tarek Khater, Anatoliy Gorbenko

“…Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. …”
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