Benchmarking machine learning models for predicting lithium ion migration by Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

“…With LiTraj, we demonstrate that classical ML models and graph neural networks (GNNs) for structure-to-property prediction of percolation and migration barriers can distinguish between “fast” and “poor” ionic conductors. …”
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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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Explainable models for predicting crab weight based on genetic programming by Tao Shi, Lingcheng Meng, Limiao Deng, Juan Li

“…Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. More importantly, the generated models can provide potential interpretability, which is particularly valuable for domain experts in fisheries management and ecological research.…”
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PREDICTING THE SHELF LIFE OF SUNFLOWER MEAL USING KINETIC MODELS by T. Matveeva, V. Papchenko, P. Petik, N. Staroselska, V. Khareba, O. Khareba

“…The study proposes a method for predicting the oxidative stability of sunflower meal during long-term storage using the Arrhenius model, which describes the dependence of the reaction rate on temperature. …”
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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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Predicting avalanche danger in northern Norway using statistical models by K.-U. Eiselt, R. G. Graversen, R. G. Graversen

“…The binary-case RF model exhibits a much higher overall accuracy (76 %) than the four-level case RF model (57 %), which is due to the latter model often misclassifying ADL 1 as ADL 2 and ADL 4 as ADL 3. …”
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Recent advances in explainable Machine Learning models for wildfire prediction by Abira Sengupta, Brendon J. Woodford

“…Machine Learning (ML) and Artificial Intelligence models have emerged to predict both the onset of wildfires and evaluate the extent of damage a wildfire would cause. …”
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Prediction of hepatitis-C virus using statistical learning models by Shalini Kumari, Subhajit Das, Prashant Kumar Sonker, Agni Saroj, Mukesh Kumar

“…The models were evaluated using various performance metrics, and a comparative analysis using non-parametric tests was conducted to evaluate the statistical significance of the model. …”
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Comparative Study of Deep Learning Models for Predicting Stock Prices by Van-Thang Duong, Duc-Tuan-Anh Nguyen, Thi-Thu-Hang Pham, Van-Hau Nguyen, Van-Quoc Anh Le

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Processing and Shelf-Life Prediction Models for Ready-to-Eat Crayfish by Qian Li, Jieyu Lei, Keying Su, Xiaoying Chen, Laihoong Cheng, Chunmin Yang, Shiyi Ou

“…This study investigated the production process of ready-to-eat crayfish, focusing on changes in sensory quality, pH, total volatile base nitrogen (TVB-N), total viable count (TVC), acid value (AV), springiness, and hardness during storage at 4 °C, 25 °C, and 37 °C. A shelf-life prediction model was developed using the Arrhenius model. …”
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Zero-Shot Prediction of Conversational Derailment With Large Language Models by Kenya Nonaka, Mitsuo Yoshida

“…First, we measured the performance of the most commonly used LLMs in predicting conversational derailments and found that the zero-shot prediction performance is comparable to that of traditional fine-tuning approaches. …”
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Development and Validation of a Predictive Model for Growth of Salmonella Infantis in Ground Turkey by Thomas P. Oscar

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Frailty risk prediction models for patients undergoing maintenance hemodialysis in China: a systematic review by Yu Zhong, Songmei Cao, Liangying Chen, Teng Li, Wei Ye

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SYNTHESIS OF ECONOMETRIC AND NEURAL NETWORK MODELS FOR INDICATORS PREDICTION IN RESEARCH AND INNOVATION IN THE RUSSIAN FEDERATION by I. Kolmakov, M. Domozhakov

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Model for Reliability Prediction of Thick Film Resistors by D. S. Campbell, R. B. Pranchov

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

Subjects: “…Protein function prediction…”
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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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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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