Predicting for mortality rate using regression analysis in patient with burn injury by O. O. Zavorotniy, E. V. Zinoviev, D. V. Kostyakov

“…The final algorithm included 18 predictors. The model allows predicting a positive outcome of treatment and the likelihood of a fatal outcome with an accuracy of 93 and 87 % respectively.Conclusion. …”
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Digital Twin and Data-Driven Remaining Useful Life Prediction of Gearbox by Quanbo Lu, Mei Li, Xiaojuan Huang

“…To further improve prediction accuracy, the paper employs the Central Particle Swarm Optimization algorithm to merge both theoretical and actual RUL values. …”
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RUL prediction method based on cross-view hybrid network model by Ai Yandi, Fang Dong, Tian Zhiping, Yan Kaiyang

“…Secondly, a RUL regression algorithm integrating Transformer encoder and nonlinear fitter is developed to automatically learn the correlation between features in different views and predict RUL. …”
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Curing simulation and data-driven curing curve prediction of thermoset composites by Chenchen Wu, Ruming Zhang, Pengyuan Zhao, Liang Li, Dingguo Zhang

“…Then, the temperature–time and the resulting degree-of-cure-time curves obtained from finite element simulations were created for training the prediction models using machine learning approaches of support vector regression (SVR), back propagation (BP) neural network and BP neural network optimized by genetic algorithm (GA-BP). …”
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RUL Prediction Based on MBGD-WGAN-GRU for Lithium-Ion Batteries by Zhiguo Zhao, Ke Li, Yibo Dai, Biao Chen, Yeqin Wang, Qian Zhao

“…To address the challenges associated with acquiring complete charge-discharge cycle data and extracting health indicator factors (IHFs) from fragmented datasets in current automotive lithium-ion batteries (LIBs), this study proposes a novel online remaining useful life (RUL) prediction method. First, the IHF, which captures battery aging characteristics, is extracted from raw LIBs data, and the dataset is partitioned into training (70%) and testing (30%) subsets. …”
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Machine learning-based prediction of LDL cholesterol: performance evaluation and validation by Jing-Bi Meng, Zai-Jian An, Chun-Shan Jiang

“…Objective This study aimed to validate and optimize a machine learning algorithm for accurately predicting low-density lipoprotein cholesterol (LDL-C) levels, addressing limitations of traditional formulas, particularly in hypertriglyceridemia. …”
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Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence by Md. Mottahir Alam, Ahteshamul Haque, Asif Irshad Khan, Samir Kasim, Amjad Ali Pasha, Aasim Zafar, Kashif Irshad, Anis Ahmad Chaudhary, Md. Samsuzzaman, Rezaul Azim

“…Moreover, Golden Eagle Optimization (GE)-based deep AlexNet algorithm is proposed for predicting the parameter variation and their effect on absorbance. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. …”
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Enhancing freight train delay prediction with simulation‐assisted machine learning by Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin

“…Additionally, utilization rates—except for the receiving yard—enhance the predictions.…”
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Artificial neural networks in predicting impaired bone metabolism in diabetes mellitus by S. S. Safarova

“…The ANN model was trained by optimizing the relationship between a set of input data (a number of clinical and laboratory parameters: gender, age, body mass index, duration of diabetes mellitus, etc.) and a set of corresponding output data (variables reflecting the state of bone metabolism: bone mineral density, markers of bone remodeling).Results. The ANN-based algorithm predicted estimated values of bone metabolism parameters in the examined individuals by generating output data using deep learning. …”
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Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning by Jianping Sun, Hantao Cao, Biao Geng, Zhaoping Tang, Xiaopeng Li

“…The demand prediction of emergency resources is helpful for rational allocation and optimization of emergency resources for railway rescue when emergency incident occurs. …”
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The Influence of Non-Landslide Sample Selection Methods on Landslide Susceptibility Prediction by Yu Fu, Zhihao Fan, Xiangzhi Li, Pengyu Wang, Xiaoyue Sun, Yu Ren, Wengeng Cao

“…Additionally, the EIV method identified smaller, more concentrated high-susceptibility zones, covering 87.37% of historical landslide points, compared to the larger, less precise zones predicted by other methods. This study highlights the effectiveness of the EIV method in refining non-landslide sample selection and improving landslide susceptibility prediction, providing valuable insights for disaster risk reduction and land use planning.…”
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Collaborative multiview time series modeling for vehicle maintenance demand prediction by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye, Fujie Ren, Guofang Wu

“…Abstract Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. …”
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Cosmological Prediction of the Void and Galaxy Clustering Measurements in the CSST Spectroscopic Survey by Yingxiao Song, Qi Xiong, Yan Gong, Furen Deng, Kwan Chuen Chan, Xuelei Chen, Qi Guo, Guoliang Li, Ming Li, Yun Liu, Yu Luo, Wenxiang Pei, Chengliang Wei

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Data-driven prediction of cardiovascular and cerebrovascular diseases in a nationwide study by Sehyun Kim, Beomsang Ryu, Mingee Choi, Sangyon Lee, Jaeyong Shin, Sok Chul Hong

“…The logistic regression model incorporating variables selected by the LASSO algorithm exhibited superior predictive performance relative to other models, although the differences were not statistically significant. …”
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Fecal metabolites as early-phase biomarkers and prediction panel for ischemic stroke by Ke Xu, Zhe Ren, Shuang Zhao, Yi Ren, Jiaolin Wang, Wentao Wu, Zicheng Hu, Fei He, Dianji Tu, Qi Zhong, Jianjun Chen, Peng Xie

“…The important differential metabolites were identified by the random forest algorithm, a prediction panel was developed to distinguish ischemic stroke patients from healthy individuals. …”
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A systematic review of neural network applications for groundwater level prediction by Samuel K. Afful, Cyril D. Boateng, Emmanuel Ahene, Jeffrey N. A. Aryee, David D. Wemegah, Solomon S. R. Gidigasu, Akyana Britwum, Marian A. Osei, Jesse Gilbert, Haoulata Touré, Vera Mensah

“…Abstract Physical models have long been employed for groundwater level (GWL) prediction. Recently, artificial intelligence (AI), particularly neural networks (NNs), has gained widespread use in forecasting GWL. …”
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Breast cancer survival prediction using an automated mitosis detection pipeline by Nikolas Stathonikos, Marc Aubreville, Sjoerd deVries, Frauke Wilm, Christof A Bertram, Mitko Veta, Paul J vanDiest

“…Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter‐ and intraobserver variation and reproducibility challenges that may hamper its clinical utility. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…Accurate wind speed prediction (WSP) remains essential for optimizing energy management in small-scale domestic windmills. …”
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Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach by Gideon Addo, Bismark Amponsah Yeboah, Michael Obuobi, Raphael Doh-Nani, Seidu Mohammed, David Kojo Amakye

“…Chi-square tests assessed diabetes-symptom associations, and the Boruta algorithm examined feature influence. Seven ML classification models were evaluated for predictive accuracy. …”
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