ALGORITHM FOR ASSESSING TIME AND COST RISKS AT ENTERPRISES OF THE MILITARY-INDUSTRIAL COMPLEX by N.D. Pechalin, A.G. Finogeev

“…The objective is to create models and an algorithm for predictive risk analysis when drawing up a calendar schedule for the implementation of project tasks to support decisionmaking by managers of defense industry enterprises. …”

Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

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Machine Learning in the Teaching Quality of University Teachers: Systematic Review of the Literature 2014–2024 by Walter Zambrano-Romero, Ciro Rodriguez, Josselyn Pita-Valencia, Walter José Zambrano-Romero, José Manuel Moran-Tubay

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DISEÑO DE UN PICOSATÉLITE PARA EL MONITOREO Y PREDICCIÓN DEL COMPORTAMIENTO DE INCENDIOS FORESTALES by Miguel Felipe Cano Bogotá, Valeria Navarro Lizcano, Ricardo Andrés Santa Quintero

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Stochastic Characterization of Faults in Electrical Transmission Networks: Case Study of the Electrical Community of Benin by Mohamed Barate, Eyouléki Tcheyi Gnadi Palanga, Ayité Sénah Akoda Ajavon, Kodjo Agbossou

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Reversible data hiding algorithm based on asymmetric histogram shifting by Yufen HE, Zhaoxia YIN, Jin TANG, Lei LIU, Shilei HUANG

“…The shifting of two asymmetric histograms in opposite directions in data embedding respectively had produced the pixel compensation and restore effect,a better reversible data hiding algorithm based on pixel prediction was proposed,two asymmetric histograms of prediction error were generated on the more right and the more left side of zero value,when they were shifed in the second data embedding stage,more pixels would be restored to the original image pixel value to reduce image distortion and improve the image quality.Compared with the traditional algorithm,it reduces the amount of pixels involved in the histogram shifting and protects the quality of secret image.…”
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Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts by Renao Jiang, Longfei Dai, Xinjian Xu, Zhen Zhang

“…Consensus clustering analysis was then used to stratify patients with NASH into distinct phenotypic subgroups based on expression levels of these genes. Results A NASH prediction model, developed using the random forest (RF) algorithm, demonstrated high diagnostic accuracy across multiple cohorts. …”
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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms by Pablo Augusto de Souza Fonseca, Aroa Suarez-Vega, Cristina Esteban-Blanco, Héctor Marina, Rocío Pelayo, Beatriz Gutiérrez-Gil, Juan-José Arranz

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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Then, the Microbial Enhanced Algorithm-Back Propagation(MEA-BP) model was used for multiple predictions, and the average values were taken, and evaluation indicators were employed to assess the seven operating conditions. …”
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Predicting Online Learners’ Performance Through Ontologies: A Systematic Literature Review by Safa Ridha Albo Abdullah, Ahmed Al-Azawei

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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Limited by the number of parameters, the traditional linear fitting method has low computational efficiency and a large error, which brings difficulties to horizontal well production prediction. In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. …”
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Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data by Wenhuan Wang, Chanchan Liao, Hongping Zhang, Yanjun Hu

“…This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. …”
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Traffic Flow Prediction Based on Fractional Seasonal Grey Model by SHEN Qinqin; ZHANG Zhijie; QI Xucun; YUE Xinyi

“…Based on the seasonal characteristic of urban road traffic flow data and the principle of new information, a new fractional seasonal GM(1, 1) prediction model is proposed. In the new model, a fractional cycle truncation accumulated generation operator(FCTAGO) was firstly proposed to weaken the stochastic disturbances and the seasonal characteristics of the original sequence, and then the particle swarm optimization(PSO) algorithm was adopted to find the optimal fractional order. …”
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AI-based prediction of academic success: Support for many, disadvantage for some? by Lisa Herrmann, Jonas Weigert

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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…This study elucidates areas where predictive ML algorithms may ideally inform GET study design to accelerate optimization and improve efficiencies upon the further training of these models.…”
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Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome by Miao-Na Bai, Miao-Na Bai, Miao-Na Bai, Ji-Xiang Wang, Ji-Xiang Wang, Xiao-Wei Li, Xiao-Wei Li, Xiao-Wei Li, Jing-Xian Wang, Jing-Xian Wang, Yu-Hang Wang, Yu-Hang Wang, Yin Liu, Yin Liu, Yin Liu, Jing Gao, Jing Gao, Jing Gao, Jing Gao

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A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals by Yifan Shen, Beng Xuan, Hongtao Hu, Yansong Wu, Ning Zhao, Zhen Yang

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Enhancing stock market predictions for classifying unlabelled celebrities' twitter data by Baljinder Singh, Mandeep Kaur, Gurbinder Singh Brar, NZ Jhanjhi

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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

“…In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. …”
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