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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Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network by Lilian Liu, Guobin Wang, Yubin Lan, Xinyu Xue, Suming Ding, Huizheng Wang, Cancan Song

“…The particle deposition distribution data under different operating parameters were obtained by EDEM simulation and data superposition methods, and a generalized regression neural network (GRNN) based on a genetic algorithm (GA) was used to establish the prediction model of particle deposition, which was validated by bench test. …”
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Aging Prediction of IGBT Based on Improved Support Vector Regression by Zhengxiong CHEN, Mahemuti PAZILAI, Wei SHEN

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Improving lameness detection in cows: A machine learning algorithm application by Elma Dervić, Caspar Matzhold, Christa Egger-Danner, Franz Steininger, Peter Klimek

“…A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. …”
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AEA-YOLO: Adaptive Enhancement Algorithm for Challenging Environment Object Detection by Abdulrahman Kariri, Khaled Elleithy

“…A lightweight Parameter Prediction Network (PPN) containing only six thousand parameters predicts scene-adaptive coefficients for a differentiable Image Enhancement Module (IEM), and the enhanced image is then processed by a standard YOLO detector, called the Detection Network (DN). …”
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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. …”

Predicting College Student Engagement in Physical Education Classes Using Machine Learning and Structural Equation Modeling by Liguo Zhang, Jiarui Gao, Liangyu Zhao, Zetan Liu, Anlin Guan

“…Nine machine learning algorithms were employed to develop interpretable predictive models, rank the importance of digital technology tools, and identify the optimal predictive model for student engagement. …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

“…The ability to predict dropout rates accurately enables timely interventions that can support students’ academic success and psychological resilience. …”
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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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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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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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Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci by Jie Lu, Xinhao Lu, Yixiao Wang, Hengdong Zhang, Lei Han, Baoli Zhu, Boshen Wang

“…LR and multiple ML algorithms were employed to establish the NIHL prediction model with accuracy, recall, precision, F-score, R2 and AUC as performance indicators. …”
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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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Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron by Hossein Moayedi, Azfarizal Mukhtar, Serhan Alshammari, Mohamed Boujelbene, Isam Elbadawi, Quynh T Thi, Mojtaba Mirzaei

“…To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). …”
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Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions by Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng

“…Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) algorithms were employed to predict monthly runoff generation in sub-basins delineated by the Soil and Water Assessment Tool (SWAT), which were subsequently integrated using a Recurrent Neural Network (RNN) for monthly runoff concentration prediction. …”
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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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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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