Early Prediction Detection of Retail and Corporate Credit Risks Using Machine Learning Algorithms by Mohamed A. Hamada, Karim Farag, Adejor E. Abiche

“…Consequently, the paper aims to utilize machine learning algorithms, regression analysis, and classification models to identify the most effective predictive model that can improve banks' credit risk prediction capabilities. …”
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Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases by Naimeh Niazi, Hamideh Razavi

“…Comparison of the results from the algorithms shows that the boosting ensemble algorithm, with an F1 score of 89 and precison of 85%, predicts the selected financing methods on the test dataset better than other algorithms. …”
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Prediction of Optimum Operating Parameters to Enhance the Performance of PEMFC Using Machine Learning Algorithms by Arunadevi M, Karthikeyan B, Anirudh Shrihari, Saravanan S, Sundararaju K, R Palanisamy, Mohamed Awad, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Hany S. Hussein, Mahmoud M. Hussein, Ahmed I. Omar

“…With a high degree of accuracy, machine learning algorithms (MLAs) can be applied to solve nonlinear problems in FCs, including performance prediction, service life prediction, and fault diagnostics. …”
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Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective by Enoch Sakyi-Yeboah, Edmund Fosu Agyemang, Vincent Agbenyeavu, Akua Osei-Nkwantabisa, Priscilla Kissi-Appiah, Lateef Moshood, Lawrence Agbota, Ezekiel N. N. Nortey

“…Four ensemble tree-based algorithms were used in this study: adaptive boosting, extreme gradient boosting, random forest, and extremely randomized trees, investigating their ability to predict heart disease. …”
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Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material by Tasbiha Ibad, Syed Muhammad Ibad, Haylay Tsegab, Rabeea Jaffari

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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. …”
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Construction and Demolition Waste Generation Prediction by Using Artificial Neural Networks and Metaheuristic Algorithms by Ruba Awad, Cenk Budayan, Asli Pelin Gurgun

“…To address this gap, this study aims to predict C&DW quantities in construction projects more accurately by integrating the gray wolf optimization algorithm (GWO) and the Archimedes optimization algorithm (AOA) into an artificial neural network (ANN). …”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. …”
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Predicting the availability of power line communication nodes using semi-supervised learning algorithms by Kareem Moussa, Khaled Mostafa Elsayed, M. Saeed Darweesh, Abdelmoniem Elbaz, Ahmed Soltan

“…Machine Learning has solved this by predicting a node having optimum readings. The more the machine learning models learn, the more accurate they become, as the model becomes always updated with the node’s continuous availability status, so self-training algorithms have been used. …”
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Predicting classification errors using NLP-based machine learning algorithms and expert opinions by Peiheng Gao, Chen Yang, Ning Sun, Ričardas Zitikis

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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms by Anwer Mustafa Hilal, Maha M. Althobaiti, Taiseer Abdalla Elfadil Eisa, Rana Alabdan, Manar Ahmed Hamza, Abdelwahed Motwakel, Mesfer Al Duhayyim, Noha Negm

“…The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. …”
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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…In this work, we explore the application of machine learning algorithms to suggest suitable parameter values. We propose a methodology to use k-nearest neighbours and artificial neural network algorithms to predict suitable parameter values based on instance features. …”
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Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms by Li Yang, Shixin He, Li Tang, Xiao Qin, Yan Zheng

“…Recent studies have extensively highlighted the strong associations between psoriasis and various inflammatory markers, which are considered novel predictive tools for evaluating systemic inflammation.MethodsCross-sectional data from the NHANES were analyzed in this study. …”
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Optimizing Crop Yield Prediction: An In-Depth Analysis of Outlier Detection Algorithms on Davangere Region by C. S. Anu, C. R. Nirmala, A. Bhowmik, A. Johnson Santhosh

“…Crop yield prediction is a critical aspect of agricultural planning and resource allocation, with outlier detection algorithms playing a vital role in refining the accuracy of predictive models. …”
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Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species by Irene T. Liao, Karen E. Sears, Lena C. Hileman, Lachezar A. Nikolov

“…While the diploid + higher ploidy set had a lower proportion of orthogroups with identical compositions, the average degree of similarity between the orthogroups was not different from the diploid set. Discussion Three algorithms—OrthoFinder, SonicParanoid, and Broccoli—are helpful for initial orthology predictions. …”
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Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms by Benjamin Mukanya Ntumba, Jean Paul Ngbolua Koto-Te-Nyiwa, Blaise Bikandu Kapesa, Nathanael Kasoro Mulenda

“…Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms separately. These results validate the effectiveness of combining models for protein secondary structure prediction (PSSP). …”
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Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms by Yonghan Luo, Wenrui Ding, Xiaotao Yang, Houxi Bai, Feng Jiao, Yan Guo, Ting Zhang, Xiu Zou, Yanchun Wang

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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. …”
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Risk Factors and Prediction Model for Postoperative Pneumonia Following Hip Arthroplasty in Older Adults by Xiang B, Zhang J, Deng C, Yang H, Qian L, Zhang W

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DEVELOPING PREDICTIVE MODELS OF INTERNET SERVICE STRATEGIES by Maxim Yu. Khramov

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