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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Research on building energy consumption prediction algorithm based on customized deep learning model by Zheng Liang, Junjie Chen

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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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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

Subjects: “…soil organic carbon prediction…”
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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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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

“…Conclusion: XGBoost and LightGBM algorithms outperform logistic regression in predicting GDM among Chinese pregnant women. …”
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An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction by Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak

“…Furthermore, we applied this algorithm for data classification to osteoporosis risk prediction, utilizing an extreme learning machine. …”
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Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

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Research on Spatial Prediction of Precipitation in Guangdong Province Based on Gaussian Process Regression Algorithm by ZENG Xiongzhi, YU Chao

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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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Smart prediction-planning algorithm for connected and autonomous vehicle based on social value orientation by Donglei Rong, Yuefeng Wu, Wenjun Du, Chengcheng Yang, Sheng Jin, Min Xu, Fujian Wang

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Prediction of rolling bearing performance degradation based on whale optimization algorithm and backpropagation model by Jingyue Wang, Yuntong Han, Haotian Wang, Jianming Ding, Cai Yi

“…The dissertation proposes a prediction model that enhances the BP (Backpropagation) neural network using the WOA (Whale Optimization Algorithm) to address the issue of local convergence during prediction. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…Therefore, this study employed machine learning algorithms to predict abortion in Ethiopia and identify its predictors using nationally representative data. …”
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Constructing and Predicting Solutions for Different Families of Partial Differential Equations: A Reliable Algorithm by Mubashir Qayyum, Amna Khan

“…In this manuscript, a new approach based on the generalized Taylor series and residual function is proposed to predict and analyze Buckmaster and KdV type models. …”
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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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Adaptive random early detection algorithm based on network traffic level grade prediction by Debin WEI, Chengsheng PAN, Li YANG, Zuoren YAN

“…In view of the problem that the calculation of average queue length and maximum packet drop probability in random early detection algorithm and its variants reflect the changes of network traffic slowly, an adaptive random early detection algorithm based on network traffic level grade prediction was proposed.Based on the statistical characteristics of self-similar network traffic, the transition probability table of network traffic level grade was established, and a grade prediction method of self-similar network traffic level with low complexity and high accuracy was proposed.Furthermore, the prediction results were applied to calculate the average queue length in equal interval and adjust the maximum packet drop probability.Under the condition of fixed and variable bottleneck link capacity, it is found that regardless of the degree of self-similarity of network traffic, the proposed algorithm can improve the throughput and packet loss rate, especially when the Hurst parameter is large and the traffic is light.…”
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Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem, Abdulaziz I. Almulhim

“…Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). …”
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