Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. …”
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Machine learning classification algorithm screening for the main controlling factors of heavy oil CO2 huff and puff by Peng-xiang Diwu, Beichen Zhao, Hangxiangpan Wang, Chao Wen, Siwei Nie, Wenjing Wei, A-qiao Li, Jingjie Xu, Fengyuan Zhang

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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. …”
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Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models by Kingsley Ifeanyi Chibueze, Nwamaka Georgenia Ezeji, Nnenna Harmony Nwobodo-Nzeribe

“…It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. …”
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Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

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Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning by Asraa Muayed Abdalah, Noor Redha Alkazaz

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Integrating Machine Learning Techniques and the Theory of Planned Behavior to Assess the Drivers of and Barriers to the Use of Generative Artificial Intelligence: Evidence in Spain by Antonio Pérez-Portabella, Jorge de Andrés-Sánchez, Mario Arias-Oliva, Mar Souto-Romero

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Machine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications by Fatih Topaloglu

“…The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. Its second motivation is to determine the optimum set/number of meteorological data required for nuclear energy projects using the best-performing ML algorithm. …”
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Development of a prediction model for acute respiratory distress syndrome in ICU patients with acute pancreatitis based on machine learning algorithms by REN Xia*，LIU Luojie，ZHA Junjie，YE Ye，XU Xiaodan，YE Hongwei，ZHANG Yan

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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data by Salha Al-Ahmari, Farrukh Nadeem

“…Seven machine learning algorithms were created and tested: Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Boosting (SGB), and K-Nearest Neighbors (KNN). …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Saad Ahmed Dheyab, Shaymaa Mohammed Abdulameer, Salama Mostafa

“…Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. …”
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Hybrid Weighted Random Forests Method for Prediction & Classification of Online Buying Customers by Umesh Kumar Lilhore, Sarita Simaiya, Devendra Prasad, Deepak Kumar Verma

“…This research article mainly proposed an extension of the Random Forest classifier named “Weighted Random Forests” (wRF), which incorporates tree-level weights to provide much more accurate trees throughout the calculation as well as an assessment of vector relevance. …”
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Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering by Bo Xu, Chunjiang Zhao, Guijun Yang, Yuan Zhang, Changbin Liu, Haikuan Feng, Xiaodong Yang, Hao Yang

“…Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. …”
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Path planning algorithm based on the improved Informed-RRT* using the sea-horse optimizer by YAN Guiseng, YANG Jie

“…ObjectiveIn order to solve the problems of random sampling, inefficient search, and difficulty in providing optimal paths in complex environments faced by traditional Informed-RRT* algorithms, an improved Informed-RRT* path planning algorithm based on the sea-horse optimizer (SHO) was proposed.MethodsThis algorithm combined the strengths of Informed-RRT* and SHO. …”
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NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

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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

“…Different MLAs are modelled to explore the PEMFC performance and results proved that gradient boosting regression provides better predictions compared to other algorithms such as decision tree regressor, support vector machine regressor, and random forest regression.…”
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Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

“…Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-parametric methods such as random effects-expectation maximization (RE-EM) and unbiased RE-EM regression tree algorithms. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning  algorithms in the decision-making process. …”
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