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

“…Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. …”
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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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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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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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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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Variance Reduction Optimization Algorithm Based on Random Sampling by GUO Zhenhua, YAN Ruidong, QIU Zhiyong, ZHAO Yaqian, LI Rengang

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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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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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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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Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm by CAO Yuan, ZHAO Yuanliang, YUAN Xuehua, YUAN Long, RONG Junqing, ZHAO Pan, BIE Kang

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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Challenges persist in implementing these techniques in pens with a large number of animals, especially in extracting physical body characteristics from images in a production environment. In this context, the main objective of this research is to investigate a novel framework comprising effective algorithms for feature extraction, attribute selection, hyperparameter optimization, and prediction modelling, using point clouds collected from production animals (growing and finishing pigs). …”
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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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Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm for Selecting Features in Cervical Cancer Data by Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh

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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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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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Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks by Du Xiaoyu, Sun Lijuan, Liu Linfeng

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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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Algorithm for Calculating Noise Immunity of Cognitive Dynamic Systems in the State Space by A. A. Solodov, T. G. Trembach, K. E. Zhovnovatiy

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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

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