POINT ESTIMATION OF THE PARAMETER OF GEOMETRIC DISTRIBUTION UNDER A PARTICULAR RANDOM CENSORING TEST by HE ChaoBing

“…The asymptotic variance of EM estimation is obtainde by Louis algorithm. Random simulation results show that the MLE and EM estimation are both fairly accurate,and make little difference.…”
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PREDICTION OF TBM OIL TEMPERATURE BY IMPROVED CAMEL ALGORITHM ASSISTED RANDOM FOREST MODEL (MT) by REN JianJi, ZHAO RunQiu, WANG ZhenXi, LIU YuMing, YUAN YongLiang

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A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy by Kangge Zou, Yanmin Liu, Shihua Wang, Nana Li, Yaowei Wu

“…To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and convergence of the population and improve the probability of particles flying to the real Pareto front. …”
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Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity by Rosalvo Ferreira de Oliveira Neto, Sérgio Ruschi Bergamachi Silva, Cintia Emi Yanaguibashi Leal, Edilson Beserra de Alencar Filho

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Prediction of Mg Alloy Corrosion Based on Machine Learning Models by Zhenxin Lu, Shujing Si, Keying He, Yang Ren, Shuo Li, Shuman Zhang, Yi Fu, Qi Jia, Heng Bo Jiang, Haiying Song, Mailing Hao

“…We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector machine regression (SVR), and extreme gradient boosting (XGBoost). …”
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An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem by Pisut Pongchairerks

“…To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. …”
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A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA by Alberto Pajares, Xavier Blasco, Juan M. Herrero, Gilberto Reynoso-Meza

“…In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.…”
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Research on TCN Model Based on SSARF Feature Selection in the Field of Human Behavior Recognition by Wei Zhang, Guibo Yu, Shijie Deng

“…To overcome this problem, this paper investigates a temporal convolutional neural network (TCN) model based on improved sparrow search algorithm random forest (SSARF) feature selection to accurately identify human behavioral traits based on wearable devices. …”
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Application of Feature Selection Based on Elastic Network and Random Forest in the Evaluation of Sports Effects by Lina Ren, Shen Cao

“…Around the difficult problems existing in the study of sports effect, given the limitations of existing data sets and traditional research methods, this paper starts from the data mining algorithm, builds the sports effect evaluation database, based on feature selection idea, using elastic network algorithm, random forest algorithm, and the influence of sports on the effect of physical indicators. …”
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Development of a High‐Latitude Convection Model by Application of Machine Learning to SuperDARN Observations by W. A. Bristow, C. A. Topliff, M. B. Cohen

“…Abstract A new model of northern hemisphere high‐latitude convection derived using machine learning (ML) is presented. The ML algorithm random forests regression was applied to a database of velocities derived from the Super Dual Auroral Radar Network (SuperDARN) observations processed with the potential mapping technique, Map‐Potential (Ruohoniemi & Baker, 1998, https://doi.org/10.1029/98ja01288). …”
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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches by Pablo Antúnez, Christian Wehenkel, Erickson Basave-Villalobos, Celi Gloria Calixto-Valencia, César Valenzuela-Encinas, Faustino Ruiz-Aquino, David Sarmiento-Bustos

“…The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. …”
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Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

“…The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). …”
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Ensemble modelling reveals spiny monkey orange (Strychnos spinosa Lam.) as a vulnerable wild edible fruit tree in West Africa by Hospice Gérard Gracias Avakoudjo, Mahunan Eric José Vodounnon, Rodrigue Idohou, Aly Coulibaly, Achille Ephrem Assogbadjo

“…Bioclimatic and soil variables were used at a resolution of 30 arcseconds with 588 occurrence records analysed using five algorithms (Random Forest (RF), Maximum Entropy (MaxEnt), Support Vector Machine (SVM), Boosted Regression Trees, and Generalized Linear Model (GLM)) and four global climate models (CanESM5, CNRM-CM6-1, HadGEM3-GC31-LL, and MIROC6). …”
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Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications by Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Mohsin Kazi, Majdi Khalid, Arnisha Akhter, Mohammad Ali Moni

“…Results The performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. …”
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Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning by Kang Huang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, Yuting Zhu

“…Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. …”
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Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure by Chenyang Jiang, Weidong Jiang

“…Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. …”
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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

“…We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. …”
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Combining machine learning and single-cell sequencing to identify key immune genes in sepsis by Hao Wang, Linghan Len, Li Hu, Yingchun Hu

“…Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. …”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Four machine learning algorithms—Random Forests, Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—were applied alongside a traditional logistic regression model. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. …”
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