Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…In order to evaluate effects of artificial rainfall enhancement objectively and quantitatively, combing linear fitting, polynomial regression, spline regression and 3 other machine learning methods including decision tree, support vector machine and neural network, the relationship model between the rainfall in the target area and the contrast area is established based on rainfall data and operation information of recent 10 years in Fujian. …”
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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…Feature selection was performed using the Boruta algorithm. We employed one traditional predictive model, logistic regression, alongside six machine learning models: support vector machine (SVM), K-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), AdaBoost, and XGBoost. …”
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Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algor... by Qi Li, Hengchao Liu, Yubo Ma, Zhenqi Tang, Chen Chen, Dong Zhang, Zhimin Geng

“…Using the aforementioned five variables, we developed four machine learning prediction models, including logistic regression, support vector machine, LightGBM, and random forest. …”
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Machine learning framework to estimate ridership loss in public transport during external crises: case study of bus network in Stockholm by Mahsa Movaghar, Erik Jenelius, David Hunter

“…To do this, seven alternative machine learning algorithms were developed to predict ridership: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Regression, and k-Nearest Neighbors. …”
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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals by Liuyi Ling, Zhu Lin, Bin Feng, Liyu Wei, Li Jin, Yiwen Wang

“…The performance of the proposed model was compared with those of traditional backpropagation neural network, support vector machine regression, and random forest models. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

“…Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. …”
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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

“…Eight machine learning algorithms were applied to the training set to construct the model, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), extreme gradient boosting (XGBoost), classification and regression tree (CART), k-nearest neighbors (KNN), and neural network (NNET). …”
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Detection of Water Surface Using Canny and Otsu Threshold Methods with Machine Learning Algorithms on Google Earth Engine: A Case Study of Lake Van by Pinar Karakus

“…When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. …”
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Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning by Po-Ting Pan, Yamine Bouzembrak, Miguel Quemada, Bedir Tekinerdogan

“…Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. …”
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Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China by Hungil Jin, Yinghui Zhao, Unil Pak, Zhen Zhen, Kumryong So

“…Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Convolutional Neural Network (CNN)). …”
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Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Develo... by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

“…The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. …”
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Short-term salinity prediction for coastal areas of the Vietnamese Mekong Delta using various machine learning algorithms: a case study in Soc Trang Province by Le Thi Thanh Dang, Hiroshi Ishidaira, Ky Phung Nguyen, Kazuyoshi Souma, Jun Magome

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Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning alg... by David M Vu, Amy R Krystosik, Bryson A Ndenga, Francis M Mutuku, Kelsey Ripp, Elizabeth Liu, Carren M Bosire, Claire Heath, Philip Chebii, Priscilla Watiri Maina, Zainab Jembe, Said Lipi Malumbo, Jael Sagina Amugongo, Charles Ronga, Victoria Okuta, Noah Mutai, Nzaro G Makenzi, Kennedy A Litunda, Dunstan Mukoko, Charles H King, A Desiree LaBeaud

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A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data by Ya-mei Ye, Yong Lin, Fang Sun, Wen-yan Yang, Lina Zhou, Chun Lin, Chen Pan

“…Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. …”
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Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico by K. D. Rodríguez González, L. E. Arista Cázares, F. D. Yépez Rincón

“…This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. …”
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Liquid-liquid equilibrium data prediction using large margin nearest neighbor by mohsen pirdashti, kamyar movagharnejad, silvia Curteanu, Florin Leon, Farshad Rahimpour

“…To fill the theoretical gaps, the typical of support vector machines was applied to the k-nearest neighbor method in order to develop a regression model to predict the LLE equilibrium of guanidine hydrochloride in the above mentioned system. …”
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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

“…To evaluate the effectiveness of the proposed method, we compared it with other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). …”
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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditi... by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

“…Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. The drying times for jujube slices dried through the open sun closed shade, and microwave methods were 1680, 1140, and 24 min, respectively. …”
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Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests by Thuy Phuong Nguyen, Phuc Khoa Nguyen, Huu Ngu Nguyen, Thanh Duc Tran, Gia Tung Pham, Thai Hung Le, Dinh Huy Le, Trung Hai Nguyen, Van Binh Nguyen

“…A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. …”
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