Yield Response of Different Rice Ecotypes to Meteorological, Agro-Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting a... by Md. Sabbir Ahmed, Md. Tasin Tazwar, Haseen Khan, Swadhin Roy, Junaed Iqbal, Md. Golam Rabiul Alam, Md. Rafiul Hassan, Mohammad Mehedi Hassan

“…Machine learning algorithms named Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) have been used for predicting the yield response. …”
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Local or Neighborhood? Examining the Relationship between Traffic Accidents and Land Use Using a Gradient Boosting Machine Learning Method: The Case of Suzhou Industrial Park, Chin... by Yueming Yang, Hyungchul Chung, Joon Sik Kim

“…Using a case study of Suzhou Industrial Park (SIP) in Suzhou, China, this paper examines the relationship between different land use types and traffic accidents using a gradient boosting model (GBM) machine learning method. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

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Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java by Khusnia Nurul Khikmah, Bagus Sartono, Budi Susetyo, Gerry Alfa Dito

“…This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model by Reza Soleimani, Amir Hossein Saeedi Dehaghani, Ziba Behtouei, Hamidreza Farahani, Seyyed Mohsen Hashemi

“…Abstract This study presents a robust method for predicting CO2 solubility in Deep Eutectic Solvents (DESs) using the stochastic gradient boosting (SGB) algorithm. DESs, promising green solvents for CO2 capture, require precise solubility data for practical applications in industrial and environmental settings. …”
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COVID-19 pandemic prediction model based on machine learning in selected regions of the Russian Federation by D. V. Gavrilov, R. V. Abramov, А. V. Kirilkina, А. А. Ivshin, R. E. Novitskiy

“…The model was trained by the CatBoost gradient boosting method and retrained daily with updated data.Results. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

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Key factors in predictive analysis of cardiovascular risks in public health by Ghazi I. Al Jowf, Manjur Kolhar

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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

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Early identification of dropouts during the special forces selection program by Ruud J. R. den Hartigh, Rik Huijzer, Frank J. Blaauw, Age de Wit, Peter de Jonge

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Experimental investigation and laser control in Ti10Mo6Cu powder bed fusion: optimizing process parameters with machine learning by Ouf A. Shams, Hanan B. Matar Al-Baity, Luttfi A. Al-Haddad

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Integrating Microgrids into Engineering Education: Modeling and Analysis for Voltage Stability in Modern Power Systems by Farheen Bano, Ali Rizwan, Suhail H. Serbaya, Faraz Hasan, Christos-Spyridon Karavas, Georgios Fotis

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Detecting Important Features and Predicting Yield from Defects Detected by SEM in Semiconductor Production by Umberto Amato, Anestis Antoniadis, Italia De Feis, Anastasiia Doinychko, Irène Gijbels, Antonino La Magna, Daniele Pagano, Francesco Piccinini, Easter Selvan Suviseshamuthu, Carlo Severgnini, Andres Torres, Patrizia Vasquez

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An Intrusion Detection Approach based on the Combination of Oversampling and Undersampling Algorithms by Ahmet Okan Arık, Gülsüm Çiğdem Çavdaroğlu

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Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

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Using a Light Gradient-Boosting Machine–Shapley Additive Explanations Model to Evaluate the Correlation Between Urban Blue–Green Space Landscape Spatial Patterns and Carbon Sequest... by Yuting Wu, Mengya Luo, Shaogang Ding, Qiyao Han

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Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

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