Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms by M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami

“…The study utilizes the Gradient Tree Boosting (GTB), Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) classifiers within the Google Earth Engine (GEE) platform. …”
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Learning control system of lifting machine motors by V. I. Zimovets, A. S. Chirva, O. I. Marishchenko

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Flood Inundation Mapping of a River Stretch Using Machine Learning Algorithms in the Google Earth Engine Environment by Maaz Ashhar, Venkata Reddy Keesara, Venkataramana Sridhar

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Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models by Jukyeong Choi, Youngjo Yun, Heemun Chae

“…XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). …”
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Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine by Hengki Muradi, Dede Dirgahayu Domiri, I Made Parsa, I Kadek Yoga, Alhadi Bustamam, Anisa Rarasati, Sri Harini, R. Johannes Manalu, Mokhamad Subehi

“…In this study, the performance of two machine learning methods for classification was compared: classification and regression trees (CART) and RF. …”
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Tribological performance of graphene oxide reinforced PEEK nanocomposites with machine learning approach by Yagnik Patel, Unnati Joshi, Prince Jain, Anand Joshi, Sanketsinh Thakor, Swapnil Parikh

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A customized ensemble machine learning approach: predicting students’ exam performance by Rasel Ahmed, Nafiz Fahad, Md Saef Ullah Miah, Kah Ong Michael Goh, Mufti Mahmud, M. Mostafizur Rahman

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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…Ultra-High Performance Concrete (UHPC) is vital for next-generation infrastructure, necessitating complex interaction modeling beyond empirical methods. …”
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Simulation Analysis of Energy Inputs Required by Agricultural Machines to Perform Field Operations by Francesco Paciolla, Katarzyna Łyp-Wrońska, Tommaso Quartarella, Simone Pascuzzi

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Statistical machine translation implementation and performance tests betweenKyrgyz and Turkish Languages by N. Tayirova, M. Tekerek, U. Brimkulov

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Parametric Characterization of a Tractor Engine by Specific Fuel Consumption by S. N. Devyanin, A. V. Bizhaev, Y. D. Pavlov, S. M. Vetrova, A. S. Barchukova

“…(Conclusions) The suggested sequence of steps for obtain a multi-parameter characteristic can be applied to other engine performance indicators. Monitoring operational performance to analyze information on the technical condition of machine components and assemblies is necessary for diagnostics and ensuring timely maintenance and repair.…”
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SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks by Krishnagopal Halder, Anitabha Ghosh, Amit Kumar Srivastava, Subodh Chandra Pal, Uday Chatterjee, Dipak Bisai, Frank Ewert, Thomas Gaiser, Abu Reza Md. Towfiqul Islam, Edris Alam, Md Kamrul Islam

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Prediction of the sound absorption performance for micro-perforated panel based on machine learning by Binxia Yuan, Tianqi You, Huanhuan Jiang, Hong Qian, Lan Cao, Rui Zhu

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Performance evaluation of rotor shears machine with bottom sieves for comminution of waste PCB by Radon Dhelika, Daniel Meino Soedira

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Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan by Arslan Berdyyev, Yousef A. Al-Masnay, Mukhiddin Juliev, Jilili Abuduwaili

“…Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). …”
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Prediction of Machining Characteristics and Machining Performance for Grade 2 Titanium Material in a Wire Electric Discharge Machine Using Group Method of Data Handling and Artific... by Sudhir Jain Prathik, Athimoolam Sundaramahalingam, Maddur Eswara Nithyashree, Addamani Rudreshi, Gonchikar Ugrasen

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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. …”
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A Comparative Study and Machine Learning Enabled Efficient Classification for Multispectral Data in Agriculture by Priyanka Gupta, Shruti Kanga, Varun Narayan Mishra

“…The main goal of the research is to analyze crop classification using various machine learning (ML) such as Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Random Forest (RF), Decision Tree (DT) as well as Classification and Regression Trees (CART) on Google Earth Engine platform. …”
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A STUDY ON SOME DIFFERENT PARAMETERS AFFECTING THE ABRASIVE PEELING MACHINE PERFORMANCE by Tarek FOUDA, Mohamed DARWESH, Mahmoud ELKHODAREY

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Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study by Ji Yoon Kim

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