Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…That is becoming increasingly popular is the use of Machine Learning  algorithms in the decision-making process. …”
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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

“…The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study’s objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. …”
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Enhancing augmented reality with machine learning for hands-on origami training by Mikołaj Łysakowski, Jakub Gapsa, Chenxu Lyu, Thomas Bohné, Sławomir Konrad Tadeja, Piotr Skrzypczyński, Piotr Skrzypczyński

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Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach by Qoriani Widayati, Kusworo Adi, R Rizal Isnanto, Eka Puji Agustini, Dewa Rizki Rahmat Julianto, Fawwaz Bimo Prakasa

“…Student loyalty is a crucial factor supporting the sustainability of higher education institutions. The aim of this study is to predict student loyalty using a machine learning approach, specifically the random forest algorithm. …”
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Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

“…Abstract Machine learning has become an essential component across various domains, including the education sector. …”
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A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations by Mario Suaza-Medina, Rita Peñabaena-Niebles, Maria Jubiz-Diaz

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Methods and reliability study of moral education assessment in universities: A machine learning-based approach by Ting Jin

“…The study demonstrates how machine learning efficiently assesses student moral education performance by leveraging PCA to identify patterns and using ML models to make accurate predictions. …”
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Suicide risk prediction for Korean adolescents based on machine learning by Haitao Wang, Han Yuan, Yunong Zhang, Qixuan Wang, Zeng Gao, Mujuan Zhao

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Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis by Yuhan Zhang, Jue Guo, Na Yang, Xiangyun Li, Yuxiang Liu, Meiqin Yan, Peng Shen

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Research Training and Machine Learning: from Matching to Convergence by Осипенко Л.Е., Козицына Ю.В., Коротков А.В.

“…Existing applied solutions with the use of machine learning in pedagogy are analyzed, ways of its extrapolation to the convergence model of research training and machine learning are shown. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

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Exploring Metaverse Technologies in Entrepreneurship Through Machine Learning-Based Topic Modeling by Krishnashree Achuthan, Sasangan Ramanathan, Raghu Raman

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Predicting Response to Transarterial Chemoembolization in Hepatocellular Carcinoma Using Machine Learning Models by Niharika Dutta, Pankaj Gupta

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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. …”
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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements by Abdollah Tabaroei, Muhand Jawad Jasim, Ali Mohammed Al-Araji, Amir Hossein Vakili

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Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal by Arnab Singh, Dibas Shrestha, Kaman Ghimire, Sangya Mishra, Darwin Rana, Sunil Acharya

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Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine by Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard, Nicolas Labroche, Cyrille Delpierre, Chantal Soulé-Dupuy, Louis Casteilla, Valérie Planat-Benard, Paul Monsarrat

“…Although the benefits of machine learning are undeniable in healthcare, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. …”
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

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Assessing an Innovative Virtual Museum Application using Technology Acceptance Model by Shinta Puspasari, Ermatita, Zulkardi

“…This study discusses the assessment of a virtual museum application development based on machine learning models for Palembang culture education through the Sultan Mahmud Badaruddin II museum cultural heritage by using the Technology Acceptance Model approach. …”
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