Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

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Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. …”
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Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

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Predicting Remaining Useful Life of Lithium-Ion Batteries for Electric Vehicles Using Machine Learning Regression Models by Sravanthi C L, Dr.J N Chandra sekhar

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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women’s employment in times of crisis. …”
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Evaluating Machine Learning Models for Predicting Late Leprosy Diagnosis by Physical Disability Grade in Brazil (2018–2022) by Lucia Rolim Santana de Freitas, José Antônio Oliveira de Freitas, Gerson Oliveira Penna, Elisabeth Carmen Duarte

“…This study evaluates machine learning models to predict factors associated with late leprosy diagnosis—defined as grade 2 physical disability (G2D)—in Brazil from 2018 to 2022. …”
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An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…Therefore, this study presents the Effectiveness of Machine Learning Models for estimating the Financial Cost of Assistive Services to Disability Care (EMLM-EFCASDC) technique in the KSA. …”
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Predicting mortality risk in Alzheimer’s disease using machine learning based on lifestyle and physical activity by Ruihong Tang, Liang Tan, Xia Chen, Zhengwu Shan, Zhao Zhang, Panpan Cheng

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Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models by Jiarun Liu, Zihang Yang, Lin Li, Xiaoxue Chu, Shiguang Wei, Juyu Lian

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Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models by Azita Molaeinasab, Hossein Bashari, Mostafa Tarkesh Esfahani, Saeid Pourmanafi, Norair Toomanian, Bahareh Aghasi, Ahmad Jalalian

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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

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Machine learning for post-diploma educational and career guidance: a scoping review in AI-driven decision support systems by Flavio Manganello, Elisa Rasca, Alberto Villa, Andrea Maddalena, Giannangelo Boccuzzi

“…The increasing complexity of career decision-making, shaped by rapid technological advancements and evolving job markets, highlights the need for more responsive and data-informed post-diploma guidance. Machine learning (ML), a core component of artificial intelligence, is gaining attention for its potential to support personalized educational and career decisions by analyzing academic records, individual preferences, and labor market data. …”
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Using Machine Learning for Arabic Sentiment Analysis in Higher Education: Investigating the Impact of Utilizing the ChatGPT and Bard Google by Salah AL-Hagree, Ghaleb Al-Gaphari, Fuaad Hasan Abdulrazzak, Maher Al-Sanabani, Ahmed Al-Shalabi

“…Mobile applications of universities have become an integral part of students’ lives, thus making it imperative to analyze user comments on these apps for SA purposes, where student input is crucial for assessing the effectiveness of educational institutions.  This paper presents a machine learning (ML) based approach to sentiment analysis on students’ evaluation of higher education institutions. …”
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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach by Iven Aabaah, Japheth Kodua Wiredu, Bakaweri Emmanuel Batowise, Nelson Abuba Seidu

“…This research assessed NMI educational programs in Ghana using machine learning techniques to analyze key factors influencing student performance, engagement, and satisfaction. …”
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Navigating the future of higher education in Saudi Arabia: implementing AI, machine learning, and big data for sustainable university development by Muhammad Adnan Khan, Abdur Rehman, Asghar Ali Shah, Sagheer Abbas, Meshal Alharbi, Munir Ahmad, Taher M. Ghazal

“…By balancing cutting-edge tech with local needs, GCC universities can provide innovative education while upholding traditions and values. This study explores how AI, Machine Learning, and Big Data can enhance sustainability and effectiveness in Saudi higher education, aligning with relevant UN Sustainable Development Goals. …”
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Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning by Aminu Suleiman Bashir, Ahmed Ibrahim Mahmud, Muhammad Yahya

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An exploratory study of high-educated poverty through machine learning approach: a case study of East Java, Indonesia by Dias Satria, Risti Permani, Kodrad Winarno, David Kaluge, Citra Rahayu Indraswari, Radityo Putro Handrito

“…Research methodology – using data from the Indonesian National Survey, this study employs Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN) algorithms as classification methods in machine learning.  Findings – the analysis results show that all agorithms has high accuracy with XGboost as the best performing model. …”
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Analyzing Fairness of Computer Vision and Natural Language Processing Models by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

“…Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. …”
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Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

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Drivers of academic achievement in high school: Assessing the impact of COVID-19 using machine learning techniques by Ana Beatriz-Afonso, Frederico Cruz-Jesus, Catarina Nunes, Mauro Castelli, Tiago Oliveira, Luísa Canto e Castro

“…This study contributes to AA literature by utilizing extensive data and machine learning models to reveal enduring and emerging factors affecting educational outcomes during challenging times.…”
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