Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

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Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models by Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò

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Machine learning approach for Studying the multifunctionality of soil against global climate changes by Xiangng Hu, Yingying Xie, Qixing Zhou, Li Mu

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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…To help specialists dealing with disabilities and motor disabilities, especially psychologists and psychiatrists, make judgments by examining patient behavior data and medical records, artificial intelligence (AI) and machine learning (ML) algorithms have been employed. …”
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Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes by Mohammad Rashidmayvan, Amin Mansoori, Elahe Derakhshan-Nezhad, Davoud Tanbakuchi, Fatemeh Sangin, Maryam Mohammadi-Bajgiran, Malihehsadat Abedsaeidi, Sara Ghazizadeh, MohammadReza Mohammad Taghizadeh Sarabi, Ali Rezaee, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

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Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms by Paramasivam Alagumariappan, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj, K. Kamalanand, C. Emmanuel, Sarah Allabun, Manal Othman, Masresha Getahun, Ben Othman Soufiene

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Artificial Intelligence in Hand Surgery: The Future is Upon Us by Bryan S. Crook, MD, Eoghan T. Hurley, MBBS, PhD, Marc J. Richard, MD, Suhail K. Mithani, MD, Tyler S. Pidgeon, MD

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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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Machine Learning in Public Governance: A Systematic Review of Applications, Trends and Challenges by Y. Nuruly, G. N. Sansyzbayeva, L. Z. Ashirbekova, S. K. Tazhiyeva

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Explainable artificial intelligence (XAI) for interpreting predictive models and key variables in flood susceptibility by Bahram Choubin, Abolfazl Jaafari, Jalal Henareh, Omid Karimi, Farzaneh Sajedi Hosseini

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Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling by Saad Sh. Sammen, Reza Mohammadpour, Karam AlSafadi, Ali Mokhtar, Shamsuddin Shahid

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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

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A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids by Qinran Zhang, Huizhong Chi, Yanhua Qi, Rongrong Zhao, Fuzhong Xue, Gang Li, Hao Xue

“…This study developed the glioma-related cell signature (GRCS), a prediction model that integrates machine learning with biological insights. …”
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Predicting stroke with machine learning techniques in a sub-Saharan African population by Benjamin Segun Aribisala, Deirdre Edward, Godwin Ogbole, Onoja M. Akpa, Segun Ayilara, Fred Sarfo, Olusola Olabanjo, Adekunle Fakunle, Babafemi Oluropo Macaulay, Joseph Yaria, Joshua Akinyemi, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Osaigbovo, Akinkunmi Paul Okekunle, Arti Singh, Philip Ibinaye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu Ogah, Ruth Y. Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi

“…Machine Learning, especially Artificial Neural Network, is recommended to enhance Afrocentric stroke prediction models for stroke risk factor quantification and control in Africa.…”
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Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

“…The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

“…The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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Key risk factors of generalized anxiety disorder in adolescents: machine learning study by Yonghwan Moon, Hyekyung Woo, Hyekyung Woo

“…This study utilized data from the Korea Youth Risk Behavior Web-based Survey (KYRBS) from 2020 to 2023 to analyze factors influencing GAD in adolescents. Using machine learning techniques such as Lasso Regression, SelectKBest, and XGBoost, we identified key variables, including health behaviors such as sleep, smoking, and fast-food intake, as significant factors associated with GAD. …”
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Machine learning in predicting infertility treatment success: A systematic literature review of techniques by Shirin Dehghan, Hamid Moghaddasi, Reza Rabiei, Hamid Choobineh, Keivan Maghooli, Mojtaba Vahidi-Asl

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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…Finally, we compare the performance of our proposed DNN model with other machine learning algorithms, such as Random Forest and Support Vector Machines, demonstrating superior predictive accuracy. …”
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Opinion mining using ensemble model for restaurant feedback analysis by Rohan S. Kamath, M. R. Kaushik, M. Ramakrishna

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