Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning by Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

“…This study aims to analyze the influence of academic and non-academic factors on career alignment and to develop a predictive model using machine learning algorithms. The data used in this study were obtained from an alumni tracer study and student academic records at Universitas Muria Kudus (UMK), comprising a total of 311 records after data transformation. …”
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Applying Machine Learning Techniques to Predict Drug-Related Side Effect: A Policy Brief by Esmaeel Toni MSc, Haleh Ayatollahi PhD

“…Drug safety is a critical aspect of public health, yet traditional detection methods may miss rare or long-term side effects. Recently, machine learning (ML) techniques have shown promise in predicting drug-related side effects earlier in the development pipeline. …”
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MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh by Momotaz Begum, Mehedi Hasan Shuvo, Jia Uddin

“…Depending on usage patterns, these technologies can positively or negatively impact students’ education. In recent years, many researchers have introduced several models, including neural networks (NNs), machine learning (ML), and deep learning (DL), to identify the impact on student academic performance using a socimedevice. …”
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A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM by Sarni Maniar Berliana, Omas Bulan Samosir, Rafidah Abd Karim, Victoria Pena Valenzuela, Krismanti Tri Wahyuni, Andi Alfian

“…Interdisciplinary collaboration and advanced machine learning techniques can enhance predictive accuracy and model interpretability.…”
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Machine learning for improved size estimation of complex marine particles from noisy holographic images by Zonghua Liu, Zonghua Liu, Marika Takeuchi, Yéssica Contreras, Thangavel Thevar, Alex Nimmo-Smith, John Watson, Sarah L. C. Giering

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A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) by Xuanyi Cai, Yunan Zhang, Meng Su, Fan Chang, Lei Quan, Yixing Liu, Bei Wang

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Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis by Ali Nabavi, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Farimah Safari

“…These findings highlight key correlates of hearing impairment within the study population.ConclusionThis study underscores the utility of a machine learning framework in identifying associations between heavy metal biomarkers and hearing loss in a nationally representative sample. …”
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Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features by Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

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Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients by Vivian Salama, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Brandon Godinich, Kareem A. Wahid, Kareem A. Wahid, Dina M. ElHabashy, Mohamed A. Naser, Renjie He, Abdallah S. R. Mohamed, Ariana J. Sahli, Katherine A. Hutcheson, Gary Brandon Gunn, David I. Rosenthal, Clifton D. Fuller, Amy C. Moreno

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Analysis of the exercise intention-behavior gap among college students using explainable machine learning by Cui Cui, Cui Cui, Jixin Yin

“…A critical challenge in improving student fitness is addressing the intention-behavior gap–the disconnect between students' intentions to engage in physical activity and their actual behavior.MethodsThis study utilized survey data from TikTok-using college students, incorporating variables such as gender, academic grade, health belief perceptions, and planned behavior perceptions. Multiple machine learning models were developed to predict the presence of the intention-behavior gap. …”
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Experimental analysis and optimization of hydrogen pre-cooling liquefaction process with composite catalyst through a hybrid priority cluster modeling approach by Faisal Khan, Osama Khan, Praveen Pachauri, Mohd Parvez, Aiyeshah Alhodaib, Zeinebou Yahya, Haidar Howari, M. Javed Idrisi, Worku Tenna

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The Impact of Travel Behavior Factors on the Acceptance of Carsharing and Autonomous Vehicles: A Machine Learning Analysis by Jamil Hamadneh, Noura Hamdan

“…The rapid evolution of the transport industry requires a deep understanding of user preferences for emerging mobility solutions, particularly carsharing (CS) and autonomous vehicles (AVs). This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). …”
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Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation by Xi Chen, Hao Ding, Jian Mou, Yuping Zhao

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Optimized machine learning mechanism for big data healthcare system to predict disease risk factor by Venkata Nagaraju Thatha, Silpa Chalichalamala, Udayaraju Pamula, D. Pramodh Krishna, Manjunath Chinthakunta, Srihari Varma Mantena, Shariff Vahiduddin, Ramesh Vatambeti

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Machine learning prediction of interfacial bond strength of FRP bars with different surface characteristics to concrete by Lingyu Tian, Luchen Wang, Guijun Xian

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Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

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Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning by Aveen K P, Ullal Vignesh Nayak, K M Pranesh Rao, Shivaramu H T, V Londhe Neelakantha, Shashikumar C M

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The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning by Yuchen Lu, Jiakun Zhuang, Jun Chen, Chenlu Yang, Mei Kong

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Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants by Shan Peng, Yukun Zhao, Xinyi Yao, Huilin Yin, Bei Ma, Ke Liu, Gang Li, Yang Cao

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Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria by Zinabu Bekele Tadese, Teshome Demis Nimani, Kusse Urmale Mare, Fetlework Gubena, Ismail Garba Wali, Jamilu Sani

“…Hence, this study aimed to predict the fertility preferences of reproductive age women in Nigeria using state-of-the-art machine learning techniques.MethodsSecondary data analysis from the recent 2018 Nigeria Demographic and Health Survey dataset was employed using feature selection to identify predictors to build machine learning models. …”
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