A NOVEL PREDICTIVE MODELING FOR STUDENT ATTRITION UTILIZING MACHINE LEARNING AND SUSTAINABLE BIG DATA ANALYTICS

A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics

Student attrition poses significant societal and economic challenges, leading to curlimal hedgehog unemployment, lower earnings, and other adverse outcomes for individuals and communities.To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively.This study leverages

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Adult attention-deficit hyperactivity disorder, risky behaviors, and motorcycle injuries: a case-control study

Homayoun Sadeghi-Bazargani,1,2 Leili Abedi,3 Minoo Mahini,4 Shahrokh Amiri,5 Davoud Khorasani-Zavareh6 1Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; 2World Health Organization Collaborating Center on Safe Community Promotion, Stockholm, Sweden; 3Department of Statistics and Epidemiology, Faculty of Healt

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