This study proposes a multiclass predictive framework for academic stress and mental health risk classification among students using a Kaggle dataset containing 25,000 records, and indicates that ensemble and deep learning approaches can effectively support multiclass mental health risk classification.
Shoaib Ahmad, A. Imran, Tahoona Kshif et al.· ICCK Journal of Software Eng...· 0 citations
This study presents a decentralized and privacy-preserving brain tumor segmentation framework that integrates Federated Learning (FL), a blockchain-inspired audit and coordination mechanism, and Explainable Artificial Intelligence (XAI) within a collaborative medical imaging environment.
Aiza Mukhtar, Aamir Ali, Wajiha Farooq et al.· ICCK Journal of Image Analys...· 0 citations
Findings indicate that explainable machine learning models, particularly KNN and XGBoost, provide accurate and interpretable decision support for early PCOS screening, enabling timely intervention and offering a promising foundation for intelligent healthcare decision-support systems.
Sana Rubab, Musarrat Shaheen, Zohrain Tabassum et al.· Biomedical Informatics and S...· 0 citations
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