Student mental health and academic stress are significant concerns in higher education, creating a need for effective approaches to early risk identification. This study proposes a multiclass predictive framework for academic stress and mental health risk classification among students. The framework was developed using a Kaggle dataset containing 25,000 records with demographic, academic, behavioral, psychological, lifestyle, and medical attributes. A unified comparison was conducted across six conventional machine learning algorithms, namely Decision Tree, Random Forest, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Naïve Bayes, and three deep learning architectures, namely Convolution... More >

Brain tumor segmentation plays an important role in clinical diagnosis, treatment planning, and neuro-oncology assessment using multimodal Magnetic Resonance Imaging (MRI) data. However, conventional centralized deep learning systems often face limitations associated with patient data privacy, secure inter-institutional collaboration, and limited model interpretability. 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. A 3D U-Net architecture was trained on... More >

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting reproductive-age women, leading to infertility, hormonal imbalance, insulin resistance, and cardiovascular complications. Early diagnosis remains challenging due to heterogeneous manifestations, overlapping symptoms, and lack of automated screening tools. To address these issues, this study presents a comprehensive comparative framework for PCOS prediction using machine learning and deep learning on a public dataset of 541 patient records. The framework incorporates missing value imputation, feature standardization, SMOTE class balancing, and correlation-based feature selection. Five machine learning algorithms (Dec... More >

Digital image manipulation has become increasingly prevalent with the widespread availability of editing tools, raising concerns regarding image authenticity in critical applications. This study presents a passive image forgery detection framework based on multiscale Weber Local Descriptor features extracted from chrominance components and classified using a Support Vector Machine. The proposed method operates without embedded authentication information and focuses on detecting both copy-move and splicing forgeries through texture-based analysis. Experiments were conducted on two benchmark datasets, CASIA v2.0 and MICC F2000, using ten-fold cross-validation. On the CASIA v2.0 dataset, the fr... More >

Accurate brain tumor classification from MRI remains essential for computer-assisted diagnosis, yet manual interpretation is time-consuming and variable. This study presents an EfficientNet-B0-based convolutional neural network for multi-class classification of glioma, meningioma, pituitary tumors, and no-tumor cases. The model was trained and evaluated on a public MRI dataset of 7023 images using a strict patient-level split to ensure unbiased assessment. A fixed EfficientNet-B0 backbone with a lightweight classification head reduces overfitting while maintaining stable learning. Performance was assessed via accuracy, precision, recall, F1-score, and specificity. The model achieved class-wi... More >