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 >

Electroluminescence imaging is widely used for detecting defects in solar cells. It reveals electrically active damage that remains invisible under conventional optical inspection. Most existing studies apply machine learning models to classify electroluminescence images and report performance mainly through accuracy scores. Inspection is often treated as an isolated prediction task, while physical defect mechanisms, sensing variability, representation bias, decision risk, and deployment constraints receive limited attention. As a result, strong benchmark results may not translate into reliable inspection outcomes in manufacturing environments. This paper presents a conceptual, non-systemati... 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 >