Kinship verification using facial images has become an important area of research within computer vision. This paper presents a robust kinship verification system that leverages Histograms of a Two-Dimensional Discrete Wavelet Transform (Hist-2D-DWT) to improve recognition accuracy. The system extracts planar features by decomposing images into four coefficients—approximation, horizontal, vertical, and diagonal—via discrete wavelet transform. Comprehensive experiments conducted on the TSKinFace and Cornell KinFace datasets validate the method’s effectiveness, achieving accuracies of 93.46% and 89.61%, respectively.

Skin cancer is one of the most common and potentially deadly forms of cancer, making early and accurate detection crucial for effective treatment. In this context, leveraging advanced machine learning techniques can significantly enhance diagnostic accuracy and efficiency. This paper presents an intelligent system for monitoring skin cancer, leveraging a deep feature extraction approach based on Convolutional Neural Networks (CNNs) combined with a Random Forest (RF) classifier. The system processes skin cancer images to extract features using three pre-trained CNN models: ResNet18, Darknet19, and MobileNetv2, which are then used to train an RF classifier for accurate skin cancer detection. The proposed model is designed for edge deployment, making it suitable for real-time applications. Experiments were conducted using the "Skin Cancer: Malignant vs. Benign" dataset, specifically curated for skin cancer classification. The results demonstrate the effectiveness of the proposed system, achieving an accuracy of 82.85%, with a precision of 82.69%, a sensitivity of 82.86%, a specificity of 82.86%, and an F1-Score of 82.75%.