This paper discusses signature verification and recognition using a new approach that depends on a neural network which enables the user to recognize whether a signature is original or a fraud. The user introduces into the computer the scanned images, modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.

This study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59 key attributes that influence website performance, derived from an extensive literature review spanning from 2010 to 2024. By integrating a comprehensive set of performance metrics—encompassing usability, accessibility, content relevance, visual appeal, and technical performance—our framework transcends traditional methods that often rely on limited indicators. Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. Our findings reveal that SVMs achieved the highest predictive accuracy of 89% with feature selection, compared to 87% without feature selection. Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. The application of feature selection techniques significantly enhances model performance, demonstrating the importance of focusing on impactful predictors. This research addresses critical gaps in the existing literature by proposing a methodology that utilizes newly extracted features, making it adaptable for evaluating the performance of various website types. The integration of automated tools for evaluation and predictive capabilities allows for proactive identification of potential performance issues, facilitating informed decision-making during the design and development phases. By bridging the gap between predictive modeling and optimization, this study contributes valuable insights to practitioners and researchers alike, establishing new benchmarks for future investigations in web page performance evaluation.

New illumination and imaging techniques are continually being developed for cancer diagnosis. They need to be evaluated in the framework of a specific diagnostic problem. In this work, we evaluate the usefulness of fluorescence illumination within the framework of skin cancer diagnosis. This illumination provides monochrome images that encode certain information about deep layers of the skin, which can be particularly interesting for the diagnosis of skin lesions such as basal cell carcinoma. A broad study of candidate diagnostic features extracted from fluorescence images and evaluated within the framework of the posed diagnostic problem was conducted. Afterward, we used both a genetic algorithm (GA) and forward and backward scanning methods for feature selection and evaluated the diagnostic results by using the K-nearest neighbors (KNN) classifier. This work validates the fluorescence illumination technique for skin cancer diagnosis, indicating concrete image processing techniques that best target the diagnostic problem, and shows that the GA approach obtains the best classification results.