The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.

Metric-based assessment of web user interface (WUI) quality attributes is shifting from code (HTML/CSS) analysis to mining webpages'visual representations based on image recognition techniques.In our paper, we describe a visual analysis tool which takes a WUI screenshot and produces structured and machine-readable representation (JSON) of the interface elements' spatial allocation.The implementation is based on OpenCV (image recognition functions), dlib (trained detector for the elements' classification), and Tesseract (label and content text recognition).The JSON representation is used to automatically calculate several metrics related to visual complexity, which is known to have major effect on user experience with UIs.We further describe a WUI measurement platform that allows integration of the currently dispersed sets of metrics from different providers and demonstrate the platform's use with several remote services.We perform statistical analysis of the

Component-based approach has proved its effectiveness in modern web engineering, where programmers increasingly rely on web development frameworks. In front-end web development parts of available solutions cannot be reused directly, due to technical and legal obstacles, so we propose a specific process to generate web user interfaces (WUI) designs from configurable components. The similarity between the generated designs and the exemplary high-quality solutions retrieved from case base is then optimized within the evolutionary algorithm. In our current paper we designate the structure of the components and justify the employment of Drupal as the organizational framework. We also describe the implementation details, including the two supplemental software tools that we developed: 1) web intelligence miner that collects website-related data from linked open data sources and 2) web UI screenshot analyzer that converts it into semantic-spatial representation in JSON format. Finally, we specify the new solutions generation algorithm, per the three WUI dimensions: functionality, layout and visual appearance.