Procedia Computer Science Authors Madalina Dicu, Camelia Chira Abstract User interfaces look different across web, desktop, and mobile platforms — not just in layout, but in how buttons, icons, and text appear. This makes it hard for deep learning models trained on one platform to accurately detect UI components on another. In this paper,…
IEEE Transactions on Intelligent Vehicles Authors Alexandru Manole, Laura Diosan Abstract Extracting essential information including the topological structure of rail-tracks, the position of switches and their current state can increase safety by reducing human error, while also boosting the efficiency of rail transportation. Despite the impressive advancements in the field of autonomous driving, computer…
Hybrid Artificial Intelligence Systems Authors Madalina Dicu, Enol García González, Camelia Chira, José R. Villar Abstract Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection crucial for improving survival rates. Deep learning-based approaches have shown remarkable potential in automating tumor detection from mammographic images; however, their effectiveness…
International Conference on Agents and Artificial Intelligence Authors Madalina Dicu, Enol García González, Camelia Chira, José R. Villar Abstract The identification of elements in user interfaces is a problem that can generate great interest in current times due to the significant interaction between users and machines. Digital technologies are increasingly used to carry out…
Artificial Intelligence Applications and Innovations Authors Madalina Dicu, Camelia Chira Abstract This article examines the use of image augmentation techniques to improve icon detection in mobile interfaces, a critical task due to the small size of graphical user interface (GUI) elements and the insufficiency of comprehensive datasets. It evaluates whether diversifying the dataset or…
Hybrid Artificial Intelligence Systems Authors Madalina Dicu, Enol García González, Camelia Chira, J. Villar Abstract Detecting icons in Graphical User Interfaces (GUIs) is essential for effective application automation. This study examines the impact of different annotation methods on the performance of object detection models for icon detection in GUIs. We compared manual, automated, and hybrid annotations…
