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 using specific augmentation methods alone can enhance detection performance. The study…
Entity resolution, the process of discerning whether multiple data refer to the same real-world entity, is crucial across various domains, including education. Its quality assessment is vital due to the extensive practical applications in fields such as analytics, personalized learning or academic integrity. With Python emerging as the predominant programming language in these areas,…
The latest improvements regarding the online world have come with great benefits, but, as well as dangerous drawbacks (i.e., web-malware). This article proposes to investigate the reliability and accuracy of a novel web-malware detection method by using images and deep learning. The web links are transformed into colored and grayscale images and then a…
The integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. This paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency…
The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in…
Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing for…
Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights…
Our research focuses on creating a framework for extracting 3D deformable objects from 2D scenes. We research the possibility of using multiple graph convolutional operators and depth estimators to extract the object, while also using predefined segmentation masks for the objects in the images. The experiments focus on a dataset from 2017, containing all…
Obtaining 3D representation of objects from scenes composed out of multiple images or frames is a task that often reacquires advanced hardware and knowledge in 3D rendering. With the rise of machine learning applications, the task became easier to solve for static objects, but there is still a lot of progress to be made…
Network motifs in complex networks signify critical patterns of connections essential for deciphering system dynamics. Identifying and understanding these rare and elusive motifs is crucial for analyzing complex network behaviors. Our previous research has established a significant positive correlation between the occurrence of motifs and two network properties at the micro level, namely Assortativity…