An important problem in network science is finding relevant community structures in complex networks. A community structure is a partition of the network nodes into clusters or modules, such that each cluster is densely connected. Current community detection algorithms have time complexity, centralization, and scalability issues. In this paper, to solve this problem, we…
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate…
Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to provide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly…
Citare Marginean, R., Andreica, A., Diosan, L., Bálint, Z., Stochastic remote neighborhoods control GrowCut’s segmentation semanticsMECO45 45th Conference of the Middle European Cooperation in Statistical Physics , 2020
Education has suffered multiple changes due to technological progress. Even though the current generation of preschoolers (aged 3 to 6 years in our country) is called digital native, there is a lack of focus on introducing technology enabled learning tools for them. This paper presents our approach to designing smart learning experiences for a…
We present a new type of optimization algorithms, adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices…
This paper proposes a novel method for supervised optimization of cellular automata rules using curriculum learning. The optimized edge detector manages to generalize a rule from synthetic data that is applicable to magnetic resonance images, removing the need for manual annotation of medical data. The method achieves competitive results with classical edge detectors on…
Children of nowadays grow in a digital landscape, so education has embraced the advantages brought by the multimedia technology progress. Appropriate interactive learning experiences positively influence learners’ performance. However, new challenges occur when the learners are preschoolers, as they are not able to articulate and communicate their experience towards interaction with edutainment applications. In…
Edge detection is an important component in many computer vision tasks since edges convey information about the objects in an image. This paper presents a comparative analysis of the proposed edge detector with respect to one of the state-of-the-art methods, the Canny edge detector. Our edge detection model involves the supervised optimization of a…
Citare Mărginean, R., Manole, S., Popa, L., Coman, M., Pop, S., Budurea, C., Andreica, A., Dioşan, L., Bálint, Z., Initial validation of an automatic algorithm for whole heart segmentation from cardiac MRI images using atrial volume determination, European congress of radiology (ECR 2020) https://www.myesr.org/congress