The critical node detection problem describes a class of graph problems that involves identifying sets of nodes that influence a given graph metric. One variant of this problem is to find the nodes that – when removed from the graph – maximize the number of connected components in the remaining graph. This is an…
Nowadays, software systems encounter repeated modifications in order to satisfy any requirement regarding a business change. To assure that these changes do not affect systems' proper functioning, those parts affected by the changes need to be retested, minimizing the negative impact of performed modifications on another part of the software. In this research, we…
Critical node detection is a crucial task in network analysis. In this article a new problem is proposed, the critical node detection in hypergraphs, which are a generalization of the ‘traditional’ graphs. A genetic algorithm is proposed to solve this problem and as an application an inflation dataset is transformed in a hypergraph and…
Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a…
The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on simple, local…
The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining the…
We aimed to assess the reliability of an automatic solution for whole-heart segmentation of MRI images of patients with atrial fibrillation (AF). We propose a semi-interactive image segmentation algorithm based on region growing, GrowCut1, using novel neighborhood structures based on Cellular Automata. We complemented the algorithm with a global view of the signal using…
We aimed to assess the reliability of teaching Artificial Intelligencefor Software Engineering master students. We propose a semi-interactive course where the students have to develop applications for solving real world problems by using various intelligent tools. We try to integrate these two disciplines, since both deal with modeling of the real case studies, sharing…
Cellular automata have been widely used for solving the edge detection problem. This paper proposes an algorithm which optimizes cellular automata rules using Particle Swarm Optimization based on an existing method in the literature. Moreover, the method is extended from grayscale to RGB images by performing the optimization on each colour channel individually. A…
In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide…