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Conceptual distance refers to the degree of proximity between two concepts within a conceptualization. It is closely related to semantic similarity and relationships, but its measurement strongly depends on the context of the given concepts. DIS-C represents an advancement in the computation of semantic similarity/relationships that is independent of the type of knowledge structure and semantic relations when generating a graph from a knowledge base (ontologies, semantic networks, and hierarchies, among others). This approach determines the semantic similarity between two indirectly connected concepts in an ontology by propagating local distances by applying an algorithm based on the All Pairs Shortest Path (APSP) problem. This process is implemented for each pair of concepts to establish the most effective and efficient paths to connect these concepts. The algorithm identifies the shortest path between concepts, which allows for an inference of the most relevant relationships between them. However, one of the critical issues with this process is computational complexity, combined with the design of APSP algorithms, such as Dijkstra, which is n 3 . This paper studies different alternatives to improve the DIS-C approach by adapting approximation algorithms, focusing on Dijkstra, pruned Dijkstra, and sketch-based methods, to compute the conceptual distance according to the need to scale DIS-C to analyze very large graphs; therefore, reducing the related computational complexity is critical. Tests were performed using different datasets to calculate the conceptual distance when using the original version of DIS-C and when using the influence area of nodes. In situations where time optimization is necessary for generating results, using the original DIS-C model is not the optimal method. Therefore, we propose a simplified version of DIS-C to calculate conceptual distances based on centrality estimation. The obtained results for the simple version of DIS-C indicated that the processing time decreased 2.381 times when compared to the original DIS-C version. Additionally, for both versions of DIS-C (normal and simple), the APSP algorithm decreased the computational cost when using a two-hop coverage-based approach. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is crucial to infer the activities of human beings, and to provide support in various aspects such as monitoring, alerting, and security. Distinct activities may possess similar movements that need to be further distinguished using contextual information. In this paper, we extract features for context-aware HAR using a convolutional neural network (CNN). Instead of a traditional CNN, a combined 3D-CNN, 2D-CNN, and 1D-CNN was designed to enhance the effectiveness of the feature extraction. Regarding the classification model, a weighted twin support vector machine (WTSVM) was used, which had advantages in reducing the computational cost in a high-dimensional environment compared to a traditional support vector machine. A performance evaluation showed that the proposed algorithm achieves an average training accuracy of 98.3% using 5-fold cross-validation. Ablation studies analyzed the contributions of the individual components of the 3D-CNN, the 2D-CNN, the 1D-CNN, the weighted samples of the SVM, and the twin strategy of solving two hyperplanes. The corresponding improvements in the average training accuracy of these five components were 6.27%, 4.13%, 2.40%, 2.29%, and 3.26%, respectively. [ABSTRACT FROM AUTHOR]