Type 2 diabetes is a common life-changing disease that has been growing rapidly in recent years. According to the World Health Organization, approximately 90% of patients with diabetes worldwide have type 2 diabetes. Although there is no permanent cure for type 2 diabetes, this disease needs to be detected at an early stage to provide prognostic support to allied health professionals and develop an effective prevention plan. This can be accomplished by analyzing medical datasets using data mining and machine-learning techniques. Due to their efficiency, metaheuristic algorithms are now utilized in medical datasets for detecting chronic diseases, with better results than traditional methods. The main goal is to improve the performance of the existing approaches for the detection of type 2 diabetes. A bio-inspired metaheuristic algorithm called cuttlefish was used to select the essential features in the medical data preprocessing stage. The performance of the proposed approach was compared to that of a well-known bio-inspired metaheuristic feature selection algorithm called the genetic algorithm. The features selected from the cuttlefish and genetic algorithms were used with different classifiers. The implementation was applied to two datasets: the Pima Indian diabetes dataset and the hospital Frankfurt diabetes dataset; generally, these datasets are asymmetry, but some of the features in these datasets are close to symmetry. The results show that the cuttlefish algorithm has better accuracy rates, particularly when the number of instances in the dataset increases.

This paper investigates how to facilitate users’ exploration through data graphs. The prime focus is on knowledge utility , i.e. increasing a user’s domain knowledge while exploring a data graph, which is crucial in the vast number of user-facing sem

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.

The utilization of semantic web technologies has led to the development of knowledge graphs represented as triples that allow for the exploration of specific and cross-domains. Despite the advantages of semantic links between entities in facilitating user exploration, they can also lead to an overwhelming number of exploration choices that can cause confusion, frustration, uncertainty, and a sense of being lost in the abundant graph, particularly for users who are not familiar with the domain. Thus, identifying exploration strategies is critical to improving user exploration and increasing exploration utility. This study aims to identify exploration strategies that promote knowledge utility (i.e., increase users’ domain knowledge) and exploration experience (i.e., provide users with a positive and pleasant feeling). To accomplish this goal, an experimental user study was conducted, involving lay users in the musical instrument domain, where they were presented with an exploration task and then allowed to freely explore musical instruments. Parameters related to exploration paths were used to analyze the exploration patterns that users follow during their exploration. The findings reveal exploration strategies that promote knowledge utility and exploration experience. This research contributes to the literature on intelligent methods of guiding user exploration through knowledge graphs to enhance exploration effectiveness, which can have broad applications in knowledge graph utilization.

The growing number of available data graphs in the form of RDF Linked Data enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they interact with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by users who are not domain experts. We propose a new navigation support approach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cognitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two application domains - semantic browsing (Music) and semantic search (Careers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts.

Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.