Preliminary Research on the Social Attitudes toward AI’s Involvement in Christian Education in Vietnam: Promoting AI Technology for Religious Education

Study and analysis of various link predictions in knowledge graph: A challenging overview

Knowledge Graph (KG) is the network which contains some topic-based entities, called nodes, and the associated information among the entities. Here, the concept in the knowledge graph is denoted by the tuple relationship, such that the ⟨entity1, predicate, entity2⟩. The entity in the knowledge graph is the abstract concepts based on the particular object, namely the organization, dataset, people, and some associated documentation. The big issue in the KG is that it consists of some incomplete information. The missing details can be identified by employing the knowledge graph completion (KGC) solution. KGC is the same as the link prediction concepts in the knowledge graphs. However, this concept is more complex that it does not predict the link relationship among the nodes but also the diversified information from the link relations. Hence this survey analyzes different methods of link prediction techniques, and this review provides a detailed review of 50 research papers concentrating on various methods, like embedding-based methods, deep learning methods, knowledge acquisition methods, ranking methods, and representation learning methods. The analysis is carried out with respect to the survey based on the publication year, research techniques, performance measures, dataset, toolset and achievement of the research methodologies. Also, the problems in the methods are explained in the research gaps and issues. Furthermore, the future extent of these research works is done based on the limitations identified from the existing research methods.

Automatic Brain Tumor Classification via Lion Plus Dragonfly Algorithm

Denoising, skull stripping, segmentation, feature extraction, and classification are five important processes in this paper's development of a brain tumor classification model. The brain tumor image will be imposed first using the entropy-based trilateral filter to de-noising and this image is imposed to skull stripping by means of morphological partition and Otsu thresholding. Adaptive contrast limited fuzzy adaptive histogram equalization (CLFAHE) is also used in the segmentation process. The gray-level co-occurrence matrix (GLCM) characteristics are derived from the segmented image. The collected GLCM features are used in a hybrid classifier that combines the neural network (NN) and deep belief network (DBN) ideas. As an innovation, the hidden neurons of the two classifiers are modified ideally to improve the prediction model's accuracy. The hidden neurons are optimized using a unique hybrid optimization technique known as lion with dragonfly separation update (L-DSU), which integrates the approaches from both DA and LA. Finally, the suggested model's performance is compared to that of the standard models concerning certain performance measures.