Hue University Journal of Science: Techniques and Technology

D-STOR: A Novel Framework of Deep-Semantic Traffic Object Recognition

2023-06-07T03:52:30+00:00

Deep learning techniques such as Convolutional Neural Networks (CNNs) have proven the efficiency in recognizing image objects. Moreover, this recognition work has been extended to discover relations among detected objects. Although this research line of mining semantic information in image has become more attractive, it was not investigated thoroughly. This paper introduces a deep-semantic traffic object recognition based on a knowledge model to reveal relations among detected objects, named D-STOR. In order to confirm the efficiency of the D-STOR framework, an experiment on a dataset of traffic images in Vietnam was conducted and then yielded promising experimental results.

Effect of similarity measures in diffusion prediction on homogeneous and heterogeneous bibliographic network

2023-11-23T14:33:50+00:00

This paper evaluates the effect of similarity measures in predicting diffusion on homogeneous and heterogeneous bibliographic networks. The bibliographic network is analyzed within a homogeneous network and heterogeneous network, where a co-author relationship exists for the former, and multiple types of meta paths are considered for the latter. The supervised learning method is used to predict whether a node will be active with a topic or not. The features are extracted as the activation probability of a node, which represents the maximum of the activation probabilities of the neighbors of this node. In a homogeneous network, the activation probability from the activated node to the inactive node is measured based on one relationship co-author with basic similarity measures while it can be calculated based on diverse meta paths with dissimilar meta path-based similarity measures in the heterogeneous network. We performed our analysis on three different datasets. Our experimental results show that diffusion prediction in bibliographic networks provides better accuracy among heterogeneous networks than among homogeneous networks and that the Bayesian similarity measure provides the best efficiency.

Boosting Prediction of Protein-Protein Interactions using Word Embedding Techniques

2023-02-07T03:55:55+00:00

Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation, protein-disease relationship identification. Machine learning methods have been developed for the PPI prediction task in order to reduce the cost and time of previous experimental methods. In this paper, we study a method for determining PPIs using deep learning and protein sequence representation learning. In our method, an word embedding technique is utilized for protein sequence representation learning. This technique captures the semantic relationship between amino acids in protein sequences. The semantic relationship is then used as the input information, which is fed into a neural network to help recognize the interaction signature of the input protein pair. Different from previous studies, we integrate the protein sequence embedding mechanism into a neural network model. Thereby, the protein sequence embedding is better controlled for PPI prediction by our neural network model. We evaluate our method on benchmark datasets including Yeast, Human, and eight different independent sets. In addition, we also conduct an extensive comparison with the other existing methods. Our results show that the proposed method is superior to other existing methods and achieves high efficiency in predicting cross-species PPIs. The dataset and our source code are available at https://github.com/thnhub/BoostPPIP.git.