This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course …

Formal concept analysis (FCA) is an important analytical tool for cognitive science. The generalized one-sided concept lattice extends the classical concept lattice, which considers the order between the attributes values. The structure of …

Data Augmentation is an essential technology for improving the performance of deep learning models. However, the semantic information change in current data augmentation methods may impair the model performance, especially in randomly …

Due to its ability to effectively address the cold start and sparsity problems in collaborative filtering, knowledge graph is commonly used as auxiliary information in recommendation systems. However, the existing recommendation algorithms based …

Precise and reliable traffic predictions play a vital role in contemporary traffic management, particularly within complex traffic networks. Currently, the approach which utilizes static graph convolution with recurrent neural networks for traffic …

Network anomaly detection (NAD) is a crucial Artificial Intelligence (AI)-based security solution for protecting computer networks. However, analyzing high-dimensional data is a significant impediment for NAD systems. The process of Feature …

The application of deep learning has demonstrated impressive performance in computer vision tasks such as object detection, image classification, and image captioning. Though most models excel at performing single vision or language tasks …

The traditional music appreciation course teaching model relies on questionnaires or manual decision-making to determine teaching content, which is time-consuming and easily reduces student satisfaction and teaching quality. How to use artificial …

Chinese named entity recognition (CNER) is one of the important tasks in the field of information extraction. And different divisions of CNER for text processing units can be generally classified into character granularity and word granularity.

Helmet detection in road surveillance images has become increasingly important with the increasing number of accidents involving two-wheeled electric vehicles and motorcycles. However, small detection targets and complex road environments make …

Aggregating deep features for image retrieval has shown excellent performance in terms of accuracy. However, exploring visual perception properties to activate the dormant discriminative cues of deep convolutional feature maps has received little …

Link prediction is an important task in social network analysis. Considering that the properties of nodes in social networks are generally inaccurate, it is more reliable and effective to use the network structure features to predict the links in …

Multi-attribute group decision making (MAGDM) is a pivotal tool in diverse evaluations. However, existing approaches often overlook attribute ambiguity and interrelationships, leading to unreliable outcomes. This article introduces a novel MAGDM …

The Internet of Things (IoT) connects billions of devices. However, because of its heterogeneous system and broad connectivity, it is vulnerable to various intrusion challenges, resulting in data and financial loss. The IoT environment must be …

The distribution of data often evolves over time, necessitating classifiers to adjust in order to maintain optimal classification accuracy. This phenomenon, termed “concept drift”, poses a significant challenge. Detectors specifically designed for …

Domain generalization person re-identification (DG Re-ID) aims to deploy a Re-ID model trained on multiple source domains to unseen domains without adaptation, which is a practical and challenging problem. Due to the significant drop in …

Few-shot learning aims to classify novel images based on a small number of labeled examples. While recent work has shown promise using local descriptors, existing methods generally classify local descriptors independently, which potentially can …

Hierarchical reinforcement learning (HRL) is a promising method to extend traditional reinforcement learning to solve more complex tasks. HRL can solve the problems of long-term reward sparsity and credit assignment. However, the existing HRL …

Self-taught learning models are successfully applied to improve the target model’s performance in different low-resource environments. In this setting, features are learned using unlabeled instances in the source domain; thereafter, the learned …

The medical big data of combined Chinese and western medicine diagnosis and treatment (CCWMDT) is difficult to be used effectively in clinical medical decision-making because of its complex structure and multi-source storage. In this paper, we …