10 citations · 29 across the 12 of their papers we have counts for
13 papers
Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining
Yingbin Liang, Lu Dai, Shuo Shi +3
Complex data mining has wide application value in many fields, especially in the feature extraction and classification tasks of unlabeled data. This paper proposes an algorithm bas…
A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data
Ran Hao, Yanlin Xiang, Junliang Du +3
This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN's strength in detecting local featu…
A Deep Learning Approach to Interface Color Quality Assessment in HCI
Shixiao Wang, Runsheng Zhang, Junliang Du +2
In this paper, a quantitative evaluation model for the color quality of human-computer interaction interfaces is proposed by combining deep convolutional neural networks (CNN). By…
Multi-Scale Transformer Architecture for Accurate Medical Image Classification
Jiacheng Hu, Yanlin Xiang, Yang Lin +3
This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medic…
Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
Jiacheng Hu, Tai An, Zidong Yu +2
This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold…
A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
Junliang Du, Shiyu Dou, Bohuan Yang +2
This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditiona…
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
Houze Liu, Tong Zhou, Yanlin Xiang +3
Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high…
Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
Wenyi Liu, Ziqi Zhang, Xinshi Li +3
This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which…
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Aoran Shen, Minghao Dai, Jiacheng Hu +3
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional…
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Yinqiu Feng, Aoran Shen, Jiacheng Hu +3
This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges p…
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs
Junliang Du, Guiran Liu, Jia Gao +3
This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attentio…
Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis
Junliang Du, Yiru Cang, Tong Zhou +2
This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention m…
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
Guanming Huang, Aoran Shen, Yuxiang Hu +3
This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance…