Special Issues
Table of Content

Deep Neural Networks-based Convergence Technology and Applications

Submission Deadline: 30 March 2025 (closed) View: 883 Submit to Special Issue

Guest Editors

Prof. Ki-Hyun Jung

Email: kingjung@anu.ac.kr

Affiliation: Department of Software Convergence, Andong National University, Andong, 36729, South Korea

Homepage: https://www.andong.ac.kr/eng/html.do?menu_idx=92

Research Interests: AI, Cybersecurity, Big Data, CNN, DNN, AI Ethics

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Summary

This special issue aims to explore the cutting-edge advancements and applications of Deep Neural Networks (DNN) and their convergence with various technologies, particularly focusing on AI, Healthcare, Big Data, AI Ethics, and AI-based Technology and Applications.

 

The special issue invites high-quality, original research papers, comprehensive reviews, and case studies that focus on, but are not limited to, the following topics:

 

· Artificial Intelligence and Deep Learning

  Innovations in DNN architectures (CNN, RNN, GANs)

  Transfer learning and meta-learning

  Real-world applications of DNNs

 

· Healthcare and DNN Applications

  DNNs in medical imaging and diagnostics

  Predictive analytics for healthcare

  Personalized medicine using DNNs

 

· Big Data Analytics

  Interation of DNNs with big data technologies

  Scalability issues and solutions in DNNs for big data 

  Case studies on big data applications using DNNs


· AI Ethics and Fairness

  Ethical considerations in the deployment of DNNs

  Bias and fairness in AI models

  Responsible AI and transparency

 

· AI-based Technology and Applications

  AI in Autonomous Systems

  AI for Biomedical Applications 

  AI in Smart Cities and Urban Planning

  AI for Cybersecurity and Threat Detection

  AI in Education and Personalized Learning

 

We encourage authors to explore a wide range of innovative and interdisciplinary applications of AI technology, beyond those explicitly mentioned.


Keywords

AI, Healthcare, Big Data, CNN, DNN, AI Ethics

Published Papers


  • Open Access

    ARTICLE

    Traffic Flow Prediction in Data-Scarce Regions: A Transfer Learning Approach

    Haocheng Sun, Ping Li, Ying Li
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063029
    (This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)
    Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >

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