Overview
This is a call for papers for a special issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS) dedicated to the theme of Deep Representation and Transfer Learning for Smart and Connected Health. This special issue aims to gather innovative research that addresses the challenges and advancements in applying deep learning techniques to the healthcare domain.
Background & Relevance
Deep neural networks (DNNs) have emerged as powerful tools for learning complex data representations. However, effectively learning representations that are tailored for specific tasks remains a significant challenge. Representation and Transfer Learning (RTL) can enhance the generalization capabilities of DNNs, allowing models trained on one domain to be adapted for use in another. This is particularly relevant in the context of Smart and Connected Health (SCH), where the need for robust models is critical due to the complexities of healthcare data and the often limited availability of labeled datasets. By leveraging RTL, researchers can develop models that improve health monitoring, diagnosis, and prediction.
Key Details
- Submission Deadline: 30 September 2019
- Reviewer’s Comments: 31 December 2019
- Revised Paper Submission: 28 February 2020
- Final Decision: 30 April 2020
- Tentative Publication Date: May-June 2020
- Topics of Interest:
- Theoretical Methods: Distributed representation learning, transfer learning, domain adaptation, and more.
- Application Areas: Health monitoring, health diagnosis, early detection, and virtual patient monitoring.
Eligibility & Participation
This special issue is open to researchers and practitioners in the fields of machine learning, artificial intelligence, and healthcare. It targets those who are working on theoretical advancements and practical applications of deep learning in health-related contexts.
Submission or Application Guidelines
- Review the Information for Authors at IEEE TNNLS.
- Submit your manuscript via the TNNLS webpage at TNNLS Submission, ensuring to indicate that your submission is for this special issue on the first page and in your cover letter.
- Notify the guest editor, Vasile Palade, via email (vasile.palade@coventry.ac.uk) with the subject “TNNLS special issue submission” upon submission.
- Early submissions are encouraged, as the review process will commence upon receipt of contributions.
Additional Context / Real-World Relevance
The integration of deep learning techniques into healthcare has the potential to revolutionize how health data is processed and utilized. As the field of Smart and Connected Health continues to evolve, the application of RTL can address significant challenges such as data scarcity and bias, ultimately leading to more effective health interventions and improved patient outcomes.
Conclusion
Researchers are encouraged to contribute to this special issue, sharing their insights and advancements in deep representation and transfer learning as they relate to smart health applications. This is an excellent opportunity to engage with the community and influence the future of healthcare technology.
Category: CFP & Deadlines
Tags: deep learning, transfer learning, smart health, neural networks, health monitoring, biomedical processing, domain adaptation, health prediction
