Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Deep Reinforcement Learning for Medical Applications on Embedded Devices

Deep Reinforcement Learning for Medical Applications on Embedded Devices

Journal
Impact Score 2.97

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 30-06-2021
Journal Impact Score: 2.97
Journal Name: Microprocessors and Microsystems
Publisher: Microprocessors and Microsystems

Special Issue Call for Papers


Deep reinforcement learning (DRL) uses feedback from the agent to make decisions in complex problems under uncertainty. Medical applications often require processing large volumes of complex data in a challenging environment. Deep reinforcement learning can process this data by analyzing the agent's feedback that is sequential and sampled using non-linear functions. The deep reinforcement learning algorithms commonly used for medical applications include value-based methods, policy gradient, and actor-critic methods. The recent advances in the increased computational capabilities of architectures like field-programmable gate array (FPGA), graphics processing units (GPU), and digital signal processors (DSP) have made it possible to infer deep reinforcement learning algorithms on them. However, efficient implementation of these architectures should consider the issues related to their portability, wearability, and power consumption.



The main objective is to provide a platform for scientists, researchers, industry experts, and scholars to share their innovative contributions in deep reinforcement learning for medical applications on various embedded devices (ED). Research articles describing only a proof of concept are not encouraged. Authors are solicited to develop novel deep reinforcement learning algorithms on medical data and implement them either on FPGA, GPUs, or DSP. The special issue invites authors to submit papers that analyze the portability, wearability, power consumption of the deep reinforcement learning algorithms implemented either on FPGA, GPU, or DSP. The deep reinforcement learning topic includes but not restricted to:




Important dates




Submission instructions



When submitting your manuscript please select the article type “VSI:DRL-MA-ED”. Please submit your manuscript before the submission deadline. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.



Guest Editors




  1. Dr. Francesco Leporati
    Associate Professor of Computer Science
    University of Pavia, Italy
    Email: [email protected]
    Web: http://mclab.unipv.it/index.php/staff/2-non-categorizzato/17-leporati-eng


  2. Dr. Emanuele Torti
    Assistant Professor of Electrical, Computer and Biomedical Engineering
    University of Pavia, Italy
    Email: [email protected]
    Web: https://scholar.google.com/citations?user=80A5s4wAAAAJ&hl=en


  3. Dr. Yu-Dong Zhang
    Professor and Chair in Knowledge Discovery and Machine Learning
    University of Leicester, UK
    Email: [email protected]
    Web: https://www2.le.ac.uk/departments/informatics/people/yudong-zhang


  4. Dr. Hong Lin
    Professor of Computer Science and Engineering Technology
    University of Houston Downtown, USA
    Email: [email protected]
    Web: https://www.uhd.edu/academics/sciences/computer-science-engineering-technology/Pages/bio-linh.aspx


  5. Dr. João Manuel R. S. Tavares
    Associate Professor of Faculty of Engineering
    University of Porto, Portugal
    Email: [email protected]
    Web: https://web.fe.up.pt/~tavares/index_uk.html


  6. Dr. Steven L. Fernandes (Managing Guest Editor)
    Assistant Professor of Computer Science
    Creighton University, USA
    Email: [email protected]
    Web: http://www.creighton.edu/ccas/computersciencedesignjournalism/faculty/stevenfernandes

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