Special Issue on High Performance Computing Methods Suitable for Machine Learning Applications

Submission Deadline: Jun. 25, 2020

Please click the link to know more about Manuscript Preparation: http://www.ijsts.net/submission

Please download to know all details of the Special Issue

Special Issue Flyer (PDF)
  • Lead Guest Editor
    • Pasquale De Luca
      University of Salerno, Department of Computer Science, Fisciano, Italy
  • Guest Editor
    Guest Editors play a significant role in a special issue. They maintain the quality of published research and enhance the special issue’s impact. If you would like to be a Guest Editor or recommend a colleague as a Guest Editor of this special issue, please Click here to complete the Guest Editor application.
    • Antonio Mentone
      "University of Konstanz, Department of Computer and Information Science, Konstanz", Württemberg, Germany
    • Rita Maranta
      "University of Naples “L’Orientale”, Department of Literary, Linguistics and Comparative Studies", Naples, Italy
    • Stefano Fiscale
      "University of Naples “Parthenope”, Department of Science and Technology", Naples, Italy
    • Luca Landolfi
      "University of Naples “Parthenope”, Department of Science and Technology", Naples, Italy
    • Padmavathi K
      Department of Computer Applications, PSG College of Arts and Science, Coimbatore, Tamilnadu, India
    • Javad Salehi
      Assistant Professor of Payam-e-Noor University, Tehran, Iran
    • Chensen Ding
      Institute of Computational Engineering, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
    • Francesco De Feo
      Department of Computer Science,University of Salerno, Fisciano, Italy
  • Introduction

    The use of Machine Learning techniques is constantly growing in several research fields such as computer science, engineering, medical sciences, economics and other scientific fields. The large datasets that have to be processed have exponential increased in size terms. Hence it requires computational resources and ad hoc techniques to deal with these problems efficiently and effectively, for example Convolutional Neural Networks. The latter can represent data using sparse methods that requires more time and resource complexity, due to both limited to canonical software modeling of Machine Learning algorithms and huge data to be processed. In recent years new parallel and distributed architectures has been introduced. In fact, the use of GP-GPU combining with virtualization systems and framework such as Hadoop are able to solve large problems. For example, Patter Recognition in several medical fields is able to detect illnesses better than human doctors. Natural Language Processing can benefit from ML combining HPC to solve problems related to Speech Recognitions, Automatic Translation and Vocal Assistants. The aims of this Special Issue include capturing the latest achievements in covered topics, building a scientific background to create new or improve existing Machine Learning techniques and combining them with high efficiency and most recently techniques of High Performance Computing.

    Aims and Scope:

    1. High Performance Computing Algorithm
    2. Machine Learning Theory and Algorithms
    3. Scientific Computing
    4. Embedded Systems
    5. Distributed Operating Systems
    6. Image Processing

  • Guidelines for Submission

    Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.

    Papers should be formatted according to the guidelines for authors (see: http://www.ijsts.net/submission). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.