• Welcome to the webpage of the book

    "Machine learning in analysis of biomedical and socio-economic data" (MLABSED 2017)!


    Best regards,
    scientific editors,
    Naidenova X.A., Military Medical Academy, Senior scientist, Candidate of Sciences in Engineering

    Yakovlev A.V., Military Medical Academy, Head of research laboratory, Candidate of Sciences in Engineering 

    Shvetsov K.V., Peter the Great St.Petersburg Polytechnic University, Professor, Candidate of Sciences in  Economics

    technical editor,
    Parkhomenko V.A., Peter the Great St.Petersburg Polytechnic University, Software engineer

    • Authors are encouraged to submit short or long research papers
      to MLABSED book, which is going to be published  
       at the beginning of 2018
      in Publishing-polygraphic center of

      Peter the Great St.Petersburg Polytechnic University (SPbPU).


      Important dates:

      1 february 2017  5 may 2018 (extended, hard deadline) chapter submission deadline (a pdf via Easychair)
      15 march 2017  15 may 2018 (extended, hard deadline) notification of acceptance/rejection
      1 april 2017  20 may 2018 (extended, hard deadline) final chapter (camera-ready) submission (a pdf via Easychair and a zip file with chapter \( \LaTeX2e \) source via email)

      The deadlines are not extendable, because of the expiry date of Easychair licence and the neccesity to submit the book to the Web of Science till the summer.

      Types of research papers (in Russian or in English):
      • Short 18-23 pages;
      • Long 28-38 pages. 

      The book will be sent to
      • Сlarivate Analytics for evaluation in Web of Science Core Collection (Book Citation index);
      • Elibrary for evaluation in Russian Science Citation Index (and free full texts distribution); 
      • SPbPU DOI center for providing persistent identifiers;
      • SPbPU Library for free full texts distribution.

      MLABSED topics include but are not restricted to:

      • Theoretical problems of machine learning in biomedical and socio-economic investigations:
        • foundations;
        • algorithms;
        • data preprocessing;
        • visualization;
        • big data;
      • Applications of machine learning in biomedical and socio-economic investigations:
        • correlations between the body indicators; 
        • evaluation and forecasting of the body conditions;
        • investigation of the body indicators impact on skills, performance...
        • data mining in biotechnologies
        • educational data mining
      • Infrastructure of machine learning in biomedical and socio-economic investigations:
        • software;
        • hardware ... 

      Third call for chapters in Russian (Третье письмо-приглашение принять участие в главе).


      Submission