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      <p>[Apologies if you receive multiple copies of this CFP] <br>
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        <p> </p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;
          mso-outline-level:1"><b><span style="font-size: 14pt;"
              lang="EN-US">Call for Papers: <span style="color:#0070C0">Emerging
                Trends and Applications of Deep Learning for Biomedical
                Data Analysis </span></span></b></p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span
            style="color:blue;mso-ansi-language:EN-US" lang="EN-US"><a
              href="https://www.springer.com/journal/11042/updates/24678968"
              class="moz-txt-link-freetext">https://www.springer.com/journal/11042/updates/24678968</a></span></p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>Summary
            and Scope</b></p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Nowadays,
          Deep learning (DL) becomes an attractive research topic for
          many researchers from academia and industry communities.
          Indeed, DL algorithms have demonstrated their ability to train
          learning models for large-volume data as well as their
          performances compared to conventional machine learning
          algorithms. The DL approaches were studied and applied to
          resolve several complex problems in various research domains,
          such as computer vision, biometrics, brain-computer
          interfaces, robotics, and other fields. Several architectures
          of DL (e.g., supervised, unsupervised, reinforcement, and
          beyond) have been proposed in the literature as solutions for
          various research problems in data analysis related to
          detection, classification, recognition, prediction,
          decision-making, etc.<br>
          <br>
          The special issue aims to solicit original research work
          covering novel algorithms, innovative methods, and meaningful
          applications based on the DL that can potentially lead to
          significant advances in biomedical data analysis.</p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">The
          main topics include, but are not limited to, the following:<br>
          <br>
          • DL for biomedical signal analysis and processing<br>
          • DL for medical image analysis and processing<br>
          • DL for diseases detection and diagnosis<br>
          • DL for pandemics detection and forecasting<br>
          • DL for biometrics<br>
          • DL in biomedical engineering<br>
          • DL for health informatics<br>
          • DL for brain-computer interfaces<br>
          • DL for neural rehabilitation engineering<br>
          • Related applications</p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><br>
          </b></p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>Important
            Dates:</b><br>
          Submission deadline: August 31, 2023<br>
          Reviewing deadline: October 15, 2023<br>
          Author revision deadline: November 15, 2023<br>
          Final notification date: December 15, 2023</p>
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          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><br>
          </b></p>
        <p class="MsoNormal"
          style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>Guest
            editors</b><br>
          Prof. Larbi Boubchir (Lead GE) - University of Paris 8, France<br>
          Email: <a class="moz-txt-link-abbreviated
            moz-txt-link-freetext"
            href="mailto:Larbi.boubchir@univ-paris8.fr">Larbi.boubchir@univ-paris8.fr</a><br>
          <br>
          Prof. Elhadj Benkhelifa - Staffordshire University, UK<br>
          Email: <a class="moz-txt-link-abbreviated
            moz-txt-link-freetext" href="mailto:Benkhelifa@staffs.ac.uk">Benkhelifa@staffs.ac.uk</a><br>
          <br>
          Prof. Jaime Lloret - Universitat Politecnica de Valencia,
          Spain<br>
          Email: <a class="moz-txt-link-abbreviated
            moz-txt-link-freetext" href="mailto:jlloret@dcom.upv.es">jlloret@dcom.upv.es</a><br>
          <br>
          Prof. Boubaker Daachi - University of Paris 8, France<br>
          Email: <a class="moz-txt-link-abbreviated
            moz-txt-link-freetext"
            href="mailto:boubaker.daachi@univ-paris8.fr">boubaker.daachi@univ-paris8.fr</a><br>
          <br>
          <b>Submission Guidelines:</b><br>
          Authors should prepare their manuscript according to the
          Instructions for Authors available from the Multimedia Tools
          and Applications <a
            href="https://www.springer.com/journal/11042/"
            target="_blank"><span style="mso-ansi-language:EN-US"
              lang="EN-US">website</span></a>. Authors should submit
          through the online submission site at <a
            href="https://www.editorialmanager.com/mtap/default.aspx"
            target="_blank"><span style="mso-ansi-language:EN-US"
              lang="EN-US">https://www.editorialmanager.com/mtap/default.aspx</span></a>
          and select “SI 1239 - Emerging Trends and Applications of Deep
          Learning for Biomedical Data” when they reach the “Article
          Type” step in the submission process. Submitted papers should
          present original, unpublished work, relevant to one of the
          topics of the special issue. All submitted papers will be
          evaluated on the basis of relevance, significance of
          contribution, technical quality, scholarship, and quality of
          presentation, by at least three independent reviewers. It is
          the policy of the journal that no submission, or substantially
          overlapping submission, be published or be under review at
          another journal or conference at any time during the review
          process.</p>
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