<div dir="ltr"><h1 class="gmail-sc-iRbamj gmail-jZOxOO gmail-sc-fCPvlr gmail-htpGQy gmail-heading-title" style="box-sizing:border-box;font-size:30px;font-weight:500;letter-spacing:1px;line-height:35px;margin-top:0px;margin-bottom:34px;color:rgb(0,0,0);font-family:"IBM Plex Sans",sans-serif">Interpretation of Machine Learning: Prediction, Representation, Modeling, and Visualization 2022</h1><div><a href="https://www.hindawi.com/journals/cin/si/470979/">https://www.hindawi.com/journals/cin/si/470979/</a><br></div><div><br></div><div><div class="gmail-SIDetails__CallForPapersWrappers-sc-xeh35r-4 gmail-xaqiQ" style="box-sizing:border-box;color:rgb(0,0,0);font-family:"IBM Plex Sans",sans-serif;font-size:16px"><h2 class="gmail-sc-eHgmQL gmail-fTkdCR gmail-SIDetails__SubtitleStyled-sc-xeh35r-6 ecdfxJ" style="box-sizing:border-box;font-size:25px;margin:21px 0px 0px;font-weight:500"></h2></div><div class="gmail-SIDetails__SiDesccriptionsWrappers-sc-xeh35r-7 gmail-iXvwZL" style="box-sizing:border-box;color:rgb(0,0,0);font-family:"IBM Plex Sans",sans-serif;font-size:16px"><p class="MsoNormal" align="left" style="margin:15.75pt 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:19pt;font-family:Arial,"sans-serif"">Call for papers</span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">This Issue is now open for
submissions.</span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Papers are published upon acceptance,
regardless of the Special Issue publication date.</span></p>
<div class="MsoNormal" align="center" style="text-align:center;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">
<hr size="2" width="100%" align="center">
</span></div>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif""><a href="https://www.hindawi.com/journals/cin/si/470979/"><span style="font-family:SimSun;color:black;text-decoration-line:none"></span></a></span></p>
<p class="MsoNormal" align="left" style="margin:15.75pt 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:19pt;font-family:Arial,"sans-serif""><a href="https://www.hindawi.com/journals/cin/si/470979/"><span style="color:black;text-decoration-line:none">Description</span><span style="font-family:SimSun;color:windowtext;text-decoration-line:none"></span></a></span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif""> </span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">The exponentially growing availability
of data such as images, videos and speech from myriad sources, including social
media and the Internet of Things, is driving the demand for high-performance
data analysis algorithms. Deep learning is currently an extremely active
research area in machine learning and pattern recognition. It provides
computational models of multiple nonlinear processing neural network layers to
learn and represent data with increasing levels of abstraction. Deep neural
networks are able to implicitly capture intricate structures of large-scale
data and deploy them in cloud computing and high-performance computing
platforms.</span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">The deep learning approach has
demonstrated remarkable performances across a range of applications, including
computer vision, image classification, face/speech recognition and medical
communications. However, deep neural networks yield ‘black-box’ input-output
mappings that can be challenging to explain to users. Especially in the
medical, military and legal fields, black-box machine learning techniques are
unacceptable, since decisions may have a profound impact on peoples’ lives due
to the lack of interpretability. In addition, many other open problems and
challenges still exist, such as computational and time costs, repeatability of
the results, convergence, and the ability to learn from a very small amount of
data and to evolve dynamically.</span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">The aim of this Special Issue is to
bring together original research articles and review articles that will present
the latest theoretical and technical advancements of machine and deep learning
models. Submissions about algorithms with improved computational efficiency and
scalability are also welcome. We hope that this Special Issue will: 1) improve
the understanding and explainability of deep neural networks; 2) enhance the
mathematical foundation of deep neural networks; and 3) increase the
computational efficiency and stability of the machine and deep learning
training process with new algorithms that will scale.</span></p>
<p class="MsoNormal" align="left" style="margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Potential topics include but are not
limited to the following:</span></p>
<ul type="disc" style="margin-bottom:0cm">
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Supervised, unsupervised, and reinforcement learning</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Classification, clustering, and optimization for big
data analytics</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Extracting understanding from large-scale and
heterogeneous data</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Dimensionality reduction and analysis of large-scale
and complex data</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Deep learning for time series forecasting</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Quantifying or visualizing the interpretability of
deep neural networks</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Stability improvement of deep neural network
optimization</span></li>
<li class="MsoNormal" style="text-align:left;margin:0cm 0cm 0.0001pt;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12pt;font-family:Arial,"sans-serif"">Novel machine and deep learning approaches in the
applications of image/signal processing, business intelligence, games,
healthcare, bioinformatics, and security</span></li>
</ul>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US"> </span></p><div class="gmail-sc-ccLTTT gmail-fmnfgA gmail-first" style="box-sizing:border-box;width:350px;margin-top:0px"><div class="gmail-sc-dRCTWM gmail-buLgAK gmail-publish_date" style="box-sizing:border-box;font-weight:bold;margin-bottom:0px;margin-top:0px">Publishing date</div><div class="gmail-sc-cgHJcJ gmail-gqTLOv" style="box-sizing:border-box">01 Dec 2022</div></div><div class="gmail-sc-ccLTTT gmail-fmnfgA gmail-status_label" style="box-sizing:border-box;width:350px;margin-top:8px"><div class="gmail-sc-dRCTWM gmail-buLgAK" style="box-sizing:border-box;font-weight:bold;margin-bottom:0px;margin-top:17px">Status</div><span color="#4D8A17" class="gmail-sc-hizQCF gmail-hGmZfD" style="box-sizing:border-box;background-color:rgb(77,138,23);color:rgb(255,255,255);padding:2px 6px;font-weight:bold;margin-top:5px;margin-bottom:5px;display:inline-block;margin-left:0px">Open</span></div><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;font-size:10.5pt;font-family:Calibri,"sans-serif""><span lang="EN-US"></span></p><div class="gmail-sc-ccLTTT gmail-fmnfgA gmail-last" style="box-sizing:border-box;width:350px;margin-top:8px;margin-bottom:15px"><div class="gmail-sc-dRCTWM gmail-buLgAK" style="box-sizing:border-box;font-weight:bold;margin-bottom:0px;margin-top:17px">Submission deadline</div><div class="gmail-sc-cgHJcJ gmail-gqTLOv" style="box-sizing:border-box">22 Jul 2022</div></div></div></div></div>