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<html>
<head>
<link rel="stylesheet" href="style.css">
</head>
<body>
<div class='container'>
<div class='container-image'></div>
<div class='main-navbar'>
<div class='ieee-logo'></div>
</div>
<div class='main-content'>
<div class='main-header'>
<h1>Special Session on Advances in Deep Fuzzy Systems</h1>
<div class='main-header-subtext coral-title'>
<h2> <span><a href='http://wcci2022.org/'>IEEE WORLD CONGRESS ON COMPUTATIONAL
INTELLIGENCE</a></span></h2>
</div>
<div class='main-header-subtext coral-title'>
<h2> <span><a href='http://wcci2022.org/'>Special Session Chairs</a></span></h2>
</div>
<div class='main-header-subtext'>
<div class='trail'> <span> Uzay Kaymak </span> u.kaymak@ieee.org </div>
<div class='trail'> Eindhoven University of Technology, The Netherlands </div>
</div>
<div class='main-header-subtext'>
<div class='trail'> <span> João Sousa </span> jmsousa@tecnico.ulisboa.pt </div>
<div class='trail'> IDMEC, instituto Superior Técnic, Universidade de Lisboa, Portugal </div>
</div>
<div class='main-header-subtext'>
<div class='trail'> <span> Alexander Gegov </span> alexander.gegov@port.ac.uk </div>
<div class='trail'> University of Portsmouth, United Kingdom </div>
</div>
<!-- <div class='btn' id='about-page'>Read More</div> -->
</div>
<div class='sub-content'>
<h2>Programme Description</h2>
<p>
Deep learning has gained significant attention within the computational intelligence community over
the recent years. Its success has been mainly due to the increased capability of modern computers to
collect, store and process large volumes of data. This has led to a substantial increase in the
effectiveness and efficiency of data management. As a result, it has become possible to achieve high
accuracy within a short time frame for some benchmark learning tasks such as object classification
and image recognition. The most common implementation of deep learning has been through neural
networks due to the ability of their layers to perform multiple functional composition as part of a
multistage learning process. However, despite the significant advances in deep learning, there are
also limitations. Effectiveness is usually adversely affected when data are not well defined due to
inherent noise, uncertainty, ambiguity, vagueness and incompleteness. This has an adverse impact on
efficiency due to the necessity to refine the data by additional collection, analysis and cleaning.
The reduced effectiveness and efficiency undermine the ability of deep learning to address some
real-life tasks that are safety critical or time critical systems. Besides this, deep leaning has
been used mainly in a passive manner for the purpose of observing the environment, but it almost has
not been used in an active manner for the purpose of changing the environment. Finally, deep
learning models often have poor transparency which makes them difficult for understanding,
explanation, and interpretation by non-technical users.
</p>
<p>
Deep fuzzy systems could address some of these problems and limitations. Deep learning can also be
used for developing fuzzy systems that can solve more complicated tasks in more dynamic
environments. DFS have been around in different forms and under different names such as hierarchical
fuzzy systems and networked fuzzy systems. DFS are well suited for performing multiple functional
composition at both crisp and linguistic level. Moreover, they have the potential of handling
effectively and efficiently data that are not well defined, due to the ability of fuzzy logic to
deal successfully with different types of uncertainty. Also, DFS can be used in both passive and
active manner with regards to the environment due to their generic structure. Finally, these systems
have a high level of transparency due to the ability of fuzzy rules to capture well the interactions
between input and output variables.
</p>
</div>
<div class='side-by-side'>
<div class='sub-content'>
<h2>Scope</h2>
<p>This special session aims to bring together contributions on recent advances in deep fuzzy
systems
(DFS), both from a theoretical and a practical perspective. As such, the session will provide a
good
outlook on the state-of-the-art in this growing field for the fuzzy systems community. The
topics
covered, include but are not limited to: </p>
<h4> Theoretical methods </h4>
<ul>
<li> Hierarchical Fuzzy Systems </li>
<li> Networked Fuzzy Systems </li>
<li>Chained Fuzzy Systems </li>
<li> Multistage Fuzzy Systems </li>
<li> Deep learning for Fuzzy Systems </li>
</ul>
<h4> Application areas </h4>
<ul>
<li> Object Classification </li>
<li> Image Recognition </li>
<li> Systems Control </li>
<li> Fault Detection </li>
<li> Decision Making </li>
</ul>
<h4> Case studies </h4>
<ul>
<li> Transport </li>
<li> Robotics </li>
<li>Business </li>
<li> Environment </li>
<li>Healthcare </li>
<li> Security </li>
<li>Energy </li>
</ul>
</div>
<div class='top-bottom'>
<div class='sub-content'>
<h2>Important Dates</h2>
<table>
<tbody>
<tr>
<td>Paper submission</td>
<td>January 31, 2022</td>
</tr>
<tr>
<td>Acceptance/rejection notification</td>
<td>April 26, 2022</td>
</tr>
<tr>
<td>Camera-ready paper submission</td>
<td>May 23, 2022</td>
</tr>
<tr>
<td>Conference Dates</td>
<td>July 18-23, 2022</td>
</tr>
</tbody>
</table>
</div>
<div class='sub-content'>
<h2>Programme Committee</h2>
<table>
<tbody>
<tr>
<td>Witold Pedrycz</td>
<td>University of Alberta, Canada</td>
</tr>
<tr>
<td>Vladik Kreinovich</td>
<td>University of Texas, USA</td>
</tr>
<tr>
<td>Scott Dick</td>
<td>University of Alberta, Canada</td>
</tr>
<tr>
<td>Derek Anderson</td>
<td>University of Missouri, USA</td>
</tr>
<tr>
<td>Uzay Kaymak</td>
<td>Eindhoven University of Technology, Netherlands</td>
</tr>
<tr>
<td>Joao Sousa</td>
<td>University of Lisbon, Portugal</td>
</tr>
<tr>
<td>Alexander Gegov</td>
<td>University of Portsmouth, UK</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
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