Track: DMM - Revision history

H. Kemal İlter at 03:33, 14 September 2018

2018-09-14T03:33:45Z

H. Kemal İlter at 18:15, 13 September 2018

2018-09-13T18:15:33Z

H. Kemal İlter at 18:13, 13 September 2018

2018-09-13T18:13:07Z

H. Kemal İlter at 17:40, 3 July 2018

2018-07-03T17:40:56Z

H. Kemal İlter at 20:35, 9 June 2018

2018-06-09T20:35:28Z

H. Kemal İlter: Created page with "
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2018-06-09T19:43:26Z

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<div class="container">
<div class="content">
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<p class="title is-3">Track</p>
<p class="subtitle is-4">Data Mining and Machine/Deep Learning</p><br>
<p class="title is-3">Track Chair</p>
<p class="subtitle is-4">Dr. H. Dağ<br><span class="has-text-weight-light is-size-5">Kadir Has University</span></p>
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<p class="subtitle is-5"><strong>Language of the Track</strong></p>
<p>English</p>
<p> </p>
<p><a class="button is-link is-medium" href="http://2018.imisc.net/oc" target="_blank""><strong><span class="icon"><i class="fa fa-send"></i></span>   Submit your paper</strong></a></p>
<p><a href="/index.php?title=Conference_Tracks"><strong>List of Confirmed Track Proposals</strong></a></p>
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<section class="section tot">
<div class="container">
<div class="content">
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<p class="title is-3">Call for Papers</p>
<p><strong>Data science</strong> expresses that previous data processing applications are not sufficient to process larger and more complex data sets. The relatively recent concepts of data mining, machine learning, and deep learning offer a new set of techniques and methods. Today, researchers and companies are dealing with and experimenting with various methods of deriving value, such as machine learning, data mining, artificial intelligence, and deep learning. Data Mining and Machine / Deep Learning track aims to contribute to fields that are related to analytics of data that based on different data types. New approaches, applications, models or methods related to the topic of this track are encouraged to apply to the track.</p>
<p>Dr. Hasan Dağ, Kadir Has University</p>
<p><span class="icon"><i class="fa fa-envelope"></i></span> <a href="mailto:hasan.dag@khas.edu.tr"><strong>hasan.dag@khas.edu.tr</strong></a></p>
<br>
<p class="title is-3"><strong>Biography of the Chair</strong></p>
<p><strong>Dr. Dağ</strong> obtained his bachelor degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey and obtained both his master and PhD degrees both in University of Wisconsin-Madison in electrical and Computer Engineering. His area of interest in general is computational science, data science and smart grid. His recent research areas are Data Science, Big Data, Cyber Security, and their application to Smart Grid. He holds the directorate position of research resources, while at the same time holding the position of the head of Management Information System at Kadir Has University, Istanbul, Turkey. He has also been appointed the directorate position of Research Center for Cyber Security and Critical Infrastructures.</p>

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<p class="subtitle is-5"><strong>Key Topics</strong></p>
<p>We welcome papers related to the following topics (but not limited to):</p>
<ul>
<li>Demand forecasting</li>
<li>Process optimization</li>
<li>Predictive maintenance or condition monitoring</li>
<li>Recommendation engines</li>
<li>Market segmentation and targeting</li>
<li>Disease identification and risk stratification</li>
<li>Dynamic pricing</li>
<li>Social media-consumer feedback and interaction analysis</li>
<li>Traffic patterns and congestion management</li>
<li>Power usage analytics</li>
<li>Energy demand and supply optimization</li>
<li>Customer behavior prediction</li>
<li>Sentiment analysis</li>
<li>Convolutional neural networks</li>
<li>Recurrent neural networks</li>
<li>Recursive neural networks</li>
</ul>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Evaluation of Call Center Efficiency Using Text Mining Approach</p>
-               <p class="is-small"></p>
+               <p class="is-small">Eda Üzüm, Boğaziçi University<br>Seren Başaran, Boğaziçi University<br>Sona Mardikyan, Boğaziçi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Veri Madenciliği Üzerine Endüstriyel Bir Durum Çalışması</p>
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+               <p class="is-small">Buse Türkoğlu, Norm Civata<br>Murat Komesli, Yaşar University<br>Mehmet Süleyman Ünlütürk, Yaşar University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Balıkçılık Endüstrisinde Kullanılan Büyüme Modellerinde Geleneksel Yaklaşımlar ile Yapay Sinir Ağlarının Yaklaşımlarının Karşılaştırılması</p>
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+               <p class="is-small">Recep Benzer, National Defense University<br>Semra Benzer, Gazi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Integration of the Google Analytics tool into the data pre-processing layer for WEB Usage Mining: A case study</p>
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+               <p class="is-small">Şükrü Can Şayan, Gazi University<br>Tahsin Çetinyokuş, Gazi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Siğil Tedavisinde Kullanılan Immunotherapy Yönteminin Uygunluğunun Bayes Yöntemi ile Tespiti</p>
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+               <p class="is-small">Sümeyye Çelik, Burdur Mehmet Akif Ersoy University<br>Melike Şişeci Çeşmeli, Burdur Mehmet Akif Ersoy University<br>İhsan Pençe, Burdur Mehmet Akif Ersoy University<br>Adnan Kalkan, Burdur Mehmet Akif Ersoy University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks</p>
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+               <p class="is-small">Furkan Gürsoy, Boğaziçi University<br>Ahmet Onur Durahim, Boğaziçi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Finding a Model that Provides High Profits with Web Usage Mining: A Case Study</p>
-               <p class="is-small"></p>
+               <p class="is-small">Şükrü Can Şayan, Gazi University<br>Tahsin Çetinyokuş, Gazi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Derin Öğrenme ile Görüntü Kümeleme</p>
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+               <p class="is-small">Ömer Faruk Akmeşe, Hitit University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> K-Means vs. Fuzzy C-Means: A Comparative Analysis of Two Popular Clustering Techniques on the Featured Mobile Applications Benchmark</p>
-               <p class="is-small"></p>
+               <p class="is-small">Tuğrul Cabir Hakyemez, Sakarya University<br>Aysun Bozanta, Boğaziçi University<br>Mustafa Coşkun, Boğaziçi University</p>
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                <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> A New Approach to Development of Recommendation Systems with Opinion Mining on Turkish User Reviews</p>
-               <p class="is-small"></p>
+               <p class="is-small">Tayfun Yalçınkaya, Kadir Has University<br>Hasan Dağ, Kadir Has University</p>
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+               <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> Evaluation of Call Center Efficiency Using Text Mining Approach</p>
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+             <div class="column is-three-fifths is-offset-one-fifth">
-               <p class="title is-3">Track</p>
+               <p class="is-small is-uppercase has-text-weight-bold" style="color: #e0470d;">Track</p>
-               <p class="subtitle is-4">Data Mining and Machine/Deep Learning</p><br>
+               <p class="title is-3">Data Mining and Machine/Deep Learning</p>
-               <p class="title is-3">Track Chair</p>
+               <p class="is-small is-uppercase has-text-weight-bold" style="color: #e0470d;">Track Chair</p>
-               <p class="subtitle is-4">Dr. H. Dağ<br><span class="has-text-weight-light is-size-5">Kadir Has University</span></p>
+               <p class="subtitle is-5">Prof. Dr. Hasan Dağ, Kadir Has University</p>
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+  <div class="is-divider" data-content="Introduction"></div>
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-              <p class="subtitle is-5"><strong>Language of the Track</strong></p>
+  <div class="is-divider" data-content="Biography of the Chair"></div>
-              <p>English</p>
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-              <p>&nbsp;</p>
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-              <p><a class="button is-link is-medium" href="/index.php?title=Submission"><strong><span class="icon"><i class="fa fa-send"></i></span> &nbsp;&nbsp;Submit your paper</strong></a></p>
+               <p>Prof. Dr. Hasan Dağ obtained his bachelor degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey and obtained both his master and PhD degrees both in University of Wisconsin-Madison in electrical and Computer Engineering. His area of interest in general is computational science, data science and smart grid. His recent research areas are Data Science, Big Data, Cyber Security, and their application to Smart Grid. He holds the directorate position of research resources, while at the same time holding the position of the head of Management Information System at Kadir Has University, Istanbul, Turkey. He has also been appointed the directorate position of Research Center for Cyber Security and Critical Infrastructures.</p>
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-               <p class="title is-3">Call for Papers</p>
+               <p class="is-small has-text-grey">Unsorted</p>
-              <p>Data science expresses that previous data processing applications are not sufficient to process larger and more complex data sets. The relatively recent concepts of data mining, machine learning, and deep learning offer a new set of techniques and methods. Today, researchers and companies are dealing with and experimenting with various methods of deriving value, such as machine learning, data mining, artificial intelligence, and deep learning. <strong>Data Mining and Machine / Deep Learning</strong> track aims to contribute to fields that are related to analytics of data that based on different data types. New approaches, applications, models or methods related to the topic of this track are encouraged to apply to the track.</p>
+<!-- 17 -->
-              <p>Dr. Hasan Dağ, Kadir Has University</p>
+               <p><span class="icon" style="color: #e0470d;"><i class="fa fa-file-text-o"></i></span> </p>
-               <p><span class="icon"><i class="fa fa-envelope"></i></span> <a href="mailto:hasan.dag@khas.edu.tr"><strong>hasan.dag@khas.edu.tr</strong></a></p>
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-               <p class="title is-3"><strong>Biography of the Chair</strong></p>
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-              <p><strong>Dr. Dağ</strong> obtained his bachelor degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey and obtained both his master and PhD degrees both in University of Wisconsin-Madison in electrical and Computer Engineering. His area of interest in general is computational science, data science and smart grid. His recent research areas are Data Science, Big Data, Cyber Security, and their application to Smart Grid. He holds the directorate position of research resources, while at the same time holding the position of the head of Management Information System at Kadir Has University, Istanbul, Turkey. He has also been appointed the directorate position of Research Center for Cyber Security and Critical Infrastructures.</p>
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