Data Science and Applications http://www.jdatasci.com/index.php/jdatasci <p><em><strong>Data Science and Applications (DataSCI)</strong> </em>is an international peer-reviewed (refereed) journal which publishes original and quality research articles in the field of Data Science and its applications. <em><strong>DataSCI</strong></em> is published twice per year online. The aim of the journal is to publish original scientific researches based on data analysis from both life and social sciences. <em><strong>DataSCI</strong></em> also provides a data-sharing platform that will bring together international researchers, professionals and academics. The <em><strong>DataSCI</strong> </em>magazine accepts articles written in English.</p> <p>Our journal covers all the studies based on data&nbsp; analysis from&nbsp;both&nbsp;lifeand&nbsp;social&nbsp;sciences.&nbsp;Your data-based works can also be accepted in areas not mentioned below.</p> <ul> <li class="show"><strong># scientific data mining, machine learning, and Big Data analytics</strong></li> <li class="show"><strong># scientific data management, network analysis, and knowledge discovery</strong></li> <li class="show"><strong>#&nbsp;scholarly communication and (semantic) publishing</strong></li> <li class="show"><strong>#&nbsp;research data publication, indexing, quality, and discovery</strong></li> <li class="show"><strong>#&nbsp;data wrangling, integration, and provenance of scientific data</strong></li> <li class="show"><strong>#&nbsp;trend analysis, prediction, and visualization of research topics</strong></li> <li class="show"><strong>#&nbsp;scalable computing, analysis, and learning for Data Science</strong></li> <li class="show"><strong>#&nbsp;scientific web services and executable workflows</strong></li> <li class="show"><strong>#&nbsp;scientific analytics, intelligence, and real time decision making</strong></li> <li class="show"><strong>#&nbsp;socio-technical systems</strong></li> <li class="show"><strong>#&nbsp;social impacts of Data Science</strong></li> </ul> en-US murat.gok@yalova.edu.tr (Dr. Murat Gök) emre.dandil@bilecik.edu.tr (Dr. Emre Dandıl) Mon, 15 Jul 2019 15:33:06 +0000 OJS 3.1.1.4 http://blogs.law.harvard.edu/tech/rss 60 Clustering of Countries by the Factors Affecting Levels of Development and It’s Comparison by Years http://www.jdatasci.com/index.php/jdatasci/article/view/16 <p>In the globalizing world, there are many variables that affect the development levels and economies of countries. A comprehensive analysis of these variables is crucial for the future of countries In this sense, countries are classified as underdeveloped countries, transition countries, developing countries, and developed countries etc. It is an undeniable fact that the countries classified in this way and in the same class have similar characteristics. In this study, it is aimed to reveal the economic changes of Balkan and former Soviet Union countries over the last 20 years with clustering of these countries by using the factors that affect levels of development. First, socio-economic variables which are considered to affect levels of development were taken according to years, missing data imputation methods were used for identification of missing values of the variables. Later, variables which affect levels of development are determined and with the help of these variables, similar countries are separated into clusters with cluster analysis. Same procedures are made for 1995 and 2015 years, changes of countries over the years are shown.</p> Coskun Parim, Batuhan Özkan, Erhan Çene ##submission.copyrightStatement## http://www.jdatasci.com/index.php/jdatasci/article/view/16 Mon, 15 Jul 2019 00:00:00 +0000 Development of A Wi-Fi Based Indoor Location System Using Artificial Intelligence Techniques http://www.jdatasci.com/index.php/jdatasci/article/view/17 <p>The main aim of this study is to resolve the problem of indoor positioning in closed areas, which has become a growing need nowadays, by using existing hardware solutions. Although the use of the GPS system, which requires satellite communication as an open space location solution, is very common, it cannot provide a solution for indoor. It is a well-known metric to measure signal strengths to determine distances between wireless nodes. However, the signal strength is affected by many external influences and causes erroneous measurements. With the developed approach, the transmission powers of the signals received from more than one transmitter located within a certain closed area are measured and given as an input to an artificial neural network. It has been seen that the outputs produced by the trained neural network are much more successful and reliable than the path-loss calculation.</p> Ismail Kirbas, Ayhan Dükkancı ##submission.copyrightStatement## http://www.jdatasci.com/index.php/jdatasci/article/view/17 Mon, 15 Jul 2019 00:00:00 +0000 Real-time Facial Emotion Classification Using Deep Learning http://www.jdatasci.com/index.php/jdatasci/article/view/4 <p>Facial emotion recognition has an important position in the computer vision and artificial intelligence field. In addition, real-time face recognition applications have to be able to be performed at high speed and accuracy rate in order to make human-computer interaction successful in increasing artificial intelligence and humanoid robot applications. In this study, we detected the faces on real-time video data to recognize the anger, fear, happy, surprise, sad and neutral emotions upon these detected faces using deep learning methods. We created our own dataset to use in this study for six different facial emotions. At first stage, we created a convolutional neural network and trained it over our dataset by scratching method and we achieved 50% accuracy rate. Then, we increased the number of images in our database by 3 times, and get better accuracy which is 62%. Thanks to transfer training method and AlexNet's pre-trained networks, we reached 74% accuracy rate after increasing the number of images 80% in the dataset. In addition, we achieved 72% accuracy rate when we test our network which is trained with our own dataset with the Compound Emotion dataset. The basic reason of this decrease can be angry emotion because there are differences poses between our dataset and Compound Emotion dataset for angry emotion images. However, we obtained 100% accuracy rate for happy emotion and 89% for sad emotion. It has been seen that the work we are doing gives successful results when tested with different people in different ambient and light conditions.</p> Emre Dandıl, Rıdvan Özdemir ##submission.copyrightStatement## http://www.jdatasci.com/index.php/jdatasci/article/view/4 Mon, 15 Jul 2019 00:00:00 +0000 Classification of Autism Spectrum Disorder: Deep Learning Approach http://www.jdatasci.com/index.php/jdatasci/article/view/20 <p>Abstract : Autism is a complex developmental disorder that manifests itself as life-long neuropsychiatric disorder in the first years of life, manifested by significant delays and deviations in the area of interaction and communication and restrictive interests. Autistic individuals may have problems in social skills, language development and behavior. These problems are usually communicating to other people, making friends and difficulties in doing what is said. It is estimated that beside genetic causes, environmental reasons are also effective in development of autism. Today it is certain that there is not a single factor that causes autism. Autism is a complex disorder that occurs when multiple factors come together. Nowadays, many researchers have worked on more effective solutions to these complex disorders. For this purpose, classification estimations have been made using machine learning methods on various data sets that have been used in the literature. Deep learning is an another approach that has expanded machine learning and artificial intelligence scope. Deep Learning is a special kind of machine learning. It learns the examined world in the form of hierarchical concepts that are nested, defining each concept as an association with simpler concepts. At this point, classifications become very strong and flexible. In this study, it has been analyzed the data sets of Autism Spectrum Disorder using deep learning based classification approach which is a sub-branch of machine learning. As a result of the analyzes, it has been observed that the deep learning approach in test data gives better results than the other machine learning methods.</p> Sevdanur GENC, Duygu Bağcı Daş ##submission.copyrightStatement## http://www.jdatasci.com/index.php/jdatasci/article/view/20 Mon, 15 Jul 2019 00:00:00 +0000 Logistic Location Selection with Critic-Ahp and Vikor Integrated Approach http://www.jdatasci.com/index.php/jdatasci/article/view/22 <p>Transportation costs’ directly affecting national economies; increase in transportation costs depending on energy resources have directed the countries to develop combined transportation strategies to reduce transportation costs. In this study, it is aimed to provide suggestions for the location selection of the logistics centers where wil be determined the strategies for the most economic, rapid and safe transportation with the integration of the transportation types which will contribute to the reduction of the transportation costs. The Aegean Region and The Central Anatolia Region were chosen as the pilot regions in the selection of the optimum location of the logistics centers required to develop combined transportation. The information required to select location in these two regions was obtained through a questionnaire survey and the CRITIC-AHP-VIKOR integrated method was used for the optimum location selection. While the criteria weights were determined by the CRITIC-AHP method, alternative location was chosen by VIKOR method.</p> Mehmet Akif Yerlikaya, Çağlar Tabak, Kürşat Yıldız ##submission.copyrightStatement## http://www.jdatasci.com/index.php/jdatasci/article/view/22 Mon, 15 Jul 2019 00:00:00 +0000