Data Science and Applications http://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 emre.dandil@bilecik.edu.tr (Dr. Emre Dandıl) murat.gok@yalova.edu.tr (Dr. Murat Gök) Thu, 31 Dec 2020 07:42:56 +0000 OJS 3.1.1.4 http://blogs.law.harvard.edu/tech/rss 60 Edge, Fog and Cloud Computing: Offering Strong Computing http://jdatasci.com/index.php/jdatasci/article/view/51 <p>Advances in the Internet of Things (IoT) and 5G technology require new strategies and technologies to process any data from these areas. With edge, fog and could computing technologies, it will be possible to collect data from devices that use a wide variety of protocols and produce data in many different formats, and process these data in real time. This article, by examining the difficulties in the advancement of computing types and their application areas, the requirements and solutions are presented to provide strong computing.</p> Fatih Topaloğlu ##submission.copyrightStatement## http://jdatasci.com/index.php/jdatasci/article/view/51 Thu, 31 Dec 2020 11:35:22 +0000 Malware Detection in Android OS using Machine Learning Techniques http://jdatasci.com/index.php/jdatasci/article/view/52 <p>Malware is a software that is created to distort or obstruct computer or mobile applications, gather sensitive information or execute malicious actions. These malicious activities include increasing access through personal information, stealing this valuable information from the system, spying on a user’s activity, and displaying unwanted ads. Nowadays, mobile devices have become an essential part of our times, therefore we always need active algorithms for malware detection. In this paper, supervised machine learning techniques (SMLTs): Random Forest (RF), support vector machine (SVM), <em>Naïve Bayes (NB)</em> and decision tree (ID3) are applied in the detection of malware on Android OS and their performances have been compared. These techniques rely on Java APIs as well as the permissions required by employment as features to generalize their behavior and differentiate whether it is benign or malicious. The experimentation of results proves that RF has the highest performance with an accuracy rate of 96.2%.</p> Maad M. Mijwil ##submission.copyrightStatement## http://jdatasci.com/index.php/jdatasci/article/view/52 Thu, 31 Dec 2020 07:36:46 +0000 Stock Market Value Prediction using Deep Learning http://jdatasci.com/index.php/jdatasci/article/view/42 <p class="5-Keywords"><span lang="EN-US">The stock market is a key indicator of the economic conditions of a country. Stock exchange provides a neutral ground for brokers and companies to invest. Due to high investment return, people tend to invest in stock markets rather than traditional banks. However, there is high risk is investment in stock markets due to high fluctuations in exchange rates. Therefore, developing a highly robust stock prediction system can help investors to make a better decision about investment. In this study, a deep learning-based approach is applied on the stock historical data to predict the future market value. Specifically, we used Long-Short Term Memory (LSTM) for prediction of stock value of five well known Turkish companies in the stock market. As a result of RMSE, MSE accuracy tests made using these data, it has been seen that stock market prediction can be made successfully with LSTM.</span></p> <p>&nbsp;</p> Seyda Kalyoncu, Akhtar Jamil, Enes Karataş, Jawad Rasheed, Chawki Djeddi ##submission.copyrightStatement## http://jdatasci.com/index.php/jdatasci/article/view/42 Thu, 31 Dec 2020 10:31:11 +0000 Evaluation of Customer Satisfaction about Telecom Operators in Turkey by Analyzing Sentiments of Customers through Twitter http://jdatasci.com/index.php/jdatasci/article/view/43 <p>Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral. Text analysis based sentiment analysis consolidates natural language processing models and machine learning techniques to determine sentiment scores to the entities, topics, themes and categories within a phrase or, sentence. Furthermore, customer satisfaction is an evaluation of how products and services supplied by a company satisfy or exceed customer expectation. In this work, we propose to analyze customer satisfaction of three big telecommunication operators which are Turkcell, Turk Telekom, and Vodafone in Turkey by utilizing sentiment analysis of customers of them. For this purpose, Twitter social media platform is used for the purpose of gathering the related tweets that are mentioned with hashtags by the customers of operators. In order to improve the system performance, various pre-processing models are used such as removing punctuation marks, stop-words elimination, removing tags, URLs filter, stemming. Finally, sentiment of users is evaluated through machine learning algorithms namely, random forest, support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors (k-NN), naive Bayes (NB), and decision tree. The experiment results present remarkable classification performance with accuracy of over 80 percent for all telecom operators. Thus, this study can inspire telecommunications companies to analyze customer satisfaction through the social media platform.</p> Doğukan Kündüm, Zeynep Hilal Kilimci, Mitat Uysal, Ozan Uysal ##submission.copyrightStatement## http://jdatasci.com/index.php/jdatasci/article/view/43 Thu, 31 Dec 2020 11:13:02 +0000 Red Dot Determination with 6 Axis ABB Robot Arm http://jdatasci.com/index.php/jdatasci/article/view/44 <p>The general aim of this study is to determine the positions of the red points on the surface using techniques of real-time image processing via the camera and user interface mounted on the 6-axis robot arm. The robot arm movement is performed in the simulation environment ABB RobotStudio. 2D Webcam camera is used in the red point detection process. Detection of the red point is done via the interface in MATLAB, depending on three axes. Using the current position of the robot and the image it receives from the camera, the interface detects the correct place of the red point. This process is made using the prepared mathematical formula according to the camera's position. The X, Y , and Z coordinates of the red point can be found on flat surfaces of all shapes with developed software. The goal of this study is to prepare red point detection environment based on six axes.</p> Güzin Tirkeş, Esra Nur Odabaşı, Simge Aytar, Gözde Nur Adışanlı, İbrahim Turan Bastacıoğlu ##submission.copyrightStatement## http://jdatasci.com/index.php/jdatasci/article/view/44 Thu, 31 Dec 2020 12:18:30 +0000