Data Science and Applications <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 (Dr. Murat Gök) (Dr. Emre Dandıl) Sat, 15 Jan 2022 11:14:43 +0000 OJS 60 Big Data AI System for Air Quality Prediction <p>Air Quality has been a research field for many investigators from varied disciplines in respect to global heating, climate change, health effect theories and others. Predicting air quality status is becoming more complex with time due to different air gases and other components. This paper aims at presenting machine learning models and techniques to predict air quality levels in cities providing accuracy measures to support data driven decision making in various sectors aligned with sustainable development, economic growth and social values. The research supports air quality policies formulation with a forward looking to eliminate global related consequences, save the world from the dangerous earth pollution and to close the gap in air quality index standardization with emphasis on cities sustainable development.</p> Roba Zayed, Maysam Abbod ##submission.copyrightStatement## Sat, 15 Jan 2022 00:00:00 +0000 Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks <p>This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.</p> Jose Eduardo Urrea Cabus, İsmail Hakkı Altaş ##submission.copyrightStatement## Mon, 06 Jun 2022 00:00:00 +0000 Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost <p>Effective management of warehouse processes is essential in order to maintain high-level service quality and keep the costs at optimum. Each item passes through numerous workstations during their journey in warehouses from the entrepot to the shipping area. Accurate estimation of workload at stations allows personnel assignment optimization and the increase of the warehouse performance. Otherwise, it causes personnel shortages at stations, delays in shipment commitment dates and disruptions in warehouse activities. In this paper, time series forecasting models are used to estimate the load in each workstation for a better operation. The proposed methodologies are applied to an automotive spare part warehouse in Turkey. The classical time series method, which performs best in estimating the workload of each workstation, is presented and these results are compared with the XGBoost model. Thus, the models that give the best results for each station are shown. The proposed research covers part acceptance, storage, order picking and packaging processes and their substations, which were not considered in previous studies.</p> İrem Kalafat, Mustafa Hekimoğlu, Ahmet Deniz Yücekaya, Nilay Ay, Habib Gültekin ##submission.copyrightStatement## Mon, 06 Jun 2022 15:49:48 +0000