SEARCH RESULT

A time series is a sequence collected at consecutive equally spaced points in time. The basic idea behind the time series forecasting is the use of a model to estimate future values based on previously observed ones. Traditionally, statistical methods are used to forecasting time series however, Machine Learning (ML) algorithms have been also proposed as alternatives to statistical methods in past decades. In this paper, we evaluate forecasting performance of different ML algorithms and statistical methods on Turkey automobile sales. Recently, various of work has claimed that traditional statistical methods dominate the ML solutions in terms of time series forecasting. This study discusses different aspects of ML and statistical methods and compare their performance on different time series.

International Data Science & Engineering Symposium
IDSES

Anıl Özdemir Furkan COŞKUN Selim BALCISOY

In this study, a nonparametric new test is proposed to test the hypothesis of equality of locations against umbrella alternatives. The Shan test for ordered alternatives is adapted to the umbrella alternatives. This test can be considered as an extension of the sign test and the Wilcoxon signed rank test. By a comprehensive simulation study, the proposed test is compared with the Mack-Wolfe and Hettmansperger and Norton tests in terms of type I error rate and power. The simulation results showed that all tests ensured the Bradley's robustness criteria for type I error rate. The power comparison results indicated that the proposed test gives better results than the other tests.

International Data Science & Engineering Symposium
IDSES

Bülent ALTUNKAYNAK Hamza GAMGAM Merve BAĞÇACI

The main objective of the present research paper is to give an exploratory vision on a new form of examining hotels’ performance of the Portuguese hotels’ market. This approach is based on the experience of a consultant in this area and is made in terms of revenue and occupancy, crossing then operational data with financial results. This analysis focuses on a recent period, since 2010 to 2017, involving the years since before and until after the big boom of tourism in Portugal. This paper exposes available data about hotels operational results and compares these results with hotels financial results. In terms of the evaluation of a hotel’s performance, the revenue per available room (RevPAR) is regarded by the tourism industry and INE (the Portuguese National Statistics Institute) as the most important measure, once it compares hotels among themselves and also compares regions. An important aspect of this study is highlighting the limitations of RevPAR.

International Data Science & Engineering Symposium
IDSES

Nuno Mendes FREITAS José António FILIPE

This research involves Big Data and its definition. Also, it covers general visualization of the problems in Turkish legal system and deal with a real case to create a methodology for solving the problems in filing an indictment process with the help of Big Data. The goal is to show that how can the finishing time of the process of filing indictments significantly reducing when Big Data technology integrated to the process. This has been done by creating a decision support system that can apply to all the cases in the phase of linking evidences and filling the indictment. This research will provide valuable information regarding the methodology of applying Big Data and address the role of Big Data in solving problems of ineffective and complex organizations.

International Data Science & Engineering Symposium
IDSES

Zeynep ÇAĞLAR

Various kinds of medical treatment methods may be used to cure common types of diseases. Experience based predictions are done to choose a treatment method among the choices of cures to get better results for the patient. This condition sometimes may continue with trying another treatment method unless a satisfactory result is reached and changing the treatment method of the cure process is not a desired course for time and health. This study presents a confident way to choose the treatment method for wart disease by using feedforward neural network. The study uses two types of datasets, one for cryotherapy and other for immunotherapy treatment methods. It was observed from the experimental results that, feedforward neural network achieved 94.4% success and 85.6% success for cryotherapy and immunotherapy datasets, respectively. The results are remarkable for both doctors and patients.

International Data Science & Engineering Symposium
IDSES

Ahmet ÇİFCİ Mehmet ŞİMŞİR

Manual categorization of applications in software repositories such as SourceForge is often time-consuming and error-prone. Automation of this process not only simplifies the daily task of administrators but also helps project owners to add their projects into the corresponding subcategory of the repository without any delay. In this study, we propose a cloudbased application categorization system that applies machine learning algorithms to support the classification of applications. The categorization system has a web-based client application to parse, process, and submit the project source code, a web service which automatically performs classification of applications into domain categories, and a cloud-computing platform which hosts the categorization service. Several multi-class classification algorithms have been adopted including, Artificial Neural Networks, Logistic Regression, Decision Jungle, and Decision Forest algorithms to validate the effectiveness of the system in multiple case studies. The case studies were performed on three public datasets generated based on 3286 Java applications of SourceForge repository. Our study shows that the highest accuracy was achieved with Artificial Neural Networks (ANN) algorithm. The resulting prediction model has been transformed into a web service and then, deployed on the Azure cloud platform.

International Data Science & Engineering Symposium
IDSES

Çağatay ÇATAL Besme ELNACCAR Özge ÇOLAKOĞLU Bedir TEKİNERDOĞAN1

Digital farming as we see it has the potential to revolutionize agriculture, and bring significant benefits for farmers and the society overall, as we need new ways to grow more food more sustainably. In this study, Sustainable Farming and Digital Age, Certification as a Model for a Sound Precision Agriculture and Impacts of Precision Agriculture and Certification Needs are described.

International Data Science & Engineering Symposium
IDSES

Massimo CANALICCHIO

It is often not easy to obtain results from complex processes multi variables. Additional techniques and methods are needed to guide. In this study, after the detecting the out of control and under control samples with Hotelling T2 control chart in a multivariate manufacturing process then machine learning algorithms was used to predict the quality of future samples. Four machine learning algorithms were trained and tested by shifts of different magnitude from the process average. The performances of the algorithms were compared according to the accuracy and error rates of the predictions and the most appropriate one was chosen as Multilayer Perceptron.

International Data Science & Engineering Symposium
IDSES

Deniz DEMİRCİOĞLU DİREN Semra BORAN Seda Hatice GÖKLER

Turkey and the world a lot of earthquakes that have occurred and will continue to come. Due to the earthquake, many people have caused their lives and damage to their shelters. Starting from a young age earthquake in Turkey to create awareness in every age group, observed and loss of life and property damage in the earthquake is one of the lowest levels to minimize the road. Therefore, it is very important to be able to use techniques and systems that can analyze a large number of data sets. The process of converting these raw data into information or meaning can be done by data mining. The aim of this study is to investigate the effects of the education given to the secondary and high school students on the students and the earthquake awareness. The research sample consisted of 14 middle school and 11 high school students, who were randomly selected in Karabük and districts. The questionnaire developed by the researcher was used as the data collection tool. In the scope of the research, 1165 students were given questionnaires before and after 80 minutes of training. In this study, the seismic awareness of the students' education on the students before and after the training was investigated by clustering analysis.

International Data Science & Engineering Symposium
IDSES

Ömer KIVRAK Filiz ERSÖZ

Risk is any possibility that affects the achievement of the intended objectives. In this study, it was investigated which provinces did not contain any risks. In the calculation of risk values of provinces; unemployment and traffic accidents were taken into consideration. Turkey Statistical Institute (TURKSTAT), the General Directorate of Security, the Social Security Institution (SSI) and Turkey Business Association (TBA) risk values obtained from benefiting from the data of the years 2013 to 2017 were calculated. Risk values are evaluated between 1 and 5. The green-colored provinces are risk-free, while the red-colored region is cons idered to be of high risk. Fine-Kinney method was used for risk analysis.

International Data Science & Engineering Symposium
IDSES

Taner ERSÖZ Betül EBESOY

Banks are one of the most important actors of the financial system. Banks can perform their services through alternative distribution channels such as branches, ATMs, internet banking and telephone banking. Banks, which are affected by economic developments and who are both financial market actors and profit-making enterprises and employing employment, provide the most efficient services through their branches. The choice of the banks' location is very important in terms of bank success. One of the factors affecting the choice of establishment location of banks is the population of the place. The closest selection criteria are the GDP per capita in the region and the activities of competing banks. It is important to choose the place of operation of the banks as well as the widespread branch network in which the customers will work. In the competitive environment, especially corporate customers work with more than one bank. It is important that the banks where they work for the enterprises that have active or widespread networks in the field have branches together in the same settlement. The aim of this study is; a total of 960 settlements with Turkey's 81 provinces, the districts and towns, bank examination for the presence of the branches of activity and which banks that operate together on the same settlement "data mining" One of the suitable ones association rules "Apriori Algorithm" and is to be determined. For this purpose, the "The Banks Association of Turkey" in the number of audits and the existence of settlements in which they operate according to their location by the banks operating in Turkey "IBM SPSS Modeler" has been analyzed with the program. According to the results obtained, the banks operating together in the same settlement area may determine the bank preferences of the financial managers.

International Data Science & Engineering Symposium
IDSES

Süleyman DÜNDAR Seliha Seçil BAYRAM

Crime analysis has importance for the detection of crime regions, the prediction of crimes before processing and the security forces to take necessary measures. By using biclustering methods to detect crime regions, simultaneous clustering of the types of crimes and regions where crime is committed to producing more comprehensive results than traditional clustering methods. In this study, CC and Xmotif algorithms of biclustering methods were applied to the real data set in order to detect the crime regions. “Crimes in Boston” data set was used in real data set application. In order to measure the efficiency of the biclusters, the performance of the algorithms was compared with Chia and Karuturi bicluster score (CCPS). The results were obtained by using Matlab functions and it was observed that results of the CC algorithm were better compared to Xmotif algorithm.

International Data Science & Engineering Symposium
IDSES

Nazan SARI Sümeyye Gizem ÇAKAR Olcay EYDEMİR İbrahim ÇİL

A container is the one of the main components of transportation systems. Allocating items into limited spaces, is a kind of combinatorial optimization problem and container loading problems is a branch of knapsack problems, in which a set of items are loaded into capacitated domains. Heuristic approaches are mostly applied to solve knapsack problems due to the problem complexity. Artificial Bee Colony Algorithm and Genetic Algorithm are successful for solving object placement issues. The first one can obtain sufficient results as well as the second one. In this study, the performance of Artificial Bee Colony Algorithm, which is applied on CLP rarely, is compared with Genetic Algorithm, which is applied on CLP widely, to see the capability of proposed ABC algorithm for further studies.

International Data Science & Engineering Symposium
IDSES

Tuğrul BAYRAKTAR Filiz ERSÖZ Cemalettin KUBAT

Value-added is an important term which indicates the efficiency level of economic activities. Valueadded term describes the difference between the value of produced goods and the total cost of production. Some areas related to value-added manufacturing are pharmaceuticals, automotive, computer and communications equipment manufacturing, aviation, etc. To develop a value-added product some inputs are needed. Some of these inputs are energy, labor force and research and development activities. The main aim of this study is to present a benchmarking analysis of 29 OECD countries in views of value-added manufacturing values. To do so, an output oriented BCC model is used to evaluate efficiency of countries and obtained results of the analysis provide some improvement ways for countries and their position against the other countries.

International Data Science & Engineering Symposium
IDSES

Billur ECER Ahmet AKTAŞ Mehmet KABAK

Long Short Term Memory (LSTM) is one of the deepest learning methods capable of learning along a chain. The method has a chain of modules able to repeating information and transferring it to the next module. Due to this feature, it is a convenient method for data sets consisting of time-dependent information such as finance. Bitcoin, using blockchain technology, has become one of the most popular cryptocurrencies today. Bitcoin data is a time series. In this study, price estimation model is proposed by using Long-Short Term Memory method for a Bitcoin price estimation for multivariate time series consisting of opening price, closing price, highest price, lowest price, Bitcoin volume, Purchasing volüme and weighted price variables. In addition, the application has been developed in Python programming language.

International Data Science & Engineering Symposium
IDSES

Ali Osman ÇIBIKDİKEN Ebru Şeyma KARAKOYUN

Bitcoin is the most popular cryptocurrency in the market. Satoshi Nakamoto has created the Bitcoin in 2009. Bitcoin movements are a widely discussed topic nowadays. On the other hand, Today machine learning is effectively deployed in an extensive variety of fields from natural language processing to image processing, from medical applications to activity recognition. In this research, we collected and used data about the market value of Bitcoin. Also, Bitcoin is briefly described in this study. Artificial neural networks and time series techniques used to estimate the market value of Bitcoin. Finally, The paper concludes with critical considerations of recent developments and some recommendations for future researches.

International Data Science & Engineering Symposium
IDSES

Derya SAĞ Ufuk CEBECİ

Human-Computer Speech is gaining momentum as a technique of computer interaction. A chatbot is a software, which can “chat" with a human user in natural language such as English. Conversational artificial intelligence technology (CAI) enables learners to engage in spoken conversations with the non-player characters. In this study, SAP's digital assistant can be used to run business processes using SAP Conversational Artificial Intelligence. The benefits of the digital assistant (chatBot) will be discussed. An example of the request for personnel’s leave request, which is one of the areas of use, will be explained.

International Data Science & Engineering Symposium
IDSES

Metehan KOCAOĞLU Kağan ÖZDEMİR Alper KARABULUT Rüştü Orkun KORKMAZ Ufuk CEBECİ

With the development of technology from past to present, the types, properties and product range of the materials used in the buildings are quite developed. Therefore, the effects of climate, environmental conditions and energy consumption cannot be ignored for selecting these materials used in the buildings. Usage of materials with the same characteristics for buildings to be built on different climate may lead to adverse effects about energy-saving and green gasses. Furthermore, the use of the same materials may not be a proper approach even in buildings with a different purpose. In this study, forecasting of energy consumption and CO2 emission is analyzed by utilizing artificial neural network structure according to different climate criteria and material characteristics for public buildings built in recent years. The Effect levels to energy consumption and CO2 emission of the building materials and the climate criteria are determined for buildings serving the same using purpose in different climate characteristics. For the study, different pilot regions where the public buildings are located are chosen according to climatic characteristics and five different building materials used in these public buildings are taken into account. When the results compare according to CO2 emission and energy consumption, it was observed that the conditions which obtain most efficient results are different

International Data Science & Engineering Symposium
IDSES

Salih HİMMETOĞLU Yılmaz DELİCE Emel KIZILKAYA AYDOĞAN

The social communities created by social media, where people have shown great interest, have led to the analysis of social network. In this sense, many techniques have been developed and these techniques have been practised in various fields. By means of the softwares, developed for investigation of complex network analysis, detailed surveys and research can be made about social media. In this study, a social network analysis has conducted on the social media. The study was carried out through the Nodexl program for two different social communities via Twitter to draw the network graphs and to compare the network analysis results by using , “#EytHepBirlikteAnkaraya and #BirlikOlFenerbahçe” According to the results obtained from the analysis, it is observed that although the EYT members are a more recent social group than Fenerbahçe supporters, the ties in their network are stronger and the network density of EYT members is four times more than in Fenerbahçe supporters. In addition, although the hashtags are addressing different topics, the value of the network characteristics such as clustering and centralization were found to be similar to each other.

International Data Science & Engineering Symposium
IDSES

Adem AKSAN Ayşe OĞUZLAR

In the modern era, enterprises are facing a variety of difficulties because of today’s emerging technologies. One of the numerous difficulties for a business is to fulfill its employees in order to adapt to the consistently changing business processes and to make progress and stay in competition. In order to build proficiency, viability, efficiency and occupation responsibility of employee, the business must fulfill the requirements of its employee by giving great working conditions. The target of this paper is to dissect the effect of workplace on employee work fulfillment. This paper may profit society by urging individuals to contribute more to their occupations and may help them in their daily work life. Consequently, it is fundamental for an association to support their employee to snap down for accomplishing the hierarchical objectives and goals. The investigation and the item are changing the advanced endeavor, expanding representative commitment over the whole workforce, including forefront, field, remote and outside laborers to enhance execution, efficiency and unwaveringness. Our stage is making ground-breaking employee encounters, where every single representative feels some portion of an option that is more noteworthy than themselves, are glad to be a piece of your image and effectively advance the positive characteristics of the association.

International Data Science & Engineering Symposium
IDSES

Batuhan Burak ERSÖZLÜ Ufuk CEBECİ Şahen TOKATLIOĞLU

Sacrifice stands for the production of goods and function, constitutes the costs of enterprises. Cost is also defined as the provision of consumed goods and functions by a production enterprise. Accuracy of enterprise activity analysis is very important in order to make appropriate decisions in enterprises. The consistency of the results ensures correct decision-making; provides right marketing and competitive advantage. Various elements are effective in the process of product costing. The items on the basis of product are examined one by one and the analysis is carried out to obtain the unit costs that reflect the reality. The aim of this study is to investigate the factors affecting the costs and to estimate the cost in the integrated system, with data mining classifying models in the process of billet production, in an A enterprise for the Iron and Steel sector. It is targeted to compare obtained estimation results with the costs presented inside the enterprise.

International Data Science & Engineering Symposium
IDSES

Gizem KAPANŞAHİN Filiz ERSÖZ

In today's urban life, most of the people's time is spent at home or work office. For this reason, design and selecting of building materials that control heating (H), ventilation (V) and air conditioning (AC) are essential. The building materials called, in short HVAC, must simultaneously provide high comfort, low cost and high energy productivity. Furthermore, HVAC must be appropriately designed to prevent adverse effects on the environment and climate. In this study, nine different HVAC systems were examined according to nine different criteria over cost, pollution, comfort and energy which are considered as four main factors in the selection of HVAC systems. Since some of the criteria for HVAC systems are described as linguistic, it is not possible to evaluate the systems with traditional methods using crisp values. Therefore, we propose generalized intuitionistic fuzzy (IF) - rough set models which are a new and flexible method. IFneighborhoods are formed by using IF-implicator and IF-t norms, and upper and lower approximations in rough set theory are calculated according to the neighborhoods. Covering-based generalized IF rough set models are generated by using the approximations and IF-TOPSIS method. According to the obtained results, we can see that the proposed method is an appropriate decision-making method which considers the uncertainties in the linguistic expressions for selecting the most suitable HVAC system.

International Data Science & Engineering Symposium
IDSES

Salih HİMMETOĞLU Emel KIZILKAYA AYDOĞAN Yılmaz DELİCE

One of the most important steps in establishing a successful business is to do accurate data analysis. The correct analysis of the data and the correct information as a result of the analysis reveal the wishes, feelings, emotions, and demands of the users. With data analysis useful information should be discovered, those who are useless should be cleaned and what should have done in the next process. Several types of data analysis methods can be done based on the data collected from Kansei survey. These analyses play an important role in the process of Kansei Engineering. There are several types of statistical analysis that are developed to use in Kansei studies such as variance analysis, linear regression analysis, flow data analysis, principal component analysis, quantification theory I, factor analysis, cluster analysis, rough set theory. The purpose of data analysis is to synthesis statistical data or Kansei words with the product properties and therefore to be applied in the design context. Kansei engineering is a method used to convert a customer’s ambiguous imagine product into detail design. Kansei Engineering starts from decision of strategy as design domain as well as target. It is collected the Kansei words related to the product domain. The word Kansei, which is used in design and other research areas. It means the feeling of beauty and pleasant emotions reflected by any object and its desire in Japanese. Kansei words form the basis of Kansei engineering. In a way, Kansei engineering is a product development method which can measure a customer’s feeling, values and affective needs and translate them into concrete product solutions. Since 1980’s Kansei Engineering has expanded greatly and become a significant discipline both in the industrial and the academic world. Furthermore, Kansei Engineering developed as an efficient research discipline, providing many innovations and market success in the industrial world. It is founded by Mitsou Nagamachi, a professor at Hiroshima University. He is considered to be the father of Kansei Engineering. The term Kansei Engineering itself was used the first time in 1986 by Yamanota, president of Mazda Automotive Corporation at Michigan University. The main aim of this study is to explain Kansei engineering and model which is rarely seen in Turkish literature and to reveal its relationship with data analysis. Besides, the future of importance of data, Big data, data analysis and Kansei Engineering will be discussed.

International Data Science & Engineering Symposium
IDSES

Mustafa Umut Öztürk Ahmet Öztürk

Nowadays, as a result of the fast advancement of technology, large and complex databases are created. Since it may be hard to process the created complex data, there is a need for software programs. The purpose of this study is to create a substructure for data mining to be used in the health sector and to give a quick and different perspective in deciding to reach the desired data. With this purpose, based on the data from the operating room of a private hospital, the data mining method was used in this study.

International Data Science & Engineering Symposium
IDSES

Duygu KARABULUT Taner ERSÖZ

In this paper, a deep learning model has been developed to detect whether malware/spyware leaks data to command and control servers and a new dataset has been obtained from real-time environment for test of the model. In addition, effect of the size of the data set and hyperparameters such as the number of layers of the deep neural network on the success rate have been investigated. In this study, real-time data for harmful and normal İnternet traffic have been obtained in the application layer and 100 features have been selected. The developed deep learning model has been applied to 16,000 sample obtained from real-time Internet traffic. From the experimental results, accuracy rates of 90% to 94% were obtained with various number of samples and various number of layers in the deep learning model. It has been seen from the experimental results that increase the number of samples increases the accuracy rate. As well as, it has been seen that as increase the number of layers in the deep neural network the accuracy rate increased first, further increase the hidden layers did not affect the success rate. In this study, more distinctive and important features have been investigated than others in the literature and the results have been tested.

International Data Science & Engineering Symposium
IDSES

Emrullah ERGİNAY M. Ali AKÇAYOL

With the rapid development of technology, competition in the markets is increasing. In the conditions of rising competition, it is inevitable for companies to make predictions for the future. The accuracy of the forecasts will also facilitate the decision-making of companies in the future. Demand forecasts are very important for every company considering these conditions. Demand forecasts play an active role in tactical and strategic decisions taken on the administrative side of companies to achieve their near or far-term goals, bringing the company's assets closer to optimal and thus ensuring the link between the targets set and the operations implemented. According to the data obtained from the firm of Antik Atölye operating in Düzce, the regression analysis method and weighted moving average method were compared. For both methods, estimation modeling was established and implemented. As a result for the Antik Atölye company operating in the wood industry regression analysis method was more effective than weighted moving average method.

International Data Science & Engineering Symposium
IDSES

Sercan ODABAŞI Taner ERSÖZ

Productivity is the relationship between the output produced by a production or service system and the input used to create this output. High efficiency is to produce more with the same amount of resources or more output with the same input. However, it is accepted that efficiency and working life quality are closely linked. The efficiency of the enterprises is important for the society and the country in which they carry out their activities as well as for themselves. While the productivity of the enterprises is reflected in the decrease in the costs, increase in the profit, the efficiency of the enterprises has reflections on the new investments, increased added value, the welfare of the society and employment in terms of society and country. It is also important to measure the efficiency that is so important in order to make it sustainable in enterprises. The productivity measured continuously in enterprises will enable managers to make correct and rational decisions, to see problems on time and to produce solutions. The purpose of this study is to determine the factors affecting the productivity of the employees in the steel industry. The study was examined under five titles. These are: “Demoraphic Information”, “Economic Factors”, “Physical and Ergonomic Factors”, “Psycho- Social Factors” and “Risk Factors”. According to the findings of the study, the effect of factors on the employees will be determined and improved findings will be presented to the management of the enterprise.

International Data Science & Engineering Symposium
IDSES

Taner ERSÖZ Filiz ERSÖZ Nurdan KALELİ Burçin ATICI

The studies related to the production industry are limited in the world and in our country. Especially in iron and steel sector, quality levels of different types of products need to be monitored. Iron and steel products obtained from the studies have prolonged their use and price and sales superiority has been achieved. At the same time, the market value of the products increases and there is a minimum loss of product. Therefore, studies in this field should be focused on. On the basis of quality, instead of debugging errors is the approach of not making mistakes. Instead of using your earnings as a philosophy, we should adopt an understanding of gaining from our losses. Understanding the importance of quality work and improvements, the primary purpose of enterprises is to support quality production by preventing or reducing errors in production. Data mining has started to be used effectively in enterprises. Data mining involves the process of selecting, organizing and modeling the most necessary data for business executives. At this point, it is possible to define data mining as a set of techniques and concepts that produce new information for decision-making processes. In this study, firstly the VM process is defined and then the VM studies which are selected from the literature covering 2010-2018 and applied to certain quality improvement problems in the manufacturing sector are evaluated. The definition of process and product quality, estimation of quality, classification of quality and optimization of quality parameters are discussed. In addition, the application of decision trees, one of the most widely used and effective VM techniques, in order to determine the variables and levels that cause production errors in an industrial organization is also included.

International Data Science & Engineering Symposium
IDSES

İsmail Burak AKINCI Filiz ERSÖZ

In general, it is seen that the fire data is presented as the amount of the area burned and the number of fires in time. Flammable material loads in forests, behavior patterns and models of flammable materials according to climatic conditions etc. works are carried out rapidly. All these studies aim to manage the process after the fire. There is a need to work on measures to be taken in order to prevent the occurrence of fire. For this purpose; socioeconomic factors that cause fires should be determined in regions where forest fire is common. The elimination of these factors will minimize the occurrence of forest fires. In Turkey; considering that 89% of the forest fires are caused by human beings, the importance of socioeconomic studies in these regions is increasing. In the studies to determine the socio-economic factors that cause forest fires; In some period, some studies such as multiple regression, correlation, factor analysis etc. were conducted among some socio-economic data determined according to the conditions of the region and / or fire numbers in the given period. In all these studies a time section / part was taken into consideration. Materials and Methods; First time in Turkey with this work; the relationship between the number of forest areas and forest fire counts and the socio-economic variables determined by considering a certain period of time was analyzed together with time and space. In the forest fires in the years 1980-1990-2000 in Antalya Forest Regional Directorate for twelve governmental forest enterprises, the relationship between the number of forest areas and fire numbers and 25 socio-economic variables were determined. These data were analyzed by panel data analysis or Time Series Cross Section Regression (TSCSREG) analysis method. Variables with no effect in analysis and at the same time the variables / criteria that were derived from each other were taken into consideration and these criteria were eliminated by multiple linkage analysis (multiple linear analysis, multiple collinearity analysis) and reduced to 12 variables. Analysis of burned forest areas and selected socio-economic variables; It was tested by Fuller and Battese Methods in the scope of TSCSREG Analysis. Conclusion: In the analysis of the amount of burned forest areas and selected socio-economic variables; A strong relationship between forest fires and selected socio-economic variables shows that the value of R2 is 90.18%. The value of R2 is 70.19%. It shows that there is a relationship between the numbers of fires and the socio-economic variables selected.

International Data Science & Engineering Symposium
IDSES

Ufuk COŞGUN

Tourism in the natural world has an important place. Tourism revenues have a significant share in the development and development of countries. The economic benefit to be achieved; it is possible to maximize the natural, historical and cultural beauties. In this study, which was conducted to determine the satisfaction of tourists, survey was applied to 169 tourists. As a result of analysis; It was concluded that variables such as age, gender, nationality and income did not affect tourist satisfaction, variables related to purchased products, shops and sellers had a significant relationship between tourist satisfaction.

International Data Science & Engineering Symposium
IDSES

Fatma ATEŞ Nuray TÜRKER Filiz ERSÖZ

Job evaluation; In order to create input for performance evaluation and wage management in businesses, the skills of job, responsibility, job conditions etc. and formally and systematically. Business valuation deals with the importance of the work and the added value it provides to the business rather than the employee. This study was carried out in the iron and steel industry. Firstly, two-way comparison matrices were formed by Analytical Hierarchy Process which is one of the Multi Criteria Decision Making (MCDM) methods for the main factors and sub-criteria to be used when conducting business evaluation. Then, the paired comparison matrices formed by Fuzzy Analytic Hierarchy Process were reconstructed with triangular fuzzy numbers and the weights of main factors and sub-criteria were calculated by Chang Ground Analysis method.

International Data Science & Engineering Symposium
IDSES

Ataberk OLCAY Muharrem DÜGENCİ Mümtaz İPEK

Many indicators are used in the analysis of the financial status of companies. Indicators are usually presented quarterly. However, some of the financial indicators of some companies are not given data. In this study, it is examined whether there will be other indicators that Show the same character instead of the data which is not present when the companies are evaluated as a whole. A hierarchical clustering approach was used to evaluate whether the financial indicators of 10 randomly selected companies traded on the NASDAQ stock exchange to have similar characteristics using distance methods. According to the results obtained, indicators with similar characteristics are listed.

International Data Science & Engineering Symposium
IDSES

Ebru Şeyma KARAKOYUN

Bu çalışmada akademisyenlerin yayınları kullanılarak yayından yazara birliktelik analizi yapılmış ve akademik çalışma grupları oluşturulmaya çalışılmıştır. Yayınların başlık, anahtar kelime, özetlerinde geçen kelimeler morfolojik olarak köklerine ayrılmış, metinsel temizleme yapılmış ve Empati-Çıkar yöntemi kullanılarak benzerlik/yakınlık katsayıları hesaplanmıştır. Oluşan benzerlik/yakınlık matrisi kullanılarak yayında katkısı bulunan yazarlar yakınlıklarına göre çalışma gruplarına dahil edilmeye çalışılmıştır.

International Data Science & Engineering Symposium
IDSES

Ömer Faruk ACAR Burhan Selcuk

The wastewater treatment in the textile industry is of special importance due to the intensive use of chemicals and dyes. However, wastewater treatment plants have impacts on environmental and these environmental impacts should also be evaluated and minimized. Neutralization is one of the processes with the huge chemical consumption in the wastewater treatment plant. Therefore, the chemical alternatives used in the neutralization process should be compared in terms of their environmental impacts. In this study, the performances of carbon dioxide and sulfuric acid as two alternative chemicals used in the neutralization process applied in a textile factory wastewater treatment plant are compared using the life cycle approach. The neutralization process using carbon dioxide yielded better results in the categories of abiotic depletion, fossil fuels, ozone layer depletion (ODP), fresh aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, photochemical oxidation, acidification, and eutrophication.

International Data Science & Engineering Symposium
IDSES

Fatma Şener FİDAN Emel KIZILKAYA AYDOĞAN Niğmet UZAL

Blood is a vital product that is needed by thousands of people every day due to diseases, surgeries or injuries. Blood banks should accurately determine the amount of blood they should have in their stock to meet blood needs. Therefore, having less blood than necessary in hospitals creates important problems such as not meet need for blood and loss of life. On the other hand, storing large amounts of blood causes deteriorating the blood and causes stock out in other hospitals. The aim of this study is to determine the criteria affecting blood demand and to forecast the blood demand by the machine learning algorithm Adaptive Network Based Fuzzy Inference System (ANFIS) method. However, since the number of impact criteria is high, principal component analysis (PCA) method has been used in order to decrease criteria and eliminate the dependencies between the criteria. The developed hybrid method was applied in a regional blood center.

International Data Science & Engineering Symposium
IDSES

Seda Hatice GÖKLER Semra BORAN

Industry 4.0 was first announced to the public by the German Federal Government at the 2011 Hannover Fair. The concept of Industry 4.0 is taken into consideration especially in our country, considering the rapidly developing countries. In this study the work of leading organizations in Turkey is being examined and the approaches to Turkey’s Industry 4.0 are reported. In the study, SWOT Analyzes are compiled from the results of civil society organizations reports, academic publications, public institutions report, consulting companies and related books. As a result of this review, a single SWOT analysis consisting of 9 items in each item is presented with the text mining method. The SWOT Analysis obtained is linearly scored between 1 and 9 points given from the sources using the R'WOT Analysis method. According to the results of the R'WOT Analysis, our country's approach to Industry 4.0 is considered as an opportunity of 31%.

International Data Science & Engineering Symposium
IDSES

Gizem ACAR İlker ÖNALAN

This article sheds light on how games applications increased its popularity using social network platform such as Facebook. What is more, this piece of writing reflects rapid evolution of particular games such as “Farmville”, “Pet society”, which using Facebook API. In addition, the article provides information what technologies, policies, protection measures; Facebook takes to protect users’ personal information, “OAuth 2.0 protocol”, in particular. Additionally, the article provides information concerning benefits Facebook and its users get from using particular games apps, challenges they are facing. Finally, the article gives some recommendations how Facebook and its followers can cope with these challenges.

International Data Science & Engineering Symposium
IDSES

Babaev Vladimir Yandashevich

A popular risk measure, Conditional Value-at-Risk (CVaR), is called Expected Shortfall (ES) in financial applications. The paper developed algorithms for implementation of linear regression for estimating CVaR as a function of some factors. Such regression is called CVaR (Superquantile) regression. The main statement of the paper: CVaR linear regression can be reduced to minimizing the Rockafellar Error function with linear programming. The theoretical basis for the analysis is established with the Quadrangle Theory of risk functions. We derived relationships between elements of CVaR Quadrangle and Mixed-Quantile Quadrangle for discrete distributions with equally probable atoms. The Deviation in CVaR Quadrangle is an integral. We presented two equivalent variants of discretization of this integral, which resulted in two sets of parameters for the Mixed-Quantile Quadrangle. For the first set of parameters, the minimization of Error from CVaR Quadrangle is equivalent to the minimization of Rockafellar Error from the Mixed-Quantile Quadrangle. Alternatively, a two-stage procedure based on Decomposition Theorem can be used for CVaR linear regression with both sets of parameters. This procedure is valid because the Deviation in the Mixed-Quantile Quadrangle (called Mixed CVaR Deviation) coincides with the Deviation in CVaR Quadrangle for the both sets of parameters. We illustrated theoretical results with a case study demonstrating the numerical efficiency of the suggested approach. The case study codes, data and results are posted at the website. The case study was done with the Portfolio Safeguard (PSG) optimization package which has precoded Risk, Deviation, and Error functions for the considered Quadrangles.

International Data Science & Engineering Symposium
IDSES

Stan Uryasev

Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today; inflation, which is tried to be kept under control by the central banks, is trying to ensure price stability, the continuous price changes that arise in all the goods or services that consumers use includes. Undoubtedly in terms of economy, inflation expectations are also ganing importance, except for rhe realized inflation. This situation makes it necessary to predict the future vaules of inflation. In that case, a reliable estimate of the future values of inflation in any country will create an entry in determining the policies that decisionmaker units will implement on the economy. The aim of this article is to predict inflation in the next period by using the Consumer Price Index (CPI) data with two alternative techniques. It is also aimed to examine the prediction performances of these two techniques in comparisons. Thus, the first of the two main objectives of the study is to predict the future values of inflation with two alternative techniques. The second goal is to determine which of these two techniques well compared to statistical and econometric criteria. In this context, the estimated performance of both techniques was predicted by the 9-month inflation, Box-Jenkins (ARIMA) and Artificial Neural Networks (ANN) in the April – December 2019 period, using CPI data consisting of 207 in the period of January 2002 – March 2019. In the study, Eviews and Matlab programs were utilized.

International Data Science & Engineering Symposium
IDSES

Erkan IŞIĞIÇOK Ramazan Öz Savaş Tarkun

Acceptance sampling plans are a decision-making process on the basis of a randomly selected sampling from a party, where it is not possible to completely scan the products for reasons such as time and cost being limited or the formation of damaged products during the inspection. For some products, the life span (time from beginning to failure) may be an important quality characteristic. In this case, the quality control adequacy of the products can be checked with an acceptance sampling plan based on the truncated life test with a censored scheme for the lifetime of the products. Acceptance sampling plans based on life test of product life in industry are called reliability plans. In this study, group acceptance sampling plans based on life tests were studied under the type-I censored scheme for the compound Weibull- Exponential distribution. Optimum sample size, optimum number of groups and acceptance number were obtained.

International Data Science & Engineering Symposium
IDSES

Canan HAMURKAROĞLU Ayten YİĞİTER

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning; can be considered as the intersection of statistics, mathematics, and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. Data science education has to be more project-based since up to now, there is no core knowledge of data science like other sciences. It is probably the hypercorrect choice to learn this job in a university which is intertwined with industry and provides plenty of opportunity for internships. In this article, we will summarize the data science education developments and give curriculum examples from the world at the undergraduate and graduate level. Regarding these examples, every university thinks data science as he wants and the names and the contents of these programs really differs.

International Data Science & Engineering Symposium
IDSES

Necmi GÜRSAKAL Fırat Melih Yılmaz Ecem Özkan Deniz Oktay

Due to the new trends in global warming, environmental consciousness has become a greater concern among the organizations and industries globally. Green Supply Chain Management (GSCM) has received an increased attention from academia and industries in last few years. Green Supply Chain Management (GSCM) has emerged as an innovative organizational strategy to reduce environmental impacts of supply chain activities as well as efficient usage of energy and material. In today’s developing economies, customers are becoming more conscious of the environment and governments are making stringent environmental laws, so the industries need to reduce the environmental impact of their supply chain activities. There are many barriers which directly and indirectly affect the implementation of GSCM in an organization. In this paper twenty barriers have been identified through extensive literature review and expert opinion of academicians. These barriers are found to exist in all organizations irrespective of industry domain. Due to the presence of various barriers, Indian organizations are struggling to implement GSCM in their operations. By removing the barriers, Indian industries can focus on cleaner production by adopting Green Supply Chain Management (GSCM) in their operations. The Objective of the study was to identify the GSCM barriers in Indian context. The research methodology was perusing literature in GSCM and validating by experts opinion. Literature was perused irrespective of industry domain. The study concludes by narrowing on twenty barriers which play a prominent role in the Indian context.

International Data Science & Engineering Symposium
IDSES

Mohd. Azmi Khan Salma Ahmed Sadia Samar Ali

With the increase in computational power and big data, studies on artificial intelligence are increasing day by day. Especially deep learning applications are seen in almost all areas of our lives. The most successful results of deep learning architectures are in image processing. Different architectural approaches are tried to make image processing fast. Due to the fact that video images consist of large capacity data, it is very important to achieve high performance in these video images. In this study, size reduction function has been proposed that can reduce the size of the high-quality and large-capacity file data and produce results with a high accuracy rate. The results of the proposed method were compared in terms of performance and speed with different architectures in image processing using CNN (Convolutional Neural Network) algorithm. In addition, an application that uses the recommended size reduction function has also been developed using the Python programming language.

International Data Science & Engineering Symposium
IDSES

Ömer PİŞGİN Ali Osman ÇIBIKDİKEN

processes in the business world have two fundamental disease, including deviation and variability from the average (target). One of the statistical process control graphs used for quantitative variables is to keep the average and the other to control variability. Apart from the normal distribution or nonnormal of quantitative data, the average and variability are controlled or not, and then the capability of process or machine is checked. The desired outcome is in addition to the normal distribution of data, the process is under control and capable. On the other hand, capability analysis is defined as the machine capability analysis when it is performed for the machine, while the process capability analysis takes its name when it is done for the process. In this study, machine capability analysis has been applied. The aim of this article is to investigate the effects of the normal distribution or nonnormal of data on machine capability analysis. For this purpose, data on the lengths measured by surface of the shock absorber body pipe cut by a CNC machine in a company in the automative industry were used. In the study, 50 observations values were used, and the lower specification limit was 124.5 and upper specification limit was 125.5, and the CNC (pipe cutting) machine was sufficient or not. The analysis first started with the implementation of the normality test and the data was not distributed normally. It is concluded that assuming this data, which does not have normal distribution, is normally distributed, and the machine is under control and is also sufficient with the I-MR control charts in the Minitab program. The same analysis was applied with the nonnormal command under the assumption that the data was nor normally distributed, and even in this case the machine was sufficient. In addition, the data that does not have normal distribution has been transformed into normality, and I-MR control charts and machine are under control and also sufficient. According to the findings, the average and specification limits of the values of I-MR control charts are the same and the machine capability results are different. In this study, these similarities and differences were examined comparatively. Let us add it right away; these findings are specific to the machine and cut pipes we take into consideration and should not be generalized.

International Data Science & Engineering Symposium
IDSES

Erkan IŞIĞIÇOK Gözde TÜRK

Teknolojinin ilerlemesi ile birlikte birçok veri dijital ortamlarda kayıt altına alınarak büyük veri yığınları ortaya çıkmıştır. Veri madenciliği sayesinde bu büyük veri yığınlarının içinden anlamlı ve yararlı bilgilerin ortaya çıkarılması için çalışmalar yapılmaktadır. Özellikle büyük veri yığınlarını analiz etmede klasik analiz yöntemlerinin yetersiz kalması veri madenciliği yöntemlerinin önemini arttırmıştır. Her dönemde olduğu gibi günümüzün en önemli araştırma alanı olan tıp alanında da sürekli olarak hastalara ait veriler artarak kayıt altına alınmaktadır. Kayıt altına alınan veriler bazen tek başına anlamsız gibi görünürken diğer verilerle birlikte bütünsel olarak analiz edildiğinde gizli kalmış önemli bilgiler elde edilebilmektedir. Bu değerli bilgiler, sağlık sektörünün gelişmesine ve doktorların daha doğru bir şekilde teşhis verebilmesine yardımcı olmaktadır. Bu çalışmada, Kronik Böbrek Hastalığı (KBH) veri seti üzerinde analiz yapılmıştır. Farklı modeller oluşturularak, bu modellerin veri üzerindeki tahmin sonuçları karşılaştırılmış ve bu sonuçlara bağlı olarak veriler üzerinde hangi modelin daha iyi sonuç verdiği belirtilmiştir.

International Data Science & Engineering Symposium
IDSES

Mustafa İlker ERDURSUN Hasan ERBAY Ömer Faruk AKMEŞE İbrahim DOĞAN

Rapidly developing technology in the world endangers many companies we are in today. The companies are now obliged to improve themselves in order to compete in both domestic and international markets. In this sense, concepts such as quality, cost and customer satisfaction are becoming more important. Lean production is a production method that is not outdated at this point. In the 1950s, the lean production which was laid the foundation with the Toyota Production Company, is still up to date. Lean production, which adopts zero error and zero inventory principle, is applied in both the manufacturing sector and the service sector. Today, it is still very important in terms of the continuity of lean production which is developed and integrated with new technologies. Lean production is a gateway to the world from the Japanese people. In this thesis,” Lean Manufacturing Application in Iron-Steel Production Line Hatt is tried to be explained. Lean production and techniques are examined in detail. Then, in light of this information, the results obtained in iron and steel industry were interpreted.

International Data Science & Engineering Symposium
IDSES

Taner ERSÖZ Filiz ERSÖZ Karanfil SARIZ

LiBerated Social Entrepreneurship in Developing and Emerging Countries consists of a social entrepreneur using business metrics, to sustain social impact. We study differences between developing and developed countries, introducing a new OR approach to development. Commercial entrepreneurs are generally oriented to business metrics like profit, revenues and return. Instead, social entrepreneurs are non-profits or a blend with for-profit goals, generating Return to Society. In DCs, a social entrepreneurship has been uncommon. We introduce a midway as LiBerated Social Entrepreneur, where social businesses should be sustainable. We apply Game and Max-Flow - Min-Cut Theories, Schumpeter’s creative destruction and Adam Smith’s diversification model for our business plan. As a result, B. Kjamili started Migport, formerly QZenobia: a mobile application that runs as a “refugee portal”, supported by “Refugee Big-Data Analytics”: refugees submit data to the application via “questionnaire” and search for opportunities, verified news privatized based on their answers. The idea of both-sided help with benefit generated by D. Czerkawski is an extension of B. Kjamili's conception. Nshareplatform (NSP) will create a friendly public space for people with disabilities, understanding they needs. It tries to facilitate better communication between “two worlds”- Ability and Disability and personalizes an assistant (Special person helping people with disabilities). Multivariate Adaptive Regression Splines (MARS), Conic MARS (CMARS) and its robust version RCMARS have shown their potential for Big-Data and, recently, Small-Data. With that toolbox, we aim to further support our joint and novel project.

International Data Science & Engineering Symposium
IDSES

Gerhard-Wilhelm WEBER Berat KJAMILI Dominik CZERKAWSKI

The map method works with a certain algorithm and the inputs which send to a value list as a parameter. All values in the list converted to the intermediate result list. We sort all of the data then obtain the map list. The proposed and developed map structure was tested with quicksort approach. The sorting process depends on the byte situation of the each data. Small data can do it easily side by side. Thus, small data do not need to applying of the reduce process. Sample selection havbe to will be easier. The goal is to give an intermediate operationtothe map-reduce structure. The more accurate is to get a ranking. In large data analysis, the data mapping sequence and reduction works with a certain algorithm structure, introducing and sending inputs as a parameter to a value list. An intermediate result list is created by converting all the values in the list, which are included in the entered system. In the structure developed after the mapping (Map) process, the mapping list is divided and obtained. The order depends on the byte value that is generated by each data. In case of large volume data, the data will be used without using a single line operation. In short, the data may be side-by-side, so there is no need to apply the reduction to each data. Therefore, the process of selecting the sample will be easier.

International Data Science & Engineering Symposium
IDSES

Safiye TURGAY Suat ERDOĞAN

At the moment we are entering a new era of knowledge, we are experiencing the first stages of a digital transformation. Businesses should be restructured in a structure suitable for the digital age when they are moving towards Industry 4.0, which is rising on cyber physical systems. In this period when the digital enterprises are rising, each business unit of the enterprise should be in the effort of using these innovations in accordance with this structure and using them in the most effective way to reach their goals. With the understanding of marketing in the modern sense, one of the most basic functions of marketing is to provide the information about the customer demands and expectations in order to be used in the product design and planning stage. For this reason, marketing is not only about the sales and marketing of the products or services produced, but also for the decision of what to produce. In this period, where the effects of the digital age are becoming more evident, the weight of digital applications in marketing is increasing and the concept of Digital Marketing is becoming more prominent. Innovations on the transformation of Data (which is called New oil) into information, give enterprises more competitive advantages and help to make more accurate decisions. In parallel with the developments in technology, the change in the market and the business environment necessitates the marketing to be prepared for these changes and to transform itself with this change. In this study, the analytical concept is discussed with marketing. In addition, the relationship between the analytical concepts of marketing is tried to be explained and the opportunities awaiting marketing are put forward. At the end of the study, it has been concluded that this digital transformation and developments all over the world increase the importance of marketing.

International Data Science & Engineering Symposium
IDSES

Muhammet GİRGİN

Support vector machine is an effective machine learning method based on statistical learning theory and used for classification problems. The optimization of the parameter is very important in order to increase the classification accuracy. Meta-heuristic methods are one of the main optimization approaches that can be applied in this context and have been used frequently for parameter optimization in recent years. These methods are generally particle swarm optimization, genetic algorithm, grid search method, differential evolution algorithm, ant colony optimization. In this study, support vector machine parameter optimization studies between 2010- 2019 were investigated. According to the results of these studies, it was observed that parameter optimization through meta-heuristic methods significantly increased the rate of classification accuracy of classifier and significantly reduced the workload.

International Data Science & Engineering Symposium
IDSES

Zübeyir ÖZKORUCU Turgut ÖZSEVEN

The revolutionary changes of the last decade in the banking sector have led to the integration of information and computer networks into a single information and cybernetic space, which has led to the creation of automated banking systems that have substantially expanded the spectrum of electronic services of state and commercial banks of the world. As a result, threats to such a national information resource of the state as the banking information resources under which the banking information refers. Threats to the security of banking information resources have become signs of hybridization. Manifestations of hybridity as a result of the simultaneous impact of threats to information security, cybernetic security and information security on banking information resources have led to the emergence of synergies, the negative manifestations of which require a radical revision of the concepts of the construction of existing security systems. Thus, it becomes clear that there is a need for a radical revision of the current methodological principles for building a security system for banking information resources both for Ukraine and for the world as a whole.

International Data Science & Engineering Symposium
IDSES

Serhii Yevseiev

Multi-objective optimization problems allow multiple purpose to be simultaneously optimized. The nondominated sorting genetic algorithm II (NSGA-II), which is one of the most effective multi-objective heuristic methods in the solution of multi-objective optimization problems, is widely used in the literature. NSGA-II obtains a Pareto optimal solutions, known as a set of dominant solutions without requiring any prior knowledge in one run. The NSGA-II is more useful than the classical genetic algorithm, minimizing the computational complexity by calculating the fast dominated sorting approach and the crowded distance without having to repeat for each solution. In this study, NSGA-II method was used to optimize the cutting parameters of hard materials turning. In the experimental studies, the regression models based on the cutting velocity, feed rate and depth of cut parameters represent three different objective functions. This optimization problem, which has five objective functions with three variables, has been discussed by NSGA-II method. The optimal solution of these functions is to use the NSGA-II method to find the most suitable set of Pareto solutions. The solutions obtained by using NSGAII method have been found to be successful in multi-objective optimization problems. In addition, decision makers from the optimal solutions can choose the most suitable solution according to their importance in the objective functions.

International Data Science & Engineering Symposium
IDSES

Ahmet KOCATÜRK Bülent ALTUNKAYNAK

Brass alloys are characterized by excellent workability, high thermal and electric conductivity, corrosion resistance as well as exceptional antibacterial properties and are therefore widely used in various industries, such as electric and electronics, automotive and sanitary industry. However, power consumption should be eliminated for cleaner production in terms of sustainable machining. Therefore, this study aims modelling of cutting forces in hard turning of C23000 brass based on finite element method. The cutting parameters are chosen as cutting speed, depth of cut and feed rate with three levels. The average of 4.66% difference is achieved between experimental and simulated feed forces while 4.39% difference for main cutting forces. The finite element modelling of cutting forces is quite compatible with the experimental results and it can be performed by high accuracy without excessive machining experiments of high machinability materials.

International Data Science & Engineering Symposium
IDSES

Mehmet Erdi KORKMAZ

Ball and beam system (BBS) is a benchmark hardware for designing control action. The structure of the system is based on changing the angle of the beam so that the position of the ball is changed. It is desired to move the ball to a reference position. In this paper Fuzzy Logic Controller (FLC) is applied for this problem. Instead of conventional FLC, the derivative and integral terms are integrated to the FLC, which is called as PID-like FLC. This controller has a constant Fuzzy structure with variable parameters. The performance of the controller is based on these parameters. Therefore, in this study, the parameters of PID-like FLC are optimized by using three optimization algorithms; Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution. The performance of the controller is demonstrated on both simulation and hardware environment. The performance of the optimization algorithm with respect to the obtained performances are compared in this paper.

International Data Science & Engineering Symposium
IDSES

O. Tolga ALTINÖZ A. Egemen YILMAZ

The possibilistic mean – variance (MV) model enables the practitioners to incorporate the expert knowledge and robust statistics into the portfolio selection. Hence, it is a considerable alternative in decision making under uncertainty. In this study, we will examine the possibilistic mean – variance model theoretically under the assumption that the possibility distributions of the asset returns are given with the triangular fuzzy numbers. Here, the triangular fuzzy numbers will be determined based on the box plots. According to this, the possibilistic mean depends on the data set’s median, interquartile range and skew. Furthermore, the possibilistic variance depends only on the data set’s interquartile range. Then, we will illustrate this model based on the weekly returns of ten sector indices in 2017. Moreover, we will compare the risk adjusted performance and profitability of the possibilistic MV model and Markowitz’s traditional MV model where the trading and testing periods cover the complete year of 2017 and 2018 respectively.

International Data Science & Engineering Symposium
IDSES

Furkan GÖKTAŞ Süleyman DÜNDAR

Denim is a popular fabric among all of the age groups because of its good usage performance and ability to provide convenience in adapting to changing trends in fashion. Apart from the fashion and general performance properties, thermo-physiological comfort properties such as air permeability are important for denim users. Fabric comfort depends on lots of factor such as fabric structure and the types of fibers. Air permeability is a one of the comfort properties of fabric is affected by many parameters of the fabric. A determination of the relationship between the fabric parameters and the air permeability is highly complex and difficult. For this reason, Artificial Neural Network model which has effective performance in very complex problems was used. In the present study, an artificial neural network has been used to predict air permeability amongst different denim production parameters. Finally, by comparison with the experimental results, the efficacy of the proposed model was verified.

International Data Science & Engineering Symposium
IDSES

Esra AKGÜL Emel AYDOĞAN Yılmaz DELİCE

Drilling operation is one of the most frequently used process in manufacturing industry. Especially in machine manufacturing sector, drilling operations take one third of the total production time. Drilling operation cost a lot, not only because of taking some serious time of total production time, but also because of the drill usage for this process. In this study, the cost comparison of three different methods evaluated for drilling operation in CNC machines. These methods are drilling cycle (G81), high-speed peck drilling cycle (G73) and deep hole peck drilling cycle (G83). The experiments were performed according to the cutting parameters suggested by the cutting tool company and the machining times measured in these three different methods. A novel program coded on Microsoft Visual Studio 2017 C#, which is able to calculate from machine amortization to workmanship, the whole process cost. Process costs can be calculated according to the number of holes in these different methods, through this program. Furthermore, drilling operation costs can be calculated for different cutting parameters too.

International Data Science & Engineering Symposium
IDSES

Tugay ÜSTÜN Yakup TURGUT

Over the last decades, marketing scholars have often drawn attention to the value of customers for businesses that aim to endure in a harsh competitive environment. Customer Relationship Management (CRM) has been a prominent approach in marketing management that aims to improve relationships with customers. A practical implication of the CRM approach is the analysis of customer data to extract value for businesses, as well as customers. Segmentation has been a useful task that helps to group customers with similar attributes and designate better-tailored marketing strategies for customer groups. Among a variety of approaches for customer segmentation, Recency Frequency Monetary (RFM) Model stands out as an easy-to-adopt and effective technique. In this study, segmentation with RFM approach will be conducted over the purchase records obtained from an e-retailer. The segments and relevant marketing strategies will be presented in the findings. Moreover, a software implementation for the RFM model will be introduced along with a case study.

International Data Science & Engineering Symposium
IDSES

İnanç KABASAKAL

Nowadays, with the developing technology, the amount of data that is owned and processed is increasing day by day. It is very important to ensure the security of data, which is one of the biggest assets for institutions and organizations. With traditional security methods, attacks can be detected and prevented, but cybercriminals spend a lot of time and resources on advanced and targeted attacks that can bypass these methods. The present methods are reactive because they are generally updated with the information obtained from the analyzes performed after a successful attack. More proactive approaches are needed to improve safety. Cyber threat intelligence represents such a proactive approach and involves collecting and analyzing information for potential threats from a wide variety of data sources. The purpose of cyber-threat intelligence is to proactively adapt security controls to understand the methodology used by different attackers and to detect and prevent such activities. In the world of technology, the defense against attacks is one of the most important issues. Today, different approaches and effective methods have been used to obtain intelligence. These include vital information about security threats, which are used by hacker forums and other platforms as a means of communication between hackers. The amount of data on such platforms is very large. The manual analysis of these data is time-consuming, ineffective and requires a considerable amount of resources. In this sense, machine learning has become one of the popular approaches used in the field of cyber-threat intelligence in terms of its suitability to the subject, producing beneficial and effective results. In this study, information is given about cyber threat intelligence and in the world of hackers, how to obtain intelligence by using machine learning techniques is examined and evaluated in detail by supporting the studies conducted in the literature.

International Data Science & Engineering Symposium
IDSES

Cemile SARICAOĞLU Mehmet Demirci

Smart agriculture is the correct and economical use of resources, product control, and production to be more ergonomic in order to increase production efficiency in agriculture. The concept of internet of things that are widely used in conjunction with Industry 4.0 has started to be applied in agriculture. The main objective is to apply the automation systems that provide communication among themselves to agriculture in order to increase production efficiency. In this study, the current state of the smart agricultural systems with IoT has been investigated. As a result, the risks that may occur in production can be predicted in a short time with smart agriculture. Thus, proper and wasteful use of the resources required for agriculture is foreseen.

International Data Science & Engineering Symposium
IDSES

Mervenur Sağlam Turgut ÖZSEVEN

Increasing the flexibility and effecient of enterprises from procurement to sales provides a great competitive advantage for meeting consumer demands. Providing competitive advantage is possible through the effective implementation of innovative technologies of fourth industrial revolution in the all stages of supply chain process. In this context, the technologies related to industry 4.0 were mentioned in the study and the differences between the traditional supply chain and the digital supply chain were determined. In addition, the industry 4.0 applications in the digital and green supply chain are mentioned and the steps that must be followed in the process of transition to the digital supply chain are indicated.

International Data Science & Engineering Symposium
IDSES

Deniz MERDİN Filiz ERSÖZ

Abstract: In this study, a buffer stock between two machines which are working at the same speed, is considered. It is assumed that the stock level between two machines alters in the interval 0 , 2α. In the case that only the first machine is broken, the stock level can decrease to zero if repairing time of the first machine is extended. This causes the second machine is required to be halted. After fixing the first machine, the system begins to work again. If the second machine is broken and the first machine is working, then the process proceeds until the stock level reaches the maximum level 2α and then the first machine will be halt compulsory. In order to re-work the system, the repairing of the second machine must be completed. Under these assumptions, the buffer stock level will be stochastically fluctuated in the interval 0 , 2α. Thus, the buffer stock between two machines can be expressed by a stochastic process Y t and it is observed that this process is a random walk with two barriers. Some problems of queuing theory, stock control, reliability, insurance models and risk management can be expressed by random walk and its modifications. In literature, there are several considerable studies (e.g., Aliyev and Khaniyev, Feller, Janseen and Leeuwarden, etc.). In this study, the stationary characteristics of the random walk process Y t which represent the buffer stock level, are investigated. Especially, for all moments of the ergodic distribution of the process Y t, the exact expressions are obtained under the assumption that the random walk Y t generated by bilateral exponential distributed summands. Moreover, the exact and approximate expressions for variance, standard deviation, coefficient of variation, skewness and kurtosis of the process, are obtained. It is also observed that the ergodic distribution of the standardized stochastic process W t = Y t / a weakly converges to a triangular distribution in the interval 0, 2.

International Data Science & Engineering Symposium
IDSES

Zülfiye HANALİOGLU Tahir HANALİOGLU

Selection and evaluation problems have uncertainties due to concept and its perception discrepancies. Fuzzy set theory is one of the widely used methodology to cope with these uncertainties. There is a growing interest in evaluations using intuitionistic fuzzy sets. Differently from fuzzy sets, intuitionistic sets include both belonging degrees and nonbelonging degrees. Thus, the evaluation using intuitionistic fuzzy sets gives more realistic results. In this study, an intuitionistic fuzzy set-based evaluation system is proposed for supplier selection problem of a construction company. This system has qualitative- and quantitative evaluation parts. As qualitative part, decision makers of the company evaluate suppliers by the supplier selection criteria: i) quality, ii) price, iii) delivery, iv) productivity, v) service, vi) flexibility. The quantitative part includes supplier scores calculated through the current evaluation system of the company. A supplier-evaluation database was created by the abovementioned parts. The database was structured by α-cut representation of the evaluations. The calculations using intuitionistic fuzzy sets were done for this database, which was occurred by lower and upper bounds. After defuzzifying the database, suppliers were classified using the well-known fuzzy clustering algorithm, fuzzy c-means. The classification using fuzzy clustering algorithm has 95.0% accuracy.

International Data Science & Engineering Symposium
IDSES

Ayşenur AKIN M. Bahar BAŞKIR Hamza GAMGAM

Bayesian networks are probabilistic graphical representations which are used to build models from data and/or expert opinion. They can be utilized for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, decision making, etc. Dynamic Bayesian Networks (DBN) are extensions of Bayesian networks with temporal support, which can be used to model systems that dynamically change by the time. Nowadays, DBNs are utilized in a wide range of applications including robotics, data mining, speech recognition, digital forensics, protein sequencing, and bioinformatics. Several software tools exist in the public as well as commercial domains that support modelling and simulation of DBNs. However, these DBN software tools differ in terms of features support, ease of use, documentation, user’s community, etc. Therefore, it has become important to establish various metrics for selecting the proper software tools for creating and simulating DBNs, such as cost, licensing, GUI, built-in support for inference algorithms, structural learning, data types, etc. The goal of this survey is the evaluation and comparison of existing software tools for building DBNs based on a set of users centered criteria.

International Data Science & Engineering Symposium
IDSES

Hüseyin ÇAMBAŞI Özgür KURU Mehmet Fatih AMASYALI Sofiene TAHAR

Amazon Prime, Hulu, Apple TV, Puhutv, BluTV, Turkcell TV Plus vb. gibi digital platformlar insanlar üzerindeki etkisini artırmaya devam ediyor ve Netflix bunlardan birisidir. Netflix insanların seyretmek istediği film ve dizileri belirleyip onlara en kısa yoldan ulaştırmayı hedefleyen bir platformdur. Bu platform günümüzde çok popüler hale gelmiştir ve sinema sektörüyle yarışır seviyede bulunmaktadır. Netflix, veri madenciliğini etkili bir şekilde kullanarak insanların neyi izlemeyi sevdiğini bilmektedir. Ayrıca, Netflix aşırı ve gereksiz verilerden kurtularak büyük verilerin değerli bilgilere dönüştürülürken daha net olmasını sağladı. Bu bilgiler ışığında ülkeler için ayrı dizi ve filmlerin çekilmesine öncülük etmiş, izleyicilerin sevdiği aktör ve yönetmenleri bir araya getirerek, çektiği filmlerin izlenilirliğini arttırmış, böylece de Netflix markasını dünyaya tanıtarak izleyicileri kendine bağlamayı başarmıştır. Bizim bu çalışmadaki amacımız Netflix kullanıcılarının davranışlarını incelemek ve açıklamaktır. Bunu sağlamak için anket çalışması yapılmış olup, Netflix platformunun kullanma maksadının belirleyicilerini araştırmak için teknoloji kabul modeli kullanılmıştır.

International Data Science & Engineering Symposium
IDSES

Ufuk CEBECİ Oğuzhan İNCE

Teknoloji kavramının insanoğlunun hayatına girmesiyle teknolojinin kullanım alanları gün geçtikçe artmıştır. Gelişen teknik beceriler ile üretimin odağı değişmiştir. Zamanla üretimin verimliliği tartışılmaya başlamıştır. Bu tartışmalar sonucunda endüstriyel hayata kalite, optimizasyon, verim gibi yeni kavramlar girmiştir. Üretim süreçleri üzerine yapılan araştırmalarda daha kısa sürede, daha az maliyetli ve daha kaliteli üretim için yeni fikirler ortaya çıkmıştır. İkinci Dünya Savaşı’nın her alanda olduğu gibi üretim yöntemleri üzerinde de etkisi olmuştur. Artan teknoloji arayışları yeni üretim ve kalite anlayışlarına olan ihtiyacı doğurmuştur. Bu çalışmada yalın üretim ve altı sigma kavramları incelenmiş, yöntemler gösterilmiş ve seçilen bir tersanede uygulaması yapılmıştır. Yalın üretim felsefesi, üretim süreci boyunca oluşan artık ve katma değersiz durumları elimine etme üzerine oturtulmuştur. Altı sigma metodoloji ise üretim çıktısındaki hataların azaltılmasını ve üretimin standartlaştırarak hızlanmasını hedeflemektedir. Yapılan uygulama sonucunda teorik olarak süreçlerdeki kayıpların büyük oranda azaldığı ve firmanın iyileştirme çalışmaları doğrultusunda kazanç sağlayacağı görülmüştür.

International Data Science & Engineering Symposium
IDSES

Emre GÜVEN Ufuk CEBECİ

Today, with digitalization, it is possible to read digital data and make the right decisions based on analytical results. Along with big data, the science of data management and analysis is evolving to enable organizations to transform their knowledge into information that will help them achieve their goals. In this study, it is given as an example to increase awareness of big data, data mining, data mining and its applications in various sectors in Turkey.

International Data Science & Engineering Symposium
IDSES

Filiz ERSÖZ

The success of an Ad Mediation Software’s decision on from which ad network to request ads and in which order depends on the ability to estimate eCPM (effective Cost Per Mille) value used to measure ad revenue. This value varies for different applications depending on different external factors. It is not possible for domain experts to make successful predictions by analyzing different sets of external factors for a large number of applications and to keep these estimates constantly up to date. Therefore, eCPM values were automatically estimated separately for each application based on different advertising spaces and different countries using time series analysis. The ARIMA model was used to estimate and the hyper-parameters of the model were optimized by using grid search method. For most of the values obtained, it was found that the values obtained with the intuition of domain experts were closer to the actual values.

International Data Science & Engineering Symposium
IDSES

Görkem GİRAY Murat Osman ÜNALIR Şeyma TAHMAZ