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https://hdl.handle.net/20.500.12511/4418

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Organizational sustainability, legitimacy, and multinational companies
(IGI Global, 2024) Divrik, Bahar
The aim of this book chapter is to anlayze how internationalization of multinational companies affect organiational sustainability. Organizational sustainability has a contrasting perspective with respect to the dual effects of internationalization on sustainability strengths (being good) and sustainability concerns (being bad). On one side, it is discussed that internationalization improves organizational sustainability strengths as multinational enterpreises, which are mainly dependent on foreign sales, are highly motivated to adopt organizational sustainability as a global business norm. Such norm?conformity overcomes the liability of origin and legitimacy challenge in foreign markets. On the other hand, it is contended that internationalization also increases organizational sustainability concerns because subsidiaries of multinationals are susceptible to being decoupled from the headquarters' organizational sustainability policy.
International market entry strategies
(IGI Global, 2023) Divrik, Bahar
Entry mode selection is one of the most strategic aspects of international business. The selection of international market entry strategy is an institutional decision that comprises the choice of target market, entry mode, marketing plan, and control system. The right entry mode decision enables companies all assets to enter the targeted foreign markets. The entry modes can be grouped into three categories; export-based methods, non-equity methods (contractual entry), and equity methods (investment entry). The export-based methods can be either indirect export or direct export. The main forms of non-equity methods (contractual entry) are licensing and franchising, and equity methods (investment entry) are foreign direct investment, joint venture and acquisitions, and mergers. In this chapter, the internal and external factors of the target and home country will be analyzed in detail. The main features of entry mode are discussed in detail, and in the last part, factors that have a strategic importance are mentioned.
Secure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems
(Springer Science and Business Media Deutschland GmbH, 2023) Şahinbaş, Kevser; Çatak, Ferhat Özgür
Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges people and information technology and speeds up shopping. For these reasons, IoT technology has started to be used on a large scale. Thanks to the use of IoT technology in health services, chronic disease monitoring, health monitoring, rapid intervention, early diagnosis and treatment, etc., facilitate the delivery of health services. However, the data transferred to the digital environment pose a threat of privacy leakage. Unauthorized persons have used them, and there have been malicious attacks on the health and privacy of individuals. In this chapter, it is aimed to propose a model to handle the privacy problems based on federated learning. Besides, we apply secure multi-party computation. Our proposed model presents an extensive privacy and data analysis and achieves high performance.
How will metaverse and ai change traditional marketing techniques?
(IGI Global, 2023) Doğuç, Özge
The development of technology has transformed the process of product development and customer relationship building. It shows impressive results in deep learning and AI applications. Companies can see farther and make data-driven decisions by leveraging advanced technology solutions managed by AI. Deep learning can be used to analyze and integrate large databases. Considering deep learning activities, it could be a significant opportunity to meet customers' needs and help companies develop profitable new products. Likewise, virtual worlds bring a new approach to marketing. The concept of the metaverse can be described as a simulated reality. Metaverse can enable the creation of virtual reality worlds by integrating online experiences. This chapter discusses how AI and deep learning affect marketing. In this context, industrial opportunities of AI in various marketing activities, AI, strategic marketing, and understanding consumer behaviors will be focused on, and cases of companies using deep learning applications will be included.
Post-epidemic national and institutional energy strategies
(Springer Science and Business Media Deutschland GmbH, 2023) Alhan, Mehmet Ali
In this study, the latest developments in the energy world are examined, the strategic plans carried out by the major energy producing countries (especially Russia, the United States, China and Europe) and the strategies for the transition to renewable energy focused on large companies producing in the world oil market are summarized. From a working point of view, the conversion to renewable energy depends on the measures taken in the energy sector and its significant financial resources. This financial resource is under the umbrella of large oil companies, whether private sector international oil companies (IOCs) or state-controlled national oil companies (NOCs). On the state side, it is particularly important to maintain economic growth with geopolitical interests, self-sufficiency and energy diversity. Companies, on the other hand, stay out of politics and act under the influence of factors such as the financial situation, the ability to coordinate the measures taken by the energy sector. Governments face a range of social demands and pressure from sectors that oppose it in the name of the energy transition. Moreover, the emergence of new economic segments (clean electricity, solar energy, etc.) poses great challenges for foreign relations and public policy formulation. Therefore, there will be compromises between renewable energies and non-renewable energies (Fossil fuels), which will require strategic interventions on behalf of CPIs (climate policy initiative) and NOCs. In general, two types of intervention stand out; increased investment in renewables or efforts to delay investment expansion of renewable resources relative to oil. From a more structured perspective, major energy companies prefer to initiate the energy transition gradually to maintain their long-term position in the energy industry while seeking to strengthen the role of the oil and gas (P&G) sector. In this way, the strategy in the global economy protects its spheres of influence. The importance of renewable energies today and in the future becomes clear as this “energy dilemma” facing power generation companies has to consider the following long-term perspectives.
Defining the significant factors of currency exchange rate risk by considering text mining and fuzzy AHP
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
This study aims to determine the factors affecting the exchange rate risk of companies. In this context, firstly, articles in the ScienceDirect database that contain exchange rate risk in their titles, abstracts, and keywords are provided. Single, double, and triple words were identified in 152 different studies that were published after 2018, which met the relevant criteria. As a result of the analysis of these words, 4 different criteria that could affect the exchange rate risk were determined. The relevant criteria were then weighted with fuzzy AHP. The findings indicate that the macroeconomic performance of countries is the most important determinant of exchange rate risk. In addition, wrong risk management practices of companies play also an important role for this risk. Thus, it is recommended that the government should design necessary regulations for the companies not to take too much currency exchange risk.
Determining the ways to increase economic growth of developing and developed economies: An application with data mining and fuzzy TOPSIS
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
This study aims to identify the influencing factors of economic growth for both developing and developed economies. Firstly, literature is reviewed, and 7 different variables are selected as leading indicators of the economic growth. In this framework, 155 developing and 31 developed economies are evaluated. After that, decision tree approach is taken into consideration and the most significant 4 variables are determined for each country group. In the final process, fuzzy TOPSIS approach is used to weigh E7 and G7 economies with respect to the economic development. The findings indicate that for developing economies, the most important determinants are foreign direct investment, interest rate, domestic credit, and research and development. On the other side, inflation, domestic credit, foreign direct investment, and unemployment play a more significant role in the economic growth of developed economies. Hence, it is recommended that developing economies should mainly focus on the strategies to attract the attention of the foreign investors and to increase research and development.
Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images
(Elsevier, 2021) Şahinbaş, Kevser; Çatak, Ferhat Özgür
Countries the world over have focused on protecting human health and combatting the COVID-19 outbreak. It has had a destructive effect on human health and daily life. Many people have been infected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. Although laboratory tests have been widely applied as diagnostic tools, findings suggest that the application of X-ray and computed tomography images and pretrained deep convolutional neural network (CNN) models can help in the accurate detection of this disease. In this study, we propose a model for COVID-19 diagnosis, applying a deep CNN technique based on raw chest X-ray images of COVID-19 patients, which can be accessed publicly on GitHub. Fifty positive and 50 negative COVID-19 X-ray images for training and 20 positive and 20 negative images for testing phases are included. Because the classification of X-ray images needs a deep architecture to cope with the complicated structure of images, we apply five different architectures of well-known pretrained deep CNN models: VGG16, VGG19, ResNet, DenseNet, and InceptionV3. The pretrained VGG16 model can detect COVID-19 from non-COVID-19 cases with the highest classification performance of 80% accuracy among the other four proposed models, and it can be used as a helpful tool in the department of radiology. In the proposed model, a limited dataset of COVID-19 X-ray images is used that can provide more accurate performance when the number of instances in the dataset increases.
Malicious URL detection using machine learning
(IGI Global, 2020) Çatak, Ferhat Özgür; Şahinbaş, Kevser; Dörtkardeş, Volkan
Recently, with the increase in Internet usage, cybersecurity has been a significant challenge for computer systems. Different malicious URLs emit different malicious software and try to capture user information. Signature-based approaches have often been used to detect such websites and detected malicious URLs have been attempted to restrict access by using various security components. This chapter proposes using host-based and lexical features of the associated URLs to better improve the performance of classifiers for detecting malicious web sites. Random forest models and gradient boosting classifier are applied to create a URL classifier using URL string attributes as features. The highest accuracy was achieved by random forest as 98.6%. The results show that being able to identify malicious websites based on URL alone and classify them as spam URLs without relying on page content will result in significant resource savings as well as safe browsing experience for the user.
The problem of depending on fossil fuels in the energy policies of the european union: A strategic analysis in the eastern mediterranean region
(Springer Science and Business Media Deutschland GmbH, 2022) Alhan, Mehmet Ali
The Eastern Mediterranean geographical area consists of an area surrounded by Europe in the North, Asia in the East, the Middle East in the Southeast, and Africa in the South. The Republic of Turkey is the country with the longest coastline in this area. Recently, the discovery of increasing hydrocarbon reserves in this geography has whetted the appetite of the European Union (EU) countries that cannot meet their energy needs. Federal Germany, which has the largest industrial capacity among the European Union countries with energy dependence on the Russian Federation, had an urgent and important need for an alternative, reliable, and clean energy supply, especially during the Russia-Ukraine crisis. In this study, the European Union’s process of closing nuclear and hydroelectric power plants for alternative, reliable, and clean energy and the depletion of fossil fuels will be discussed. It will be argued that energy domination has a very strategic meaning. In the process of liberation from fossil fuel, the search for gas hydrate and the political, economic, and cultural relations of the Republic of Turkey with Europe will be discussed.
Analysis of customer churn in the banking industry using data mining
(2022) Doğuç, Özge
Today, banks have a very important place in the great economic environments of countries. As in every sector, there are many competitors and a great competitive environment in the banking field. Especially individual customers prefer digital channels to make their banking transactions faster and easier. Banks need to take fast and industry-leading steps to meet these expectations of their customers. They need to differentiate themselves from the competition with innovative features by giving importance to digital. The main goals of the banks in the competitive environment are gaining new customers, increasing customer loyalty, reducing customer churn rates, and providing superior customer satisfaction. In this study, customer data belonging to a bank were analyzed with data analysis algorithms. Customer churn analysis was performed using different machine algorithms. The model was created on the Knime platform. This study performs a customer loss analysis using data mining algorithms. The aim is to reveal the reasons for losing customers, the elements of customer loyalty and to help develop customer relations activities accordingly.
Prediction of general anxiety disorder using machine learning techniques
(Nova Science Publishers, Inc., 2022) Şahinbaş, Kevser
Today, the increase in mental health problems, the variable nature of mental health and the lack of sufficient number of mental health professionals have led to the search for machine learning that applied to mental health problems extensively, and its use in the field of health is considered as a new hope. Mental disorders are a health illness that affects a person's emotions, reasoning, and social interaction. Early diagnosis and the application of the right treatment after the correct diagnosis have always been the expectation of all humanity. As technologies develop, machine learning has started to attract attention in the field of medicine with the development of diagnostic methods. The aim of this study is to conduct classification studies by using machine learning methods in the diagnosis process of anxiety disorder diseases. A publicly available dataset of 672 people's Generalized Anxiety Disorder 7-item (GAD-7) responses during the COVID-19 period is used. This study demonstrates that it is possible to classify mental health status with 0.97 accuracy rates with the Support Vector Machine algorithm, which has a higher performance than other algorithms.
Comparative study of the forecasting solar energy generation in Istanbul
(Springer Science and Business Media Deutschland GmbH, 2022) Şahinbaş, Kevser
The importance of renewable energy sources makes it extremely important day by day due to the limited reserves of fossil fuels and the damage they cause to the environment. Fossil fuels play an important role in electrical energy production. This situation brings up the necessity of turning to alternative sources that cause the formation of renewable energies such as solar energy, which is one of the renewable energy sources. Solar energy is a renewable energy source with benefits such as ease of installation and use, as well as the fact that it does not pollute the environment or produce toxic waste. In the world and in our country, investments in solar power plants are increasing rapidly from year to year. In this study, the solar energy situation of our country was discussed and a model for solar energy generation forecasting was proposed by using RNN, LSTM, and GRU deep learning architecture using the İkitelli Solar Power Plant daily data of Istanbul between May 2018 and May 2019. Generation forecasting values for 5 days later were estimated with 0.0069 error and 0.92 R2 values, which are accepted as one of the most important performance criteria by the LSTM model. The LSTM model’s solar energy generation values are slightly greater than those of the other models, it can be concluded that the LSTM model is appropriate for forecasting solar energy generation.
Identifying indicators of global financial crisis with fuzzy logic and data science: A comparative analysis between developing and developed economies
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
The main purpose of this study is to define the leading indicators of the 2008 global financial crisis for both developing and developed economies. In this context, 16 different variables are defined with the help of literature review. These variables are weighted by considering the fuzzy DEMATEL approach and the most significant 8 variables are identified. In addition to this analysis, decision tree methodology is also taken into consideration to understand main determinants of the financial crisis. The findings show that growth rate and capital adequacy of the banking industry are accepted as the leading indicators of the global crisis for developed economies. On the other hand, it is also concluded that inflation and industry development are accepted as the indicator of the financial crisis in addition to the economic growth and capital adequacy of the banks for developing economies. While considering the results, it is recommended that developed countries should periodically control economic growth rate and capital adequacy ratio of the banks to understand whether they have a financial crisis risk or not. Furthermore, for developing countries, it is also recommended that inflation rate should be periodically controlled, and they should identify necessary actions in order to minimize inflation rate.
Introduction to data science and machine learning algorithms
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
Data science has gained importance since available data and hardware facilities have been ubiquitous. Algorithms to process a huge amount of data and extract information were developed decades ago. However, due to the lack of high-capacity computers, it was not possible to use them on real-life data and problems. Today, from finance to medicine data science plays an important role to solve problems. Suffice it to say, machine learning algorithms are the core of this new phenomenon besides data itself. Artificial neural networks, deep learning, Support Vector Machines, Decision Tree Learning Models, and related algorithms have been used successfully and yielded very important results recently. On the other hand, text data have also gained importance being the fuel of machine learning in data science. Especially the emergence of social media and communication technology contributed to the popularity of texts in data science. In this chapter, concise introductions have been given about the most popular and also successful machine learning algorithms. This chapter will be helpful for those readers who do not have enough information about machine learning and its algorithms.
The influence of the politicians on macroeconomic performance: An analysis of donald trump’s tweets
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
The aim of this study is to understand the role of the politicians on the macroeconomic situation of the countries. For this purpose, the tweets of Donald Trump are taken into consideration. Text mining approach is used to evaluate these tweets and most used words are identified. Next, all-important keywords are matched with significant topics and 4 different classes are identified for this condition, which are financial, social, military, and political issues. After that, these keywords are ranked with the help of fuzzy VIKOR approach according to their impacts on macroeconomic performance. In this context, five different factors are also defined, which represent macroeconomic performance that are economic growth, unemployment, inflation, international trade, and industrial development. The findings show that political and economic factors are the most important items which affect macroeconomic situation of the country. It is recommended that politicians should not make negative explanations about political and financial conditions in order not to have unfavorable results in the economic activities.
How is the stock exchange index affected by the disclosures of politicians?
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
The main purpose of this study is to understand the main influence of the politicians’ disclosure on the stock exchange index. In this context, a machine learning model is built in order to understand the hidden patterns behind the daily changes (rises and falls) of Dow Johns index. In the fourth chapter of this book, Donald Trump’s personal tweets are obtained to make evaluations. Similarly, in the analysis process of this chapter, these tweets and four-day time series Dow Johns industrial average values are taken into consideration. The findings show that when Mr. Trump uses the word witch at least once, it is certain that the index will rise. On the other side, when he uses both the word military and witch on the same day, the index gets a steep high. These results give an idea that when politicians use negative words, it increases the volatility in the stock market. Similar to this situation, if their disclosures are related to military issues, the stock exchange will also be influenced. Therefore, it is strongly recommended that politicians should choose their words carefully in order not to increase the volatility in the stock market.
Profitability prediction of Turkish banking industry: A comparative analysis with data science and fuzzy ANP
(Springer Science and Business Media Deutschland GmbH, 2021) Silahtaroğlu, Gökhan; Dinçer, Hasan; Yüksel, Serhat
In this study, it is aimed to estimate the factors affecting the profitability of the Turkish banking sector. For this purpose, 34 different variables were firstly determined by literature review. Then, with the help of decision trees method, the most important 8 variables were selected. These variables were also evaluated with fuzzy ANP approach. As a result, it is understood that the foreign exchange position is the most important variable for the profitability of the Turkish banking sector. Based on this result, it is recommended that Turkish banks take some actions to minimize the currency exchange rate risk. In this context, it is considered that financial derivative products will help the management of this mentioned risk.
Recent applications of data mining in medical diagnosis and prediction
(Elsevier, 2022) Doğuç, Özge; Canbolat, Zehra Nur; Silahtaroğlu, Gökhan
Big data has been used in the health sector to improve the quality of life, predict epidemics, cure diseases, and avoid preventable deaths, beyond increasing profits or reducing the burden of excess labor. Data sources in healthcare have become quite diversified and accessible to individuals, such as wearable and implantable devices, smartphones, and real-time sensors. When combined with existing health data, daily (even instantaneous) data from these devices can be used to predict future health conditions of individuals and to identify necessary intervention points. This chapter discusses a number of recent studies that introduces methods for using big data to create intelligent systems for patient diagnosis, triage, predicting lab results, and even detecting tumors. These studies open ways for researchers in the healthcare sector to improve the quality of services provided to the patients as well as reducing costs for the healthcare institutions.
Robot process automation (RPA) and its future
(IGI Global, 2021) Doğuç, Özge
Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don’t require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

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