Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12899/35

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Turkish Exports Before and After the 2001 Financial Crisis: A Panel Gravity Model
(SAGE Publishing, 2022) Karacan, Semih; Korkmaz, Özge
Turkey was subjected to a number of financial shocks after the liberalisation movements in the 1980s. The most devastating of them was the consecutive political and financial crises in late 2000 and early 2001. The absence of political stability and depreciated Turkish Lira devastated the markets. The Turkish government immediately acted against the collapsed economic system and introduced a radical Economic Stability Programme under the supervision of Kemal Dervis¸. The programme has restructured the banking and financial system and improved economic discipline. In this study, we aim to investigate the impacts of the 2001 crisis and the structural changes on Turkish exports. To this end, we estimate a one-way gravity model, using panel data belonging to Turkish exports to 135 World Trade Organization (WTO) member countries, over the period between 1981 and 2015. The augmented model controls for the inter/intra-industry exports, competitiveness, trade agreements, trade unions and additional demographics. We utilised Poisson pseudo-maximum-likelihood (PPML) estimator to account for unobservable time-invariant effects, zero trade, possible heteroscedasticity, and cross-correlation. The results reveals that Turkey has become a free market economy after the liberalisation movements in the early 1980s, and its exports are determined by the same indicators that affect other similar economies; on the other hand, the 2001 crisis has an immediate positive effect on exports through weak Turkish Lira, but this effect turns to negative in the following year. In addition, we find that structural changes in the economic system has a significant effect on exports and help to mitigate the trade-distorting effects of the global financial crisis in 2008.
What is the role of the rents in energy connection with economic growth for China and the United States
(Elsevier Ltd, 2022) Korkmaz, Özge
Energy is one of the essential factors in the industrial production process, and it is important for economic growth. Within the context of the debate on the ecological consequences of energy usage and the importance of renewable energy, we have analyzed the relationships between fossil energy and GDP for both China and the United States. This study has been constructed on related economic data from 1990 to 2015. By employing the Hacker and Hatemi-J (2006) causality tests and the Autoregressive Distributed Lag (ARDL) bounds testing approach, we have examined the relationship between economic growth (GDP) and all of the following: natural gas rents, renewable energy consumption, renewable electricity production, coal rents, and oil rents. The main contribution of this study to literature is relating and analyzing the relationship between GDP and all of the following: renewable energy (RE) consumption and production, fossil energy consumption and production, as well as gas/coal/oil rents for the United States and China. It considers the nexus not only separately but also collectively. Furthermore, to explore the causality connection between each type of energy input, this method is employed. Our empirical findings show that there is no long-term causal relationship among these variables for China while there is a long-term causal relationship among the variables in the United States. In addition, we found that the RE production, fossil energy production, and consumption, GDP, rents, all of these variables cause RE consumption in both China and the United States
Türkiye’de Doğrudan Yabancı Yatırımlar İle İşsizlik Oranı Arasındaki İlişki (2005- 2019)
(Onur Oğuz, 10 Ekim 2020) Daştan, Barış; Korkmaz, Özge
Doğrudan yabancı yatırımlar, ev sahibi ülke ekonomilerini sosyal ve ekonomik açıdan etkilemektedir. Doğrudan yabancı yatırımların ev sahibi ülkeye teknoloji transferi sağlama, girişimcilik becerisini artırma, piyasayı daha rekabetçi hale getirme ve ekonomik büyümeye pozitif katkı sağlayarak istihdam artışı sağlama gibi etkileri vardır. Bu çalışmada doğrudan yabancı yatırımların iş gücü piyasası üzerinde bir etkisinin olup olmadığı araştırılmaktadır. Bu bağlamda Türkiye’ye yapılan doğrudan yabancı sermaye yatırımları ile Türkiye’deki işsizlik oranı arasındaki ilişki 2005:Q4-2019:Q1 dönemi için incelenmiştir. Çalışmada ilk olarak doğrudan yabancı sermaye yatırımları ile işsizlik oranı arasındaki uzun dönemli ilişki yapısal kırılmaları dikkate alan Maki eşbütünleşme testi ile araştırılmıştır. Eşbütünleşme testi sonuçlarına göre, iki değişken arasında uzun dönemli bir ilişkinin var olmadığı gözlenmiştir. Ardından çalışmada değişkenler arasındaki nedensel bağın varlığı araştırılmıştır. Bu amaçla Toda-Yamamoto nedensellik testinden yararlanılmıştır. Nedensellik analizi sonucuna göre, tek yönlü bir nedenselliğin varlığı gözlenmiştir. Bir diğer ifadeyle, Türkiye’de işsizlik oranından doğrudan yabancı sermaye yatırımlarına doğru tek yönlü bir nedensel bağın var olduğu saptanmıştır.
Sosyal Medya Bağımlılığı: Bayburt Üniversitesi İktisadi ve İdari Bilimler Öğrencileri Üzerine Bir İnceleme
(Kırklareli Üniversitesi, 2020) Korkmaz, Özge
Günümüzde dünya nüfusunun yaklaşık üçte ikisinin internet kullanıcısı olduğu tahmin edilmektedir. Günlük atılan tweet sayısı yüz milyonlarla ifade edilebilirken, Google arama sayısı ve e-posta sayısı ise milyarın çok daha üzerindedir. Bu da gelişen teknolojilerle beraber bireylerin sosyal hayatlarında dijital ve sosyal medya araçlarının etkilerinin yadsınamaz boyutlara ulaştığının bir göstergesidir. Başlangıçta eğlenceli olarak nitelendirilen bu uygulamaların fazla kullanılması durumunda, bağımlılık sorunları ortaya çıkabilmektedir. Bir diğer ifadeyle, tıp alanında sosyal medya bağımlılığı bir hastalık olarak nitelendirilmekte ve bağımlılığın kontrol altında tutulmasının önemli olduğu belirtilmektedir. Sosyal medya bağımlılığının birey üzerindeki olumsuz etkileri dikkate alındığında genç neslin bağımlılık düzeylerinin belirlenmesi ve buna göre önlemler alınması önem arz etmektedir. Bu çalışmada Bayburt Üniversitesi İktisadi ve İdari Bilimler Fakültesi'nde 2018-2019 akademik yılı Bahar döneminde eğitim gören öğrencilerinin dijital ve sosyal medya bağımlılık düzeylerine yönelik bilgiler elde edilmiştir. Çalışmanın sonuç kısmında anket verilerine dayalı olarak dijital ve sosyal medya bağımlılık düzeyinin azaltılmasına yönelik öneriler sunulmuştur.
Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks
(Celal Bayar Üniversitesi, 2020) Dönmez, Emrah
Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG-16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.
Energy Policy Recommendations for ASEAN Countries: Empirical Evidence from the Bootstrap Panel Granger Causality Analysis
(Springer, 2021) Adalı, Zafer; Korkmaz, Özge; Çelik, Orkun
In social sciences, especially in the economic literature, energy has been one of the most important topics. Energy seems to be dominant on the political and economic agendas because of nearly all economic activities linked to energy. Hence, all economies have endeavored to detect and implement policies to increase their efficiency and mitigate energy’s detrimental effects on the earth. This study’s principal mission is to check the connection between economic growth, non-renewable and renewable energy consumption in ASEAN-5 countries over 1990–2014 to recommend energy-saving policies. Within this view, bootstrap panel Granger causality test is applied. The model results indicate that economic growth causes renewable energy consumption in the Philippines and economic growth induces non-renewable energy consumption in Indonesia and Malaysia. A unidirectional causality relationship operating from renewable energy consumption to economic growth is approved for Brunei Darussalam. Finally, a unidirectional causality relationship working from renewable energy consumption to Brunei Darussalam’s economic growth is confirmed.
New human identification method using Tietze graph-based feature generation
(Springer, 2021) Tuncer, Türker; Aydemir, Emrah; Doğan, Şengül; Kobat, Mehmet Ali; Kaya, Muhammed Çağrı; Metin, Serkan
Electrocardiogram (ECG) signals have been widely used for disease diagnosis. Besides, the ECG signals can be used for human identification. In this work, a Tietze pattern and neighborhood component analysis (NCA)-based human identification method is proposed. Our model uses two feature generation methods to extract both statistical and textural features. The Tietze graph is considered to create a pattern of the presented local graph structure (LGS). Both statistical and textural feature generations are not enough to present a high-accurate model. Therefore, a multileveled structure must be created. Tunable Q-factor wavelet transform (TQWT) is employed as a decomposer. The generated/extracted features in each level are merged, and the merged features are selected using NCA. The k-nearest neighbors (kNN) classifier is deployed on the chosen features in the classification phase to obtain predicted values. The recommended method was tested on two ECG signal corpora called ECGID and MIT-BIH. The model achieved 99.12% and 99.94% accuracies on the used ECGID and MIT-BIH datasets, respectively.
A hybrid DNN–LSTM model for detecting phishing URLs
(Springer, 2021) Özcan, Alper; Çatal, Çağatay; Dönmez, Emrah; Şentürk, Behçet
Phishing is an attack targeting to imitate the official websites of corporations such as banks, e-commerce, financial institutions, and governmental institutions. Phishing websites aim to access and retrieve users’ important information such as personal identification, social security number, password, e-mail, credit card, and other account information. Several anti-phishing techniques have been developed to cope with the increasing number of phishing attacks so far. Machine learning and particularly, deep learning algorithms are nowadays the most crucial techniques used to detect and prevent phishing attacks because of their strong learning abilities on massive datasets and their state-of-the-art results in many classification problems. Previously, two types of feature extraction techniques [i.e., character embedding-based and manual natural language processing (NLP) feature extraction] were used in isolation. However, researchers did not consolidate these features and therefore, the performance was not remarkable. Unlike previous works, our study presented an approach that utilizes both feature extraction techniques. We discussed how to combine these feature extraction techniques to fully utilize from the available data. This paper proposes hybrid deep learning models based on long short-term memory and deep neural network algorithms for detecting phishing uniform resource locator and evaluates the performance of the models on phishing datasets. The proposed hybrid deep learning models utilize both character embedding and NLP features, thereby simultaneously exploiting deep connections between characters and revealing NLP-based high-level connections. Experimental results showed that the proposed models achieve superior performance than the other phishing detection models in terms of accuracy metric.
A cloudware architecture for collaboration of multiple agvs in indoor logistics: Case study in fabric manufacturing enterprises
(Multidisciplinary Digital Publishing Institute (MDPI), 2020) Okumuş, Fatih; Dönmez, Emrah; Kocamaz, Adnan Fatih
In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.

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