WOS - Web of Science
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Publication The US real GNP is trend-stationary after all(Informa UK Limited, 2016-07-13) Tolga Omay; Rangan Gupta; Giovanni BonaccoltoPublication Empirical Investigation of Small-Scale Aluminium Wool Packed Solar Air Heater Made with Waste Material(Politeknik Dergisi, 2021-12-01) Ataollah Khanları; Azim Doğuş Tuncer; Ceylin Şirin; Faraz Afshari; Afşin GüngörWaste production is an important problem for the developing world and globalization. The waste materials can be reused through recycling process and its environmental effects can be minimized. Utilizing renewable energy sources at the maximum level is also an important issue for a sustainable development in future as well as waste management. In this study, small-scale solar air heating systems were produced from waste materials to analyze the usability of waste material in renewable energy systems. Scrap metal elbows were used in the production of heaters. The first solar heater is hollow (SH) and the second one was modified by filling it with aluminum wool (SHAW). Both heaters were tested simultaneously at different flow rates (0.014, 0.010 and 0.006 kg/s). According to the experimental results, the thermal efficiency values for SH and SHAW were found in the range of 33.63-42.90% and 42.69-56.98%, respectively. In addition, it was observed that a low cost modification such as using aluminum wool can significantly increase thermal performance of the solar heating system.Publication A VARIATIONAL APPROACH OF THE STURM-LIOUVILLE PROBLEM IN FRACTIONAL DIFFERENCE CALCULUS(Dynamic Publishers, 2018-01-26) RAZIYE MERT; LYNN ERBE; THABET ABDELJAWADPublication Solving Constrained Optimal Control Problems Using State-Dependent Factorization and Chebyshev Polynomials(American Institute of Aeronautics and Astronautics (AIAA), 2018-03) Mohammad Mehdi Gomroki; Francesco Topputo; Franco Bernelli-Zazzera; Ozan TekinalpPublication Model Continuity in Discrete Event Simulation(Association for Computing Machinery (ACM), 2015-04-16) Deniz Çetinkaya; Alexander Verbraeck; Mamadou D. SeckMost of the well-known modeling and simulation (M&S) methodologies state the importance of conceptual modeling in simulation studies, and they suggest the use of conceptual models during the simulation model development process. However, only a limited number of methodologies refers to how to move from a conceptual model to an executable simulation model. Besides, existing M&S methodologies do not typically provide a formal method for model transformations between the models in different stages of the development process. Hence, in the current M&S practice, model continuity is usually not fulfilled. In this article, a model-driven development framework for M&S is presented to bridge the gap between different stages of a simulation study and to obtain model continuity. The applicability of the framework is illustrated with a prototype modeling environment and a case study in the discrete event simulation domain.Publication Intrapreneurship and expectations restrictions(Universidad Autonoma del Caribe, 2020-02-28) Olcay Okun; Korhan Arun; Suat BegecThis article develops arguments about the factors that promote intrapreneurship in relation to role theory. These are based on contributions from interactional and structural sociology. Fixed theoretical tools for intrapreneurship are not enough. So, the structural and interactionist perspective of sociology is necessary to understand the concept of intrapreneurship. The above approaches depend on individuals, organizations or environments to encourage potential employees to be intrapreneurs. Thus, Expectations can be a cornerstone for intrapreneurship because intrapreneurs learn from their roles.Publication The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis(Hindawi Limited, 2020-04-25) Omer F. Akmese; Gul Dogan; Hakan Kor; Hasan Erbay; Emre DemirAcute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.Publication The Relationship between Tertiary Level EFL Teachers’ Self-efficacy and their Willingness to Use Communicative Activities in Speaking(Elsevier BV, 2015-08) Aycan Demir; Ayşegül Yurtsever; Betül ÇimenliPublication How does language model size effects speech recognition accuracy for the Turkish language?(LookUs Bilisim A.S., 2016) Behnam Asefisaray; Erhan Mengüşoğlu; Murat Hacıömeroğlu; Hayri SeverPublication Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms(Politeknik Dergisi, 2021-12-01) Khaldoon AWADH; Ayhan AKBAŞIn recent years, the use of machine learning and data mining technologies has drawn researchers’ attention to new ways to improve the performance of Intrusion Detection Systems (IDS). These techniques have proven to be an effective method in distinguishing malicious network packets. One of the most challenging problems that researchers are faced with is the transformation of data into a form that can be handled effectively by Machine Learning Algorithms (MLA). In this paper, we present an IDS model based on the decision tree C4.5 algorithm with transforming simulated UNSW-NB15 dataset as a pre-processing operation. Our model uses Term Frequency.Inverse Document Frequency (TF.IDF) to convert data types to an acceptable and efficient form for machine learning to achieve high detection performance. The model has been tested with randomly selected 250000 records of the UNSW-NB15 dataset. Selected records have been grouped into various segment sizes, like 50, 500, 1000, and 5000 items. Each segment has been, further, grouped into two subsets of multi and binary class datasets. The performance of the Decision Tree C4.5 algorithm with Multilayer Perceptron (MLP) and Naive Bayes (NB) has been compared in Weka software. Our proposed method significantly has improved the accuracy of classifiers and decreased incorrectly detected instances. The increase in accuracy reflects the efficiency of transforming the dataset with TF.IDF of various segment sizes.