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Nicosia, Cyprus

Near East University is a private university located in North Cyprus. It was founded in North Nicosia in 1988. The founder of the Near East University is Dr. Suat İ. Günsel, a Turkish Cypriot educationalist and entrepreneur.The Near East University currently has 16 faculties with 98 departments, 4 vocational schools, 2 high schools and 4 graduate schools offering programs at undergraduate and postgraduate levels.With over 25,000 students, it is the largest university in Northern Cyprus. Wikipedia.

Rasmussen F.,Near East University | Hancox R.J.,University of Otago
Current Opinion in Allergy and Clinical Immunology | Year: 2014

PURPOSE OF REVIEW: Obesity and asthma are chronic conditions affecting millions of people worldwide. The two conditions also appear to be linked with an increased risk of asthma in people who are obese. The purpose of this review is to describe mechanism(s) that may explain the association between asthma and obesity. RECENT FINDINGS: Current evidence suggests that the association between asthma and obesity is linked by two major phenotypes and three important pathways of obesity-related asthma: one phenotype with primary (often atopic) asthma that is aggravated by obesity and a second phenotype with late-onset nonatopic asthma, which predominantly affects women and primarily seems to be associated with neutrophilic inflammation. Proposed pathways include the mechanical effects of obesity (fewer deep inspirations leading to increased airway hyperresponsiveness), an inflammatory pathway driven by obesity-related cytokines (adipokines), and finally environment and lifestyle changes that have led to an increasing prevalence of obesity over the past 50 years (including exposures in utero, physical activity, and diet) may also result in asthma in predisposed individuals. How these environmental changes influence the occurrence and expression of asthma may depend on the age of exposure and on interactions with genetic susceptibilities. SUMMARY: Future research should be directed to shed light on the associations between obesity and asthma phenotypes, modern lifestyles and environmental exposures and genetic susceptibilities. Copyright © Lippincott Williams & Wilkins. Source

Abiyev R.H.,Near East University | Kaynak O.,Bogazici University
IEEE Transactions on Industrial Electronics | Year: 2010

In industry, most dynamical plants are characterized by unpredictable and hard-to-formulate factors, uncertainty, and fuzziness of information, and as a result, deterministic models usually prove to be insufficient to adequately describe the process. In such situations, the use of fuzzy approaches becomes a viable alternative. However, the systems constructed on the base of type 1 fuzzy systems cannot directly handle the uncertainties associated with information or data in the knowledge base of the process. One possible way to alleviate the problem is to resort to the use of type 2 fuzzy systems. In this paper, the structure of a type 2 TakagiSugenoKang fuzzy neural system is presented, and its parameter update rule is derived based on fuzzy clustering and gradient learning algorithm. Its performance for identification and control of time-varying as well as some time-invariant plants is evaluated and compared with other approaches seen in the literature. It is seen that the proposed structure is a potential candidate for identification and control purposes of uncertain plants, with the uncertainties being handled adequately by type 2 fuzzy sets. © 2010 IEEE. Source

Cavus N.,Near East University
Advances in Engineering Software | Year: 2010

There are many open source and commercially available Learning Management System (LMS) on the Internet and one of the important problems in this field is how to choose an LMS that will be the most effective one and that will satisfy the requirements. In order to help in the solution of this problem, the author has developed a computer program to aid in the selection of an LMS. The developed system is web-based and can easily be used over the Internet any where over the world at any time. The developed system is basically a web-based decision support system used to evaluate LMSs by using a flexible and smart algorithm derived from artificial intelligent concepts with fuzzy logic values. The paper describes the development of the LMS evaluation system. The individuals who are most likely to be interested in the LMS evaluation process are teachers, students, and any educational organizations such as: universities, schools, institutes, and anyone else who seeks to have a LMS. © 2009. Source

Khashman A.,Near East University
Expert Systems with Applications | Year: 2010

This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to- validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications. © 2010 Elsevier Ltd. Source

Khashman A.,Near East University
Applied Soft Computing Journal | Year: 2011

Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. Artificial neural networks (NNs) have been considered to be accurate tools for credit analysis among others in the credit industry. Lately, emotional neural networks (EmNNs) have been suggested and applied successfully for pattern recognition. In this paper we investigate the efficiency of EmNNs and compare their performance to conventional NNs when applied to credit risk evaluation. In total 12 neural networks; based equally on emotional and conventional neural models; are arbitrated under three learning schemes to classify whether a credit application is approved or declined. The learning schemes differ in the ratio of training-to-validation data used during training and testing the neural networks. The emotional and conventional neural models are trained using real world credit application cases from the Australian credit approval datasets which has 690 cases; each case with 14 numerical attributes; based on which an application is accepted or rejected. The performance of the 12 neural networks will be evaluated using certain criteria. Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, thus, making them ideal for implementation in fast automatic processing of credit applications. © 2011 Elsevier B.V. All rights reserved. Source

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