Nicosia, Cyprus
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.

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The approximate analytical bound-state solution of the Schrödinger equation for the Manning-Rosen (MR) potential is found by taking a new approximation scheme to the orbital centrifugal term. The Nikiforov-Uvarov method is used in the calculations. We obtain analytic forms for the energy eigenvalues and the corresponding normalized wave functions in terms of Jacobi polynomials or hypergeometric functions for different screening parameters 1 /b. The rotational-vibrational energy states for a few diatomic molecules are calculated for arbitrary quantum numbers n and l with different values of the potential parameter α. The present numerical results agree within five decimal digits with the previously reported results for different 1 /b values. A few special cases of the s-wave (l = 0) MR potential and the Hulthen potential are also studied. © 2011 The Royal Swedish Academy of Sciences.

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.

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.

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.

Abiyev R.H.,Near East University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

This paper presents the development of novel type-2 wavelet neural network system for time series prediction. The structure of type-2 Fuzzy Wavelet Neural Network (FWNN) is proposed and its learning algorithm is derived. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a wavelet function in the consequent part of the rules. For generating the structure of prediction model a fuzzy clustering algorithm is implemented to generate the rules automatically and the gradient learning algorithm is used for parameter identification. Type-2 FWNN is used for modelling and prediction of exchange rate time series. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of type-2 FWNN based systems and with the comparative simulation results of previous related models. © 2010 Springer-Verlag.

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.

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.

We solve the parametric generalized effective Schrdinger equation with a specific choice of position-dependent mass function and Morse oscillator potential by means of the Nikiforov-Uvarov method combined with the Pekeris approximation scheme. All bound-state energies are found explicitly and all corresponding radial wave functions are built analytically. We choose the Weyl or Li and Kuhn ordering for the ambiguity parameters in our numerical work to calculate the energy spectrum for a few (H 2, LiH, HCl and CO) diatomic molecules with arbitrary vibration n and rotation l quantum numbers and different position-dependent mass functions. Two special cases including the constant mass and the vibration s-wave (l=0) are also investigated. © 2012 Copyright Taylor and Francis Group, LLC.

Ikhdair S.M.,Near East University
Central European Journal of Physics | Year: 2012

Using an approximation scheme to deal with the centrifugal (pseudo-centrifugal) term, we solve the Dirac equation with the screened Coulomb (Yukawa) potential for any arbitrary spin-orbit quantum number κ. Based on the spin and pseudospin symmetry, analytic bound state energy spectrum formulas and their corresponding upper- and lower-spinor components of two Dirac particles are obtained using a shortcut of the Nikiforov-Uvarov method. We find a wide range of permissible values for the spin symmetry constant C s from the valence energy spectrum of particle and also for pseudospin symmetry constant C ps from the hole energy spectrum of antiparticle. Further, we show that the present potential interaction becomes less (more) attractive for a long (short) range screening parameter α. To remove the degeneracies in energy levels we consider the spin and pseudospin solution of Dirac equation for Yukawa potential plus a centrifugal-like term. A few special cases such as the exact spin (pseudospin) symmetry Dirac-Yukawa, the Yukawa plus centrifugal-like potentials, the limit when α becomes zero (Coulomb potential field) and the non-relativistic limit of our solution are studied. The nonrelativistic solutions are compared with those obtained by other methods. © 2012 © Versita Warsaw and Springer-Verlag Wien.

By using an improved approximation scheme to deal with the centrifugal (pseudocentrifugal) term, we solve the Dirac equation for the generalized Morse potential with arbitrary spin-orbit quantum number κ. In the presence of spin and pseudospin symmetry, the analytic bound state energy eigenvalues and the associated upperand lower-spinor components of two Dirac particles are found by using the basic concepts of the Nikiforov-Uvarov method. We study the special cases when κ = ±1 (l =l̃ = 0, s-wave), the non-relativistic limit and the limit when a becomes zero (Kratzer potential model). The present solutions are compared with those obtained by other methods. © 2011 American Institute of Physics.

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