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Öğe Classification of adrenal lesions by bounded PSO-NN [Sinirli PSO-YSA Yapisi ile Sürrenal Lezyonlarin Siniflandirilmasi](Institute of Electrical and Electronics Engineers Inc., 2017) Koyuncu H.; Ceylan R.Adrenal glands are the organs at which vitally important hormones are released. In adrenal glands, different kind of benign and malign lesions can arise. Herein, Dynamic Computed Tomography (dynamic CT) is the most used scan type for definition of lesion types. On the events that dynamic CT underwhelms, biopsy process is performed which is difficultly implemented because of the location of adrenal glands. During biopsy process, different complications can happen since adrenals glands are surrounded by spleen, lung, etc. At this point, a decision support system is needed for helping to medical experts. In this study, a Region of Interest (ROI) is defined that includes adrenal lesions. After that, feature extraction is realized by using Gray-Level Co-Occurance Matrix (GLCM) and the second-order statistics. At classification part, Neural Network (NN) and a novel approach including NN (Bounded PSO-NN) are evaluated by utilizing from three performance metrics. As a result, it's confirmed that Bounded PSO-NN classifies the malign and benign patterns more accurately which obtained by analysis taken from ROI. © 2017 IEEE.Öğe Kinetics of mercury (II) transport through a bulk liquid membrane using macrocyclic compound as carrier(2006) Koyuncu H.; Ersoz M.; Yilmaz M.[Abstract not Available]Öğe RF ensemble novelties based on optimized & backpropagated NNs(International Association of Computer Science and Information Technology, 2017) Koyuncu H.; Ceylan R.This paper presents a classifier model based on Rotation Forest (RF) ensemble structure for biomedical data classification. Classifiers based on RF are generally implemented by using Decision Trees. In this study, optimized Neural Network (NN) is preferred as being the base classifier in RF so as to achieve higher classification performance. Two optimization techniques, Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO), are utilized to improve the performance of NN for escaping from local minima. In this way, PSO-NN and ABC-NN based RF structures are designed, and they are called as RF (PSO-NN) and RF (ABC-NN), respectively. In these classifiers, initial weights of NNs are found by using PSO or ABC algorithms. The implemented classifiers based on RF are applied to biomedical datasets (Wisconsin Breast Cancer and Pima Indian Diabetes) that are taken from UCI Machine Learning Repository. Furthermore, fourteen different ensemble structures are generated using these algorithms to prove the superiority of the proposed method. When the results are examined using several performance metrics, it is seen that RF (ABC-NN) classifier achieves to more reliable and better results than other classifiers.Öğe ScPSO-based multithresholding modalities for suspicious region detection on mammograms(Elsevier Inc., 2018) Ceylan R.; Koyuncu H.[Abstract not Available]
