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Öğe Enhancement Of Breast Cancer Diagnosis Accuracy With Deep Learning(2019) Yıldız, İlker; Karadeniz, Alper TalhaBreast cancer is a highly fatal disease that is very prevalent among the female population. In this study, a new type of approach is proposed with the aim of improving the accuracy of breast cancer diagnosis, an important problem of our present time, by means of deep learning, one of the techniques in machine learning. In the designed method, the original data set of Breast Cancer Wisconsin being available in the Irvine Machine Learning Repository of University of California was used. Within this data set, there were 699 data consisting of 10 independent variables and 1 dependent variable. The complete utilization of the entire data set was ensured by correction of 16 incorrect data. A normalization process was applied in the data set for the purpose of reducing the time required for learning process. The used data set was allocated as 80% for training, 10% for validation, and 10% for testing. An artificial neural network was designed for the deep learning model. The neural network was set up of a total of 5 layers which were an input layer with 10 neurons, 3 hidden layers with 1000 neurons for each layer, and an output layer with 3 neurons. The software, developed for implementation was written by using Spyder which is an interactive development environment for Python programming language. In addition, Keras neural network API was used. The performance of the model was evaluated with Confusion Matrix and ROC (Receiver Operating Characteristic) analysis. According to the test data obtained at the end of the training, it was observed that the implemented model provided successful results. It is considered that the proposed method will contribute to the improvement of breast cancer diagnosis accuracy.Öğe Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme(2019) Çelik, Yüksel; Yıldız, İlker; Karadeniz, Alper TalhaOptimizasyon bir problemin olabilecek farklı çözümleri arasından en iyi sonucu verenin bulunmasıdır. Optimizasyon problemlerinin çözümünde birçok algoritma kullanılmaktadır. Optimizasyon algoritmaları genel olarak sezgisel optimizasyon algoritmaları ve matematiksel optimizasyon algoritmaları olarak ikiye ayrılmaktadır. Matematiksel optimizasyon algoritmaları, tüm çözüm kümesini tarayarak çözüme ulaşmayı amaçlarken, sezgisel optimizasyon algoritmaları ise, çözüm kümesine sezgisel olarak yaklaşmakta ve en iyi çözüme yada en iyiye yakın bir çözüme ulaşmayı amaçlamaktadır. Çözüm kümesi geniş olan problemlerde matematiksel optimizasyon algoritmalarının kullanımı maliyetlidir. Bu tip problemlerin çözümünde, sezgisel optimizasyon algoritmaları daha avantajlı olup daha fazla tercih edilmektedir. Bir optimizasyon algoritmasının her tür problem veya test fonksiyonu üzerinde başarılı olması beklenemez. Bu nedenle hangi tür problemin hangi algoritma ile daha iyi çözüldüğünün belirlenmesi gerekmektedir. Günümüzde temel sezgisel metotların birleşerek etkili kullanımı sonucunda Metasezgisel isimli algoritmalar geliştirilmiştir. Bu algoritmalar, yüksek seviyeli çalışma ortamında, verimli arama işlemleri kullanarak çözüm uzayındaki optimum çözüme daha hızlı şekilde ulaşmaktadır. Metasezgisel optimizasyon tekniklerinin kullanımının yaygın olması nedeniyle, günümüzde birçok yeni metasezgisel optimizasyon algoritmaları önerilmektedir. Önerilen bu algoritmalar üzerinde geliştirmeler ve hibrit çalışmalar da yapılmaktadır. Bu çalışmada, literatürde son üç yılda (2017-2019) önerilmiş olan, Harris Hawks Optimizasyon Algoritması (HHO), Satin Bowerbird Optimizasyon Algoritması (SBO), Optimal Foraging Algoritması (OFA), Butterfly Optimizasyon Algoritması (BOA), Pity Beetle Algoritması (PBA) ve Collective Decision Optimizasyon Algoritması (CDOA) ele alınmıştır. Geliştirilen bu yeni optimizasyon algoritmalarının esinlendikleri alan, çalışma mantıkları ve arama stratejileri incelenerek sunulmuştur. Gerçekleştirilen bu derlemenin metasezgizel optimizasyon problemleri alanında yapılacak olan çalışmalara ışık tutacağı düşünülmektedir.Öğe Urban traffic optimization with real time intelligence intersection traffic light system(2018) Çelik, Yüksel; Karadeniz, Alper TalhaThese instructions give you guidelines for preparing papers for IJISAE. Use this document as a template if you are using Microsoft Word 2007 or later. Otherwise, use this document as an instruction set. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd–Fe–B"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field, but are not required. Put a space between authors’ initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstract must be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must be between 150–250 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstract must be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.Öğe Urban Traffic Optimization with Real Time Intelligence Intersection Traffic Light System(2018) Çelik, Yüksel; Karadeniz, Alper TalhaThese instructions give you guidelines for preparing papers for IJISAE. Use this document as a template if you are usingMicrosoft Word 2007 or later. Otherwise, use this document as an instruction set. Paper titles should be written in uppercase and lowercaseletters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g.,\"Nd–Fe–B\"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field, but are not required. Put a spacebetween authors’ initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstractmust be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must bebetween 150–250 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstractmust be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should includethree or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases asthis can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.Öğe Whale Optimization Algorithm for Numerical Constrained Optimization(2020) Celik, Yuksel; Karadeniz, Alper TalhaWhale Optimization Algorithm (WOA), WOA is a recently developed, nature-inspired, meta-heuristic optimization algorithm. The algorithm was developed in 2016, inspired by bubble hunting strategies used by humpback whales. To determine the performance of each optimization algorithm developed, they should be tested on a different type of optimization test problems. In this paper, we aim to investigate and analyse WOA logarithm on constrained optimization the performance and accuracy of the proposed method are examined on 13 (G1-G13) constrained numerical benchmark functions, and the obtained results are compared with other meta-heuristic optimization algorithms which taken from the literature. The experimental results show that WOA has low performance on constrained optimization.
