IJET Vol. 2, Issue 4, December 2016
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International Journal of Engineering Technologies (IJET) Dergisi / International Journal of Engineering Technologies (IJET)
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Öğe International Journal of Engineering Technologies (IJET) Vol. 2, Issue 4, December 2016 & Vol. 3, Issue 1, March 2017(İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2017) Bayram, MustafaDear Colleagues, On behalf of the editorial board of International Journal of Engineering Technologies (IJET), I would like to share our happiness to publish the eighth and ninth issues of IJET. My special thanks are for members of editorial board, publication board, editorial team, referees, authors and other technical staff. Please find the eighth and ninth issues of International Journal of Engineering Technologies at http://ijet.gelisim.edu.tr or http://dergipark.gov.tr/ijet. We invite you to review the Table of Contents by visiting our web site and review articles and items of interest. IJET will continue to publish high level scientific research papers in the field of Engineering Technologies as an international peer-reviewed scientific and academic journal of Istanbul Gelisim University. Thanks for your continuing interest in our work, Professor Mustafa BAYRAM Istanbul Gelisim University mbayram@gelisim.edu.tr http://ijet.gelisim.edu.tr http://dergipark.gov.tr/ijet Printed ISSN: 2149-0104 e-ISSN: 2149-5262Öğe Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis(İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2017-04-20) Oyewola, David; Hakimi, Danladi; Adeboye, Kayode; Shehu, Musa DanjumaBreast cancer is one of the causes of female death in the world. Mammography is commonly used for distinguishing malignant tumors from benign ones. In this research, a mammographic diagnostic method is presented for breast cancer biopsy outcome predictions using five machine learning which includes: Logistic Regression(LR), Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) and Support Vector Machine(SVM) classification. The testing results showed that SVM learning classification performs better than other with accuracy of 95.8% in diagnosing both malignant and benign breast cancer, a sensitivity of 98.4% in diagnosing disease, a specificity of 94.4%. Furthermore, an estimated area of the receiver operating characteristic (ROC) curve analysis for Support vector machine (SVM) was 99.9% for breast cancer outcome predictions, outperformed the diagnostic accuracies of Logistic Regression(LR), Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Random Forest(RF) methods. Therefore, Support Vector Machine (SVM) learning classification with mammography can provide highly accurate and consistent diagnoses in distinguishing malignant and benign cases for breast cancer predictions.Öğe Numerical and Experimental Investigation of Aerodynamics Characteristics of NACA 0015 Aerofoil(İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2017-04-20) Rubel, Robiul Islam; Uddin, Kamal; Islam, Zahidul; Rokunuzzaman, M. D.An aerofoil is stream line body. Symmetric aerofoil (NACA 0015) is used in many applications such as in aircraft submarine fins, rotary and some fixed wings. The ultimate objective of an aerofoil is to obtain the lift necessary to keep an airplane in the air. But construction of the blade with proper angle of attack and implementation has significant effect on lift force. Insufficient lift force might cause fail of airplane flying, especially at high speed. Modern technologist use different simulation techniques to avoid costly model testing. But simulation is based on some assumption. Thus practically results are not fully authentic and have a deviation. In this work numerical and experimental investigation of NACA 0015 is studied at different angle of attack (degree) at different velocity of air by determining the forces at every two degrees from 00 to 180. The experimental is conveyed in a low speed wind tunnel. The numerical analysis is conducted using ANSYS (combined with CFD and FLUENT FLOW). The use of the CFD technology greatly reduces the overall investment and efforts for aerofoil design. CFD method contributes to visualize the flow pattern inside aerofoil and takes less time and faster according to experimental methods. After completing the experimental and numerical data is compared. Therefore objective of this paper is to find the deviation and validation of aerodynamics characteristics of aerofoil NACA 0015 for experimental and numerical method.
