https://hdl.handle.net/20.500.11857/96 2026-06-02T19:44:05Z 2026-06-02T19:44:05Z Çelik, Enes Kondiloğlu, Adil https://hdl.handle.net/20.500.11857/3613 2023-01-28T12:23:28Z 2015-01-01T00:00:00Z

DETECTION OF FAKE BANKNOTES WITH ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES Çelik, Enes; Kondiloğlu, Adil The document and banknote counterfeiting remove us as a usurpation of the rights of individuals and institutions. By the advancement of technology special paper, the ink usage in the original banknote, placing watermarks, micro text are challenged to fraud that also does not prevent the counterfeiting of widespread. The counterfeit banknotes detection and minimizing damage is one of the important elements. In this, decided and expert systems can be improved to estimate the counterfeit banknotes to using dates of the moneys. The data of banknotes are sorted before to this point, after comparative results are discussed of the testing. In this study, it is classified by the classification algorithms to using digitized available data of real and counterfeit banknotes images. Artificial Neural Networks and Support Vector Machines are used in the classification. Correctly identified is detected to rate of 74.6% in the test results which is tested by Artificial Neural Networks, correctly identified is detected to rate of 93.8% in the results of tests which is tested by Support Vector Machines. 23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY -- Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Univ

2015-01-01T00:00:00Z Çelik, Enes Omurca, Sevinç İlhan https://hdl.handle.net/20.500.11857/3320 2023-01-28T12:17:23Z 2019-01-01T00:00:00Z

Improving Parkinson's Disease Diagnosis with Machine Learning Methods Çelik, Enes; Omurca, Sevinç İlhan Parkinson's disease is a type of disease caused by the loss of dopamine-producing cells in the brain. As the amount of dopamine decreases, the symptoms of Parkinson's disease emerge. Parkinson's disease is a slow-developing disease, and symptoms such as hands, arms, legs, chin and face tremors are increasing over time. As the disease progresses, people may have difficulty in walking and speaking. There is no definitive treatment for Parkinson's disease; however, with the help of some drugs, the symptoms of the disease can be reduced. Although there is no definitive treatment for Parkinson's disease, the patient can continue his normal life by controlling the problems caused by the disease. At this point, it is important to prevent early detection and progression of the disease. In this study, different types of classification methods such as Logistic regression, Support Vector Machine, Extra Trees, Gradient Boosting and Random Forest are compared in order to predict Parkinson's disease. A total of 1208 speech data sets consisting of 26 features obtained from Parkinson's patients and non-patients were used in the classification stage. The feature space of the dataset is expanded due to correlation maps. These correlation maps are constructed with the features which are obtained by using Principal Component Analysis (PCA), Information Gain (IG) and all features respectively. It is concluded that, classification results which are attained with expanded features outperform the classification results attained with the original features of the data. International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEY -- IEEE Turkey Sect, IEEE EMB, Erasmus+, Europass

2019-01-01T00:00:00Z İlhan, Hamza Osman Çelik, Enes https://hdl.handle.net/20.500.11857/3243 2023-01-28T12:23:26Z 2016-01-01T00:00:00Z

The Mesothelioma Disease Diagnosis with Artificial Intelligence Methods İlhan, Hamza Osman; Çelik, Enes Asbestos is a carcinogenic substance, and threatens human health. Malignant Mesothelioma disease is one of the most dangerous kind of cancer caused by asbestos mineral. The most common symptom of the disease, progressive shortness of breath and constant pain. Early treatment and diagnosis are necessary. Otherwise, the disease can lead people to die in a short period of time. In this paper, different types of artificial intelligence methods are compared for effective Malignant Mesothelioma's diseases classification. Support Vector Machine, Neural Network and Decision Tree methods are selected in terms of regular machine learning concept. Additionally, Bagging and Adaboost re-sampling within ensemble learning terminology is also adapted. Totally 324 Malignant Mesothelioma data which consists of 34 features is used in this study. K-fold cross-validation technique is performed to compute the performance of the algorithms with different K values. 100% classification accuracies are obtained from three tested methods; Support Vector Machine, Decision Tree and Bagging. Additionally, the process time of methods are measured in case of using method in lots of data. In this sense, methods are evaluated based on accuracy and time complexity. The results of this paper are also compared with previous studies using same Malignant Mesothelioma's dataset. 10th IEEE International Conference on Application of Information and Communication Technologies (AICT) -- OCT 12-14, 2016 -- Baku, AZERBAIJAN -- Nar, Dell EMC, IEEE, IEEE Reg 8, Minist Commun & High Technologies, Minist Educ Azerbaijan, Qafqaz Univ, Baku State Univ, Baku Higher Oil Sch, Lomonosov Moscow State Univ, Baku branch, Azerbaijan Tech Univ, Univ Malaysia Sabah, ANAS, Inst Informat Technol, ANAS, Inst Control Syst, UNESCO Inst Informat Technol Educ

2016-01-01T00:00:00Z Çelik, Enes İlhan, Hamza Osman Elbir, Ahmet https://hdl.handle.net/20.500.11857/3023 2023-01-28T12:23:17Z 2017-01-01T00:00:00Z

Detection and Estimation of Down Syndrome Genes by Machine Learning Techniques Çelik, Enes; İlhan, Hamza Osman; Elbir, Ahmet Down syndrome is accepted as the common birth defect in population and diagnosed as more physical development with less cognitive activity than an average human. Early diagnosis of disease play important role for the patient future life. Computer aided systems, in terms of artificial intelligence, results more accurate and consistent diagnosis in the detection and estimation of down syndrome genes compare to doctor decisions. In this study, detection and estimation of down syndrome disease is maintained by analyzing the protein levels in genes. In this sense, a Decision Support System based on machine learning techniques are proposed to estimate the down syndrome automatically. Additionally, another technique named as Principal Component Analyses are performed to eliminate multi proteins in genes into fewer number to achieve the same success with less information. 25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY -- Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univ

2017-01-01T00:00:00Z