https://hdl.handle.net/20.500.11857/87 2026-06-02T18:37:57Z https://hdl.handle.net/20.500.11857/4352 Meslek Yüksekokulunda Yapılan Eğitimin Mezunlarının İş Hayatındaki Etkisi Üzerine Bir Araştırma Atalay, Muhammet; Bayer, Harun; Çelik, Enes Yükseköğretimkurumları toplumun ve ekonominin ihtiyaç duyduğu yetişmiş insan gücünühazırlayan önemli bir eğitim öğretim basamağıdır. Meslek yüksekokullarımezunları, yükseköğretimin diğer düzeylerine göre iş hayatına çok daha hızlı dahilolmaktadır. İki yıllık öğretim sürecinde başarılı olan ve ön lisans derecesialanlar, yetişmiş ara eleman olarak ekonomik ve sosyal hayatın çok çeşitlikollarında iş hayatına atılmaktadır. Hem yükseköğretim kurumların verimliliğihem de mezunlarının meslek hayatı açısından, üniversitelerden mezun olanlarındonanımlarıyla iş dünyasının beklentilerinin örtüşmesi arzu edilir. Bu çalışma,bir meslek yüksekokulunun mezunları örneği üzerinden, mezunların öğretimhayatında edindikleri donanım ve mezuniyet sonrası iş bulma ve çalışmasüreçlerindeki yaşadıkları tecrübeleri karşılaştırmalı olarak değerlendirmeküzere yapılmıştır. Çalışmada genel tarama yöntemi kullanılmış ve betimselistatistikler incelenmiştir. Bir meslek yüksekokulundan 2010-2014 yılları arasıdört farklı programdan mezun olan kişiler ile yapılan araştırma sonuçlarınagöre; mezuniyet sonrası bir işe girme süresinin 1-2 yıl aralığında olduğu,çalışmaya başlamayan mezunların önemli bir kısmının örgün öğretim hayatınadevam ettikleri ve mezunların büyük bir çoğunluğunun açık veya örgün öğretimile lisans diploması almaya çalıştıkları belirlenmiştir. Ayrıca öğretimsüresince kazanılan yeterliliklerin, bu programlardan mezun olanların çalışmahayatında önemli ve yol gösterici olduğu saptanmıştır. DergiPark: 364439; kusbder 2019-01-01T00:00:00Z https://hdl.handle.net/20.500.11857/3613 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 https://hdl.handle.net/20.500.11857/3320 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 https://hdl.handle.net/20.500.11857/3243 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