Person: YILDIZ, KAZIM
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YILDIZ
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KAZIM
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Publication Metadata only A novel thermal-based fabric defect detection technique(TAYLOR & FRANCIS LTD, 2015) BULDU, ALİ; Yildiz, Kazim; Buldu, Ali; Demetgul, Mustafa; Yildiz, ZehraDuring the fabric production process, many defects can be occurred stemming from the unevenness in spinning, weaving, finishing processes, or from the raw materials. The fabric quality control process for the detection of these defects is carried out by specialist operators. In this paper, a new method based on the use of thermal camera for detecting these defects from the textile fabric images is presented. For identification process of defective area, fabric images were obtained by a thermal camera during the fabric flow in quality control machine that was specially designed for this experiment. Defective and defect-free regions on fabric surface were determined by thermal camera due to the thermal differences. The mentioned thermal defect detection system will eliminate the worker usage for fabric quality control process, thus it will provide a cost-effective and competitive manufacturing.Publication Metadata only A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm(SAGE PUBLICATIONS INC, 2016) BULDU, ALİ; Yildiz, Kazim; Buldu, Ali; Demetgul, MustafaIn this study, fabric defects have been detected and classified from a video recording captured during the quality control process. Fabric quality control system prototype has been manufactured and a thermal camera was located on the quality control machine. The defective areas on the fabric surface were detected using the heat difference occurring between the defective and defect-free zones. Gray level co-occurrence matrix is used for feature extraction for defective images. The defective images are classified by k-nearest neighbor algorithm. The image processing stage consists of wavelet, threshold, and morphological operations. The defects have been classified with an average accuracy rate of 96%. In addition, the location of the defect has been identified and the defect type and location are recorded during the process via specially designed image processing interface. According to the experimental results, the proposed method works effectively.Publication Metadata only Detection of DDoS attacks with feed forward based deep neural network model(PERGAMON-ELSEVIER SCIENCE LTD, 2021) BULDU, ALİ; Cil, Abdullah Emir; Yildiz, Kazim; Buldu, AliAs a result of the increase in the services provided over the internet, it is seen that the network infrastructure is more exposed to cyber attacks. The most widely used of these attacks are Distributed Denial of Service (DDoS) attacks that easily disrupt services. The most important factor in the fight against DDoS attacks is the early detection and separation of network traffic. In this study, it is suggested to use the deep neural network (DNN) as a deep learning model that detects DDoS attacks on the sample of packets captured from network traffic. DNN model can work quickly and with high accuracy even in small samples because it contains feature extraction and classification processes in its structure and has layers that update itself as it is trained. As a result of the experiments carried out on the CICDDoS2019 dataset containing the current DDoS attack types created in 2019, it was observed that the attacks on network traffic were detected with 99.99% success and the attack types were classified with an accuracy rate of 94.57%. The high accuracy values obtained show that the deep learning model can be used effectively in combating DDoS attacks.Publication Metadata only Comparison of LDA, NMF and BERTopic topic modeling techniques on Amazon product review dataset: A case study(2023-07-21) YILDIZ, KAZIM; BÜYÜKTANIR, BÜŞRA; TURAN S., YILDIZ K., BÜYÜKTANIR B.Publication Metadata only Hand-Held Spectrophotometer Design for Textile Fabrics(AMER INST PHYSICS, 2017) YILDIZ, KAZIM; Bocekci, Veysel Gokhan; Yildiz, Kazim; Perova, TIn this study, a hand-held spectrophotometer was designed by taking advantage of the developments in modern optoelectronic technology. Spectrophotometer devices are used to determine the color information from the optic properties of the materials. As an alternative to a desktop spectrophotometer device we have implemented, it is the first prototype, low cost and portable. The prototype model designed for the textile industry can detect the color tone of any fabric. The prototype model consists of optic sensor, processor, display floors. According to the color applied on the optic sensor, it produces special frequency information on its output at that color value. In Arduino type processor, the frequency information is evaluated by the program we have written and the color tone information between 0-255 ton is decided and displayed on the screen.Publication Metadata only Dimensionality reduction-based feature extraction and classification on fleece fabric images(SPRINGER LONDON LTD, 2017) YILDIZ, KAZIM; Yildiz, KazimThis work performs dimensionality reduction-based classification on fleece fabric-based images taken by a thermal camera. In order to convert images into the gray level, a principal component analysis-based dimension reduction stage was proposed. In addition, symmetric central local binary patterns were performed with the help of the proposed method by using the images after dimension reduction process. The local binary pattern features preserve local texture features from different kinds of defective image types. The experimental results showed that combined work has a great classification accuracy. The classification accuracy was reported using two different algorithms: Naive Bayes and K-nearest neighbor classifier.Publication Metadata only Fault diagnosis on material handling system using feature selection and data mining techniques(ELSEVIER SCI LTD, 2014) YILDIZ, KAZIM; Demetgul, M.; Yildiz, K.; Taskin, S.; Tansel, I. N.; Yazicioglu, O.The material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems. Diffusion Map (DM), Local Linear Embedding (LLE) and AutoEncoder (AE) algorithms were used for future extraction. Encoded signals were classified by using the Gustafson-Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm. (C) 2014 Elsevier Ltd. All rights reserved.Publication Metadata only Raspbraille: Conversion to braille alphabet with optical character recognition and voice recognition algorithm(2022-01-01) YILDIZ, KAZIM; ÜLKÜ, EYÜP EMRE; BÜYÜKTANIR, BÜŞRA; DALIP F., YILDIZ K., ÜLKÜ E. E. , BÜYÜKTANIR B.