Browsing by Author "Maraş, H.H."

Now showing 1 - 3 of 3

Citation - WoS: 9
Citation - Scopus: 12
A New Robust Binary Image Embedding Algorithm in Discrete Wavelet Domain
(Institute of Electrical and Electronics Engineers Inc., 2014) Mohammed, A.; Maraş, H.H.; Elbasi, E.; 34410; Bilgisayar Mühendisliği
Digital watermarks have recently emerged as a possible solution for protecting the copyright of digital materials, the work presented in this paper is concerned with the Discrete Wavelet Transform (DWT) based non-blind digital watermarking, and how the DWT is an efficient transform in the field of digital watermarking. In this work we used an optimum criteria that embeds four watermarks in more than one level of DWT in the same algorithm. The aim of this work is to keep the Correlation Coefficient (CC) between the original and the extracted watermark around the value of 0.9.
Citation - Scopus: 1
Automatic detection of spina bifida occulta with deep learning methods from plain pelvic radiographs
(Springer Science and Business Media Deutschland GmbH, 2023) Duran, S.; Üreten, K.; Maraş, Y.; Maraş, H.H.; Gök, K.; Atalar, E.; Çayhan, V.; 34410; Bilgisayar Mühendisliği
Purpose: Spina bifida occulta (SBO), which is the most common congenital spinal deformity, is often seen in the lower lumbar spine and sacrum. In this study, it is aimed to develop a computer-aided diagnosis method that will help clinicians in the diagnosis of spina bifida occulta from plain pelvic radiographs with deep learning methods and transfer learning method. Materials and methods: The You Only Look Once (YOLO) algorithm was used for object detection, and classification was made by applying transfer learning with a pre-trained VGG-19, ResNet-101, MobileNetV2, and GoogLeNet networks. Our dataset consisted of 206 normal lumbosacral radiographs and 160 SBO lumbosacral radiographs. The performance of the models was evaluated by metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC) results. Results: In the detection of SBO, 85.5%, 80.8%, 89.7%, 87.5%, 84%, and 0.92 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained with the pre-trained VGG-19 model, respectively. The pre-trained VGG-19 model performed better than the others. Conclusion: Successful results were obtained in this study performed to the diagnosis of SBO with deep learning methods. A model that will assist physicians in the diagnosis of SBO can be developed with new studies to be conducted with a large number of spinal radiographs. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.
Spam Detection With Fasttext Based Features
(Institute of Electrical and Electronics Engineers Inc., 2024) Karadeniz, T.; Tokdemir, G.; Maraş, H.H.; Yazılım Mühendisliği; Bilgisayar Mühendisliği
Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative; mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.