Kocaeli Sağlık ve Teknoloji Üniversitesi

Eker, Ayse Gul; Pehlivanoglu, Meltem Kurt; Ince, Ibrahim; Duru, Nevcihan

In brain tumors, which are formed by the abnormal growth of cells in brain tissue, early diagnosis, and accurate treatment are crucial. Brain tumor segmentation provides experts with the ability to accurately identify brain tumors and determine their location. Through segmentation, the type, size, and shape of tumors can be determined, enabling the application of appropriate treatment. In this study, the aim was to perform brain tumor segmentation on the Brain Tumor Figshare (BTF) dataset, which includes diverse images in terms of structural complexity, viewing angles, different device usage, noise, and bias field effects, without applying any preprocessing. Basic models such as U-Net and FCN were utilized, along with transfer learning-based approaches using VGG, XceptionNet, InceptionNet, and ResNet architectures as the underlying structures in these models. According to the experimental results, the 16-layer FCN architecture with VGG- 19 as the underlying structure achieved the highest score with a dice score of 0.9169.

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Pehlivanoǧlu, Meltem Kurt; Varol, Onur; Aldemir, Osman; Uncu, Ata Emir; Duru, Nevcihan

The classification of dementia levels in Alzheimer's disease is divided based on various characteristics of the disease. In this study, for the purpose of classifying dementia levels in Alzheimer's disease using MRI images, various Convolutional Neural Network (CNN)-based deep learning models, as well as machine learning and Artificial Neural Network (ANN) models, were trained. Various image manipulation and augmentation techniques were applied to create new datasets, and model performances were observed on these datasets. As a different approach, numerical features were extracted from MRI images, and models were built using these extracted features. Fusion hybrid models were produced by combining the most successful models trained with the images and their numerical representations. Experimental results have proven that hybrid models outperform other models in terms of performance in the classification of Alzheimer's disease.

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Eker, Kadir; Eker, Ayse Gul; Mandal, Dilek; Pehlivanoglu, Meltem Kurt; Duru, Nevcihan

Today, detection of malware is often done automatically with artificial intelligence supported systems. In this way, this task, which was previously done manually, can be performed more precisely and more quickly. Darknet traffic is an internet network with many different cybercrime threats. It is also fake traffic observed in empty address space. A globally valid set of Internet Protocol (IP) addresses that are not assigned to any host or device. In this study, a deep learning model and pervasive machine learning classification algorithms are compared using the CIC-Darknet2020 dataset to describe Darknet traffic. The 'Tor' and 'Vpn' classes in the dataset, 'Darknet', 'Non-Tor' and 'Non-Vpn' classes are also specified as harmless. Various adjustments have been made for the unbalanced dataset, which has been performed with multiple classification, various machine learning algorithms. Random Forest Algorithm, Decision Trees and Deep Learning Model were the algorithms with the most successful accuracy values with %97.3, %97.4 %98.2values respectively.

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Karaduman, Ismet; Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Duru, Nevcihan; Dundar, Tolga Turan; Serdar Mutluer, Ahmet

Many of the decisions made and taken in the field of health are important for human life to be sufficient. In such an important area where human transactions are concerned, artificial intelligence-supported studies have a very important place, especially in recent years. This is the correct classification of four classes (Normal Tissue, Tumor Tissue, Hypervascularized Tissue and Background) separately with the 3D-CNN based deep learning model on the In-vivo Hyperspectral Human Brain Image dataset. For this purpose, firstly, three different data settings with 128 spectral bands were obtained from raw images, each containing 826 spectral bands, with dimension reduction methods. In this way, each data setting was made with the designs used in the 3D-CNN based deep learning model. As a result of the experimental studies, the highest F1 score of 99.69% was achieved with the 3D-CNN-based deep learning model.

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Eker, Ayse Gul; Duru, Nevcihan; Eker, Kadir

EEG signals are one of the most basic methods used in identifying and analyzing brain activities. Visual representation of EEG signals can be achieved with spectrograms. Spectrograms represent a visual representation of a signal's signal strength over time. In this study, the signals in an EEG dataset containing 'positive', 'negative' and 'neutral' emotion classes were classified with a deep learning model, and then these signals were transformed into a spectrogram image in the dataset with convolutional network model and also with transfer learning (EfficientNet and XceptionNet). Multiple classification was performed with pre-trained models. The success value obtained by the classification of the EEG signals and the success of the visualization in this classification were measured and presented by comparison. While higher accuracy values were achieved in the classification of signals with the deep network model, in metrics such as precision and F1-score, the classification of images with the proposed convolutional network model achieved much higher performance.

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Salici, Mustafa; Olcer, Uyesi Ercan

This article examines in depth the effects of BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer) models in the field of natural language processing (NLP). NLP, as a sub-branch of computer science and artificial intelligence, has an important place in the processes of understanding, processing and producing human language. Transformer-based models have provided revolutionary advances in many NLP tasks, especially language modeling, sentiment analysis, machine translation and question answering. In this article, the technical structures of BERT and GPT models, their performance in low-resource languages such as Turkish and the difficulties encountered in these languages are discussed in detail. The BERT model stands out with its ability to evaluate the contextual meaning of the language from both directions using bidirectional context and offers high success rates especially in tasks such as text classification, named entity recognition (NER) and sentiment analysis. The GPT model, on the other hand, provides superiority in text production with its autoregressive structure and gives successful results in tasks such as creative writing and chatbots. However, the application of these models to morphologically rich and agglutinative languages such as Turkish poses certain challenges. These challenges arise due to the structural features of the language and the limitations of the datasets. The article also discusses the development of Turkish-specific models such as BERTurk, data augmentation techniques, and the success of modifications appropriate to the characteristics of the language in overcoming these challenges. In conclusion, the development of BERT and GPT models in the field of NLP has been a major step in terms of natural language processing and modeling. However, further research and development is required for these models to perform better in languages such as Turkish. The article predicts that future developments of these models will enable language technologies to reach wider audiences and be used in various applications.

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