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Yayın Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset(IEEE, 2022-11-18) Ezerceli, Özay; Eskil, Mustafa TanerFacial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, and medical domains. Automated FER systems had been developed and have been used to recognize humans’ emotions but it has been a quite challenging problem in machine learning due to the high intra-class variation. The first models were using known methods such as Support Vector Machines (SVM), Bayes classifier, Fuzzy Techniques, Feature Selection, Artificial Neural Networks (ANN) in their models but still, some limitations affect the accuracy critically such as subjectivity, occlusion, pose, low resolution, scale, illumination variation, etc. The ability of CNN boosts FER accuracy. Deep learning algorithms have emerged as the greatest way to produce the best results in FER in recent years. Various datasets were used to train, test, and validate the models. FER2013, CK+, JAFFE and FERG are some of the most popular datasets. To improve the accuracy of FER models, one dataset or a mix of datasets has been employed. Every dataset includes limitations and issues that have an impact on the model that is trained for it. As a solution to this problem, our state-of-the-art model based on deep learning architectures, particularly convolutional neural network architectures (CNN) with supportive techniques has been implemented. The proposed model achieved 93.7% accuracy with the combination of FER2013 and CK+ datasets for FER2013.Yayın Unreasonable effectiveness of last hidden layer activations for adversarial robustness(Institute of Electrical and Electronics Engineers Inc., 2022) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerIn standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some ad-ditional benefits against some other attack methods like Deepfool attack.Yayın Application of ChatGPT in the tourism domain: potential structures and challenges(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanThe tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.Yayın TUR2SQL: A cross-domain Turkish dataset for Text-to-SQL(IEEE, 2023-09-15) Kanburoğlu, Ali Buğra; Tek, Faik BorayThe field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research.Yayın Analysis of single image super resolution models(IEEE, 2022-11-18) Köprülü, Mertali; Eskil, Mustafa TanerImage Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.Yayın Segmentation based classification of retinal diseases in OCT images(Institute of Electrical and Electronics Engineers Inc., 2024) Eren, Öykü; Tek, Faik Boray; Turkan, YaseminVolumetric optical coherence tomography (OCT) scans offer detailed visualization of the retinal layers, where any deformation can indicate potential abnormalities. This study introduced a method for classifying ocular diseases in OCT images through transfer learning. Applying transfer learning from natural images to Optical Coherence Tomography (OCT) scans present challenges, particularly when target domain examples are limited. Our approach aimed to enhance OCT-based retinal disease classification by leveraging transfer learning more effectively. We hypothesize that providing an explicit layer structure can improve classification accuracy. Using the OCTA-500 dataset, we explored various configurations by segmenting the retinal layers and integrating these segmentations with OCT scans. By combining horizontal and vertical cross-sectional middle slices and their blendings with segmentation outputs, we achieved a classification a ccuracy of 91.47% and an Area Under the Curve (AUC) of 0.96, significantly outperforming the classification of OCT slice images.Yayın Retinal disease classification using optical coherence tomography angiography images(Institute of Electrical and Electronics Engineers Inc., 2024) Aydın, Ömer Faruk; Nazlı, Muhammet Serdar; Tek, Faik Boray; Turkan, YaseminOptical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality widely used for the detailed visualization of retinal microvasculature, which is crucial for diagnosing and monitoring various retinal diseases. However, manual interpretation of OCTA images is labor-intensive and prone to variability, highlighting the need for automated classification methods. This study presents an aproach that utilizes transfer learning to classify OCTA images into different retinal disease categories, including age-related macular degeneration (AMD) and diapethic retinopathy (DR). We used the OCTA-500 dataset [1], the largest publicly available retinal dataset that contains images from 500 subjects with diverse retinal conditions. To address the class imbalance, we employed k-fold cross-validation and grouped various other conditions under the 'OTHERS' class. Additionally, we compared the performance of the ResNet50 model with OCTA inputs to that of the ResNet50 and RetFound (Vision Transformer) models with OCT inputs to assess the efficiency of OCTA in retinal condition classification. In the three-class (AMD, D R, Normal) classification, ResNet50-OCTA o utperformed ResNet50-OCT, but slightly underperformed compared to RetFound-OCT, which was pretrained on a large OCT dataset. In the four-class (AMD, DR, Normal, Others) classification, ResNet50-OCTA and RetFound-OCT achieved similar classification a ccuracies. This study establishes a baseline for retinal condition classification using the OCTA-500 dataset and provides a comparison between OCT and OCTA input modalities.Yayın Integrating the focusing neuron model with N-BEATS and N-HiTS(Institute of Electrical and Electronics Engineers Inc., 2024) Özçelik, Şuayb Talha; Tek, Faik BorayThe N-BEATS (Neural Basis Expansion Analysis for Time Series) model is a robust deep learning architecture designed specifically for time series forecasting. Its foundational idea lies in the use of a generic, interpretable architecture that leverages backward and forward residual links to predict time series data effectively. N - BEATS influenced the development of N-HiTS (Neural Hierarchical Interpretable Time Series), which builds upon and extends the foundational ideas of N-BEATS. This paper introduces new integrations to enhance these models using the Focusing Neuron model in blocks of N-BEATS and N-HiTS instead of Fully Connected (Dense) Neurons. The integration aims to improve the forward and backward forecasting processes in the blocks by facilitating the learning of parametric local receptive fields. Preliminary results indicate that this new usage can significantly improve model performances on datasets that have longer sequences, providing a promising direction for future advancements in N-BEATS and N-HiTS.Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.Yayın Turkish sentiment analysis: a comprehensive review(Yildiz Technical University, 2024-08) Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem; Birdemir, Nuri CanSentiment analysis (SA) is a very popular research topic in the text mining field. SA is the process of textual mining in which the meaning of a text is detected and extracted. One of the key aspects of SA is to analyze the body of a text to determine its polarity to understand the opinions it expresses. Substantial amounts of data are produced by online resources such as social media sites, blogs, news sites, etc. Due to this reason, it is impossible to process all of this data without automated systems, which has contributed to the rise in popularity of SA in recent years. SA is considered to be extremely essential, mostly due to its ability to analyze mass opinions. SA, and Natural Language Processing (NLP) in particular, has become an overwhelmingly popular topic as social media usage has increased. The data collected from social media has sourced numerous different SA studies due to being versatile and accessible to the masses. This survey presents a comprehensive study categorizing past and present studies by their employed methodologies and levels of sentiment. In this survey, Turkish SA studies were categorized under three sections. These are Dictionary-based, Machine Learning-based, and Hybrid-based. Researchers can discover, compare, and analyze properties of different Turkish SA studies reviewed in this survey, as well as obtain information on the public dataset and the dictionaries used in the studies. The main purpose of this study is to combine Turkish SA approaches and methods while briefly explaining its concepts. This survey uniquely categorizes a large number of related articles and visualizes their properties. To the best of our knowledge, there is no such comprehensive and up-to-date survey that strictly covers Turkish SA which mainly concerns analysis of sentiment levels. Furthermore, this survey contributes to the literature due to its unique property of being the first of its kind.
