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Yayın Multi-task learning on mental disorder detection, sentiment detection and emotion detection(Işık Üniversitesi, 2024-02-12) Armah, Courage; Dehkharghani, Rahim; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Computer Science Engineering Master ProgramSuicidal behavior is a global cause of life-threatening injury and most of the time, death. Mental disorders such as depression, anxiety, and bipolar are prevalent among the youth in recent decades. Social media are popular platforms for individuals to post their thoughts and feelings on. Extracting people’s sentiments and feelings from such online platforms would help detect mental disorders of the users to treat them before it becomes too late. This thesis investigates the use of multi-task learning systems and single-task learning techniques to estimate behaviors and mental states for early diagnosis. I used data mined from Reddit, one of the popular social media platforms that provides anonymity. Anonymity increases the chances of individuals sharing what they truly feel in their real life. The obtained results by the proposed approaches open new doors to the understanding of how multi-task systems can increase the performance of text classification problems such as depression detection, emotion detection, and sentiment analysis, trained together in a multi-task learning network when compared to their training in isolation in a single-task learning network. We used the SWMH dataset, already labeled by 5 different depression labels (depression, anxiety, suicide, bipolar, and off my chest) and then added emotion and polarity labels to it and made it publicly available for researchers in the literature. The obtained results in this study are also comparable to other approaches in the field.Yayın Deep learning techniques for building density estimation from remotely sensed imagery(Işık Üniversitesi, 2019-04-05) Süberk, Nilay Tuğçe; Ateş, Hasan Fehmi; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıThis thesis is about point-wise estimation of building density on the remote sensing optical imageries by applying deep learning methods. The goal of the project is to reduce mean square error of the estimated density by applying architectural modi?cations on the deep learning network and using augmented training data. Recently, deep learning is one of popular ?eld of science and convolutional neural networks (CNNs) are well-known deep neural network. Recent studies indicate that some of the convolutional neural networks are highly e?ective in large scale image works such as recognition, semantic segmentation. There has been limited research in using deep networks to learn urbanization characteristics from remote sensing images. Remote sensing images could be used for regression problems and building density estimation is one of them. Building density information provides knowledge for real estate agents and urban planners, estimating disaster risk areas, environment protection and resource allocation. Our method provides a cheap and fast solution to these needs when there is no cadastral information. The main objective of this thesis is to achieve fast and accurate local building density estimation using high resolution remote sensing images. Deep learning methods based on CNN are applied in this project. Pre-trained visual geometry group (VGG-16) and fully convolutional network (FCN) are tested as convolutional neural network. We tested three di?erent modi?ed networks and then applied data augmentation in the train data to reduce mean square error value. The networks that we have performed simpli?ed original VGG-16 network for regression, VGG-16 network with sigmoid layer added and simpli?ed VGG-16 network with sigmoid layer. The best result (lowest mean square error) is obtained from sigmoid layer added VGG-16 network with data augmentation. Sigmoid layer added VGG-16 network gives us (?0,084) RMSE on building density estimation with the augmented train dataset. Original VGG-16 network gives (?0,105) RMSE, sigmoid layer added VGG-16 network gives (?0,095) RMSE and sigmoid layer added simpli?ed VGG-16 network gives (?0,090) RMSE on building density estimation with the small train dataset. FCN is one of the ideal network for classi?cation tasks so we have also applied fully convolutional network result to compare our results with its result. We have modi?ed the network to perform building density estimation in addition to semantic segmentation. The root mean square error of FCN is (?0,084) and our best result (lowest mean square error) is also (?0,084) RMSE at the same iteration number. Our results show that fast and accurate building density estimation is possible by using vanilla CNNs. Sigmoid layer addition, simpli?cation of the network for small dataset and data augmentation improves accuracy in the regression. Data augmentation is the most e?ective method to reduce RMSE in this thesis.Yayın A theoretical comparison of ResNet and DenseNet architectures on the subject of shoreline extraction(Işık Üniversitesi, 2020-09-23) Ecevit, Mert İlhan; Çavdaroğlu, Gülsüm Çiğdem; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Enformasyon Teknolojileri Yüksek Lisans ProgramıToday's Deep Learning technologies provides numerous approaches on the subject of convolutional networks. These approaches serve researchers to train datasets and generate wanted results from these datasets. Each CNN architecture has its own strong points and weak sides. Because of this situation a comparison between these architectures is a valuable asset. Image processing is a method that is frequently used to process remotely sensed data in remote sensing studies.. Between current architectures, RESNET and DENSENET architectures are chosen to be used by Dr. Çavdaroğlu for her project on TÜBİTAK. The result of this comparison will be used in that project in order to apply most ecient architecture. This thesis is written to draw outlines of RESNET and DENSENET and create a foresight for further projects which can be supported by this thesis. In order to achieve an accurate image recognition process in remote sensing domain, a preliminary research is requisite. As a research thesis this work serves the purpose of learning manner of works, performance indicators of RESNET and DENSENET convolutional networks. The result of this research will create a baseline for an academical project. At the other hand, comparison of these two convolutional network approaches provides information to decide which approach is more suitable for remote sensing projects depending upon the subject of the project. For future works on Remote Sensing this thesis work will serve a guideline and reason for preference. The presented thesis work has been developed as the technical feasibility of the 3501 TÜBITAK Project named "Uydu Görüntülerinden Kıyı Sınırlarının Derin Öğrenme Yöntemleri ile Otomatik Çıkarımı", applied by Dr. G. Çiğdem Çavdaroğlu, and the thesis results will be applied within the scope of the Project after the project acceptance.Yayın Image super resolution using deep learning techniques(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-09-02) El Ballouti, Salah Eddine; Eskil, Mustafa Taner; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringImage SR using Deep Learning Techniques has become a critical area of research, with significant progress in improving image quality and detail. This thesis examines and contrasts eight advanced deep learning-based SR methods: CARN, EDSR, ESPCN, RCAN, RDN, SRCNN, SRGAN, and VDSR, using the DIV2K dataset. The evaluation covers multiple aspects to offer a thorough understanding of each method's effectiveness, efficiency, and structure. Performance measurements such as PSNR and SSIM are utilized for evaluating the fidelity of super-resolved images. Computational efficiency is evaluated based on inference time and memory requirements. Training time is analyzed, taking into account the speed of convergence for training on the DIV2K dataset. Model complexity is examined, exploring architectural details such as network depth, and the integration of specialized elements like residual blocks and attention mechanisms. Additionally, the thesis explains in a clear and detailed manner the trade-offs between performance and complexity, discussing whether more complex architectures deliver significantly better results compared to simpler models and whether the computational cost justifies the improvements. Finally, a qualitative comparison is conducted to emphasize the strengths and weaknesses of each technique. Through this comprehensive analysis, this thesis offers insights into the field of deep learning-based image SR, assisting researchers and practitioners in choosing the most appropriate method for various applications.Yayın Supervised decision making in forex investment using ML and DL classification methods(Işık Üniversitesi, 2023-07-20) Jiroudi, Abdullah; Eskil, Mustata Taner; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringThe suggested trading system offers an approach that takes into account the complexity and high trading volume of the foreign exchange (FX0) market. Its main objective is to address the challenges faced by traders in the GBP/JPY currency pair and assist them in making quick decisions. To achieve this, machine learning and deep learning techniques are integrated to propose a trading algorithm. The proposed algorithm works by combining data from different time intervals. The Long Short-Term Memory (LSTM) model is used to predict indicator values, while the XGBoost classifier is employed to determine trading decisions. This method aims to adapt to rapidly changing patterns in the forex market and enables the detection of subtle changes in price dynamics through a sliding window training approach. Experiments conducted have shown promising results for the suggested trading system. Positive outcomes have been obtained in terms of capital growth and prediction accuracy. However, since this method is highly risky and requires further development in terms of risk management, the inclusion of risk management techniques and algorithm optimization is targeted. This study contributes to the improvement of trading strategies while bridging the gap between researchers and traders. It also demonstrates the potential of machine learning and deep learning techniques to enhance decision-making processes in financial markets. This trading system offers traders a range of advantages. The utilization of machine learning and deep learning techniques enables rapid analysis of large amounts of data and decision-making capabilities. Additionally, by combining data from different time intervals, it becomes possible to evaluate long-term trends and short-term fluctuations more effectively. In conclusion, the suggested trading system empowers traders to be competitive in the forex market and achieve better outcomes. Furthermore, it contributes to the increased utilization of machine learning and deep learning techniques in financial markets and encourages further research in the field.Yayın Tuş vuruşlarına dayalı kimlik doğrulama yöntemleri: evrimi, zorlukları ve gelecek yönelimlerinin kapsamlı bir incelemesi(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-09-23) Gündoğan, Nebil Vural; Çeliktaş, Barış; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Siber Güvenlik Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in CybersecurityTuş vuruşu (keystroke) ile kimlik doğrulama, bireylerin klavye kullanımındaki yazım ritimlerini ve zamanlama desenlerini analiz ederek kimlik doğruluğunu sağlayan sofistike bir davranışsal biyometrik yöntemdir. Bu yöntemin dikkat çekici avantajları arasında, kullanıcıdan ek bir işlem gerektirmemesi, herhangi bir ek donanım ihtiyacı doğurmaması ve maliyet etkinliği bulunmaktadır. Gelişmiş bilişim altyapılarında ve güvenlik hassasiyeti yüksek uygulamalarda, kullanıcıyı tanımak için sürekli izleme ve ikinci faktör doğrulama gerekliliği artarken, tuş vuruşu temelli yöntemler bu gereksinimlere düşük maliyetli ve sezgisel bir çözüm sunmaktadır. Bu çalışma, tuş vuruşu dinamik kimlik doğrulama yöntemleri ile ilgili literatürü sistematik olarak incelemektedir. İlk olarak, farklı setler ve özellikleri gözden geçirilmekte, ardından makine öğrenimi (ML), derin öğrenme (DL) ve hibrit modeller performans, güvenlik ve kullanılabilirlik açısından karşılaştırılmaktadır. Ayrıca, mevcut metodolojiler OWASP Kimlik Doğrulama Hile Sayfası aracılığıyla sunulan kılavuz bağlamında ele alınarak, güvenlik açıkları ve olası saldırılar analiz edilmektedir. Hibrit modellerin, daha yüksek doğruluk ve üstün dayanıklılık açısından otonom ML veya DL yöntemlerinden daha iyi performans gösterdiği ortaya çıkmaktadır. Gelecekteki yönelimler açısından, federatif öğrenme (FL), açıklanabilir yapay zekâ (XAI) ve multimodal biyometrik füzyon, gizlilik, açıklana bilirlik ve platformlar arasında genelleştirile bilirlik açısından daha sağlam çözümler üretme konusunda umut vaat etmektedir. Değerlendirme kapsamında, söz konusu modellerin masaüstü sistemlerde, web tabanlı platformlarda ve mobil cihazlarda sergilediği performanslar karşılaştırmalı olarak analiz edilmiştir. Elde edilen veriler, bazı modellerin yüksek doğruluk oranlarına ulaştığını ancak kullanıcı deneyiminde sürtünme (friction) oluşturduğunu; diğer modellerin ise kullanıcı dostu yapısına karşın daha düşük güvenlik sunduğunu ortaya koymaktadır. Bu bağlamda, sistem seçiminde güvenlik, doğruluk ve kullanıcı konforu arasında bir denge kurulması gerektiği sonucuna varılmıştır. Bu bağlamda önerdiğimiz hibrit doğrulama çerçevesi, derin sinir ağlarının sınıflandırma yeteneklerini anomali tespit teknikleriyle birleştirmekte ve bağlamsal farkındalığa sahip özellik çıkarımı ile uyarlanabilir eşikleme mekanizmaları kullanmaktadır. Böylelikle, modelimiz hem yeni kullanıcı davranışlarına uyum sağlayabilmekte hem de sahtecilik girişimlerine karşı yüksek hassasiyetle yanıt verebilmektedir. Ayrıca, önerilen çerçevenin farklı kullanım bağlamlarında—örneğin sürekli oturum denetimi veya ikinci faktör doğrulama senaryolarında—uygulanabilirliği değerlendirildiğinde, sistemin ölçeklenebilirliği ve uygulama kolaylığı da ön plana çıkmaktadır. Sonuç olarak, elde edilen bulgular, tuş vuruşu doğrulama sistemlerinin, özellikle diğer biyometrik yöntemlerle bütünleştiğinde veya bağlamsal verilerle desteklendiğinde, yüksek güvenlik gerektiren uygulamalarda etkin, uyarlanabilir ve kullanıcı dostu bir çözüm sunduğunu göstermektedir. Çalışma sadece literatürde bulunan yöntemlerin kapsamlı bir karşılaştırmasını yapmakla kalmayıp, aynı zamanda gelecekteki çalışmalarda metodolojik seçimler için bir kılavuz da çizmektedir.
