5 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 5 / 5
Yayın Designing an interactive non-linear documentary contributed by public participation suburbs of Istanbul(Association for Computing Machinery, 2020-07-03) Çevikayak Yelmi, Pınar; Bayar, TuluSuburbs of Istanbul is a web-based interactive documentary project that examines the identity of suburban neighborhoods in Istanbul through the participation of its residents via online submission of their visual and written stories. Public participation also led the design process and helped prototype the interfaces of this non-linear documentary. This project aims to contribute to the field of interactive documentary and non-linear storytelling by integrating participatory design techniques in the prototyping process of documentary interfaces. Involving public both as content providers and as active decision makers in design process lead to a more genuine outcome with a human-centered approach. This project also intends to create an interactive experience to provide a greater insight into rapidly changing lifestyles of Turkish people, to provide a global context to the stories presented, and to generate widespread awareness of issues surrounding suburban lifestyles across the world.Yayın Statistical analysis of bus transportation networks of Istanbul(World Scientific Publishing Co Pte Ltd, 2016) Çoban, Veysel; Atan, Sabri TankutTransportation networks such as railway, airport and bus networks are the real-world networks whose inherent statistical properties characterize and differentiate the networks. In order to understand the network characteristics of bus transportation networks (BTNs) of Istanbul, we analyzed its network properties such as degree distributions, clustering coefficients and assortativity. BTNs of Istanbul is defined into three networks as the existence and nonexistence of the metrobus and existence of third- bridge. They are also graphically represented within C-, L- and P-Space topologies that are defined with the connection of the bus stops or routes. Statistical results obtained from network properties reflected the characteristics of the BTNs of Istanbul and give an information about the effects of the metrobus lines and third bridge on the BTNs in Istanbul.Yayın Sentiment analysis for hotel reviews in Turkish by using LLMs(Institute of Electrical and Electronics Engineers Inc., 2024) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta SaidThe field of sentiment analysis plays a pivotal role in consumer decision-making and service quality improvement within the hospitality industry. This study explores the application of Large Language Models (LLMs) for sentiment analysis of Turkish hotel reviews, contributing to the understanding of customer feedback and satisfaction. We created a dataset of 5,000 reviews by translating an English corpus into Turkish, which was then utilized to evaluate the performance of a state-of-the-art Turkish language model, TURNA. The study demonstrates that LLMs, particularly TURNA, outperform traditional machine learning algorithms and other advanced models in sentiment classification tasks, achieving an accuracy of 99.4%. This research underscores the potential of LLMs to enhance the accuracy of sentiment analysis, offering valuable insights for the tourism and hospitality sectors. The findings contribute to the ongoing evolution of sentiment analysis methodologies and suggest that LLMs can significantly improve t he understanding a nd processing of customer feedback in Turkish hotel reviews.Yayın LLM’leri kullanarak otel incelemelerini görüntü manipülasyonu ile görselleştirme(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta SaidDijital çağda müşteri yorumları, özellikle otelcilik sektöründe, karar verme sürecinde önemli bir rol oynamaktadır. Metin tabanlı yorumlar değerli bilgiler sunsa da, potansiyel müşteriler genellikle öznel ifadeleri doğru şekilde yorumlamakta zorlanmaktadır. Araştırmalar, görsel temsillerin anlaşılırlığı artırdığını ve kullanıcı etkileşimini güçlendirdiğini göstermektedir. Bu çalışma, metin tabanlı görüntü manipülasyonu ile yazılı otel yorumlarını orijinal otel görselleri üzerinde değişiklikler yaparak görsel incelemelere dönüştürmeyi amaçlamaktadır. Stable Diffusion modeli kullanılarak yazılı otel yorumları girdileriyle otel odası görüntüleri manipüle edilmiştir. Manipüle edilen görsellerin değerlendirilmesinde SSIM (Structural Similarity Index Measure) ve PSNR (Peak Signal-to-Noise Ratio) metrikleri uygulanmıştır. Ayrıca, manipüle edilmiş ve orijinal görüntü örnekleri karşılaştırmalı olarak sunulmuştur. Sonuçlar, modelin küçük ölçekli değişikliklerde başarılı olduğunu, ancak büyük değişikliklerde kalite kaybı yaşadığını göstermektedir.Yayın Retinal disease classification from bimodal OCT and OCTA using a CNN-ViT hybrid architecture(Institute of Electrical and Electronics Engineers Inc., 2025-09-21) Aydın, Ömer Faruk; Tek, Faik Boray; Turkan, YaseminRetinal diseases are the leading cause of vision impairment and blindness worldwide. Early and accurate diagnosis is critical for effective treatment, and recent advances in imaging technologies such as Optical Coherence Tomography (OCT) and OCT Angiography (OCTA), have enabled detailed visualization of the retinal structure and vasculature. By leveraging these modalities, this study proposes an advanced deep learning architecture called MultiModalNet for automated multi-class retinal disease classification. MultiModalNet employs a dual-branch design, where OCTA projection maps are processed through a ResNet101 encoder, and cross-sectional slices from the OCT volume (B-scans) are analyzed using a Vision Transformer (ViT-Large). The extracted features from both branches were fused and passed through the fully connected layers for the final classification. Evaluated on the 3-class OCTA-500 dataset, which includes Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR), and Normal cases, the proposed model achieved state-of-the-art classification accuracy of 94.59 percent, significantly o utperforming single-modality baselines. This result highlights the effectiveness of integrating vascular and structural information to improve the diagnostic performance. The findings suggest that hybrid multi-modal deep learning approaches can play a transformative role in computer-aided ophthalmology, enhancing both clinical decision-making and screening workflows.
