Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Understanding the Impact of Deep Learning Models on Building Information Modeling Systems: a Study on Generative Artificial Intelligence Tools †(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Yönder,V.M.The power of the relationship between building information modeling (BIM) systems and advanced artificial intelligence models holds considerable weight for users of BIM. This relationship allows the generation, analysis, and deduction of insights from substantial construction digital data. This research explores the relationship between generative artificial intelligence (generative AI), deep neural nets, and the BIM systems, including its users. This study examines the correlation between generative artificial intelligence and BIM methodology by conducting a case study. Furthermore, this paper investigates the conceptual and practical use of generative AI components (e.g., text-to-image models, diffusion networks, deep neural networks, large language model, and generative adversarial network) in BIM systems via bibliometric analysis. © 2023 by the author.Conference Object Citation - Scopus: 1Classification of Turkish and Balkan House Architectures Using Transfer Learning and Deep Learning(Springer Science and Business Media Deutschland GmbH, 2024) Yönder,V.M.; İpek,E.; Çetin,T.; Çavka,H.B.; Apaydın,M.S.; Doğan,F.Classifying architectural structures is an important and challenging task that requires expertise. Convolutional Neural Networks (CNN), which are a type of deep learning (DL) approach, have shown successful results in computer vision applications when combined with transfer learning. In this study, we utilized CNN based models to classify regional houses from Anatolia and Balkans based on their architectural styles with various pretrained models using transfer learning. We prepared a dataset using various sources and employed data augmentation and mixup techniques to solve the limited data availability problem for certain regional houses to improve the classification performance. Our study resulted in a classifier that successfully distinguishes 15 architectural classes from Anatolia and Balkans. We explain our predictions using grad-cam methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.