Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/833
  A New Hybrid ConvViT Model for Dangerous Farm Insect Detection

  Utku, Anil; Kaya, Mahmut; Canbay, Yavuz

  This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT’s self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model’s performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use.

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  AtıfScopus9·WoS5·OpenAlex11

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• MakaleYalnız künye2024/r/munzur/6356
  A Comparative Study for Privacy-Aware Recommendation Systems

  Utku, Anıl; Canbay, Yavuz

  Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.

  AtıfOpenAlex2·TR Dizin1

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• BildiriYalnız künye2022/r/munzur/2935
  Privacy Concerns and Measures in Metaverse: A Review

  Canbay, Yavuz; Utku, Anil; Canbay, Pelin; Ozbudak, F.; Sagiroglu, S.; Selcuk, A.A.

  Metaverse is the new buzzword in today's digital world. It is a new internet application and a new form of the social world that integrates new technologies such as; Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Blockchain (BC),etc. The metaverse can be seen as a virtual world that mimics the real world. Most things that can be done in the real world, such as shopping, making new friends, attending concerts, playing games, and so on, can be done differently in Metaverse. While a person who represents himself with his presence in the real world realizes this representation in the digital world with his personal data. Hence, the Metaverse exploits personal data to create and maintain this virtual world. At this point, privacy concerns of users in Metaverse occur, and Metaverse Service Providers (MSP) should take these concerns into account. This paper focuses on privacy concerns in Metaverse, presents some measures in order to minimize these concerns, and provides a comprehensive list of personal data collected and processed in Metaverse.

  AtıfScopus44·OpenAlex53

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