Browsing by Author "Ünal, Uğur"

Now showing 1 - 3 of 3

Citation Count: 1
Open data in agriculture: Sustainable model development for hazelnut farms using semantics
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydın, Mehmet Nafiz; Ünal, Uğur; Aydın, Mehmet Nafiz
Turkey accounts for 75% of the global hazelnut production and 70-75% of the exportation. Taking into account the socioeconomic importance of hazelnut, the stakeholders of hazelnut domain still have problems such as availability, meaningful, accuracy of the hazelnut related data. Providing data to stakeholders is crucial for sustainable agricultural activities. This data should be freely available to everyone to use and republish. With the aforementioned reasons "Open Data" is an efficient way in Turkish Agriculture.In this paper, we shall investigate the open data term and semantics in the context of hazelnut data management. In addition, a data processing model with regard to agricultural open data is proposed.
Citation Count: 28
Random CapsNet forest model for imbalanced malware type classification task
(Elsevier, 2021) Dağ, Hasan; Ünal, Uğur; Dağ, Hasan
Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the machine learning-based models. However, traditional deep learning architectures components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature.
Citation Count: 3
Website category classification using fine-tuned BERT language model
(Institute of Electrical and Electronics Engineers Inc., 2020) Dağ, Hasan; Demirkıran, Ferhat; Ünal, Uğur; Dağ, Hasan
The contents on the Word Wide Web is expanding every second providing web users a rich content. However, this situation may cause web users harm rather than good due to its harmful or misleading information. The harmful contents can contain text, audio, video, or image that can be about violence, adult contents, or any other harmful information. Especially young people may readily be affected with these harmful information psychologically. To prevent youth from these harmful contents, various web filtering techniques, such as keyword filtering, Uniform Resource Locator (URL) based filtering, Intelligent analysis, and semantic analysis, are used. We propose an algorithm that can classify websites, which may contain adult contents, with 67.81% (BERT) accuracy among 32 unique categories. We also show that a BERT model gives higher accuracy than both the Sequential and Functional API models when used for text classification.