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Yönetim Bilişim Sistemleri Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/6195

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Browsing Yönetim Bilişim Sistemleri Bölümü Yayın Koleksiyonu by Author "51704"

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    Article
    Citation Count: Nanehkaran, Yaser A...et al. (2023). "Deep learning method for compressive strength prediction for lightweight concrete", Computers and Concrete, Vol. 32, No. 3, pp. 327-337.
    Deep learning method for compressive strength prediction for lightweight concrete
    (2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatlı, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Chen, Junde; Derakhshani, Reza; 51704
    Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code’s requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
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    Citation Count: Binglaw, Ftayem; Koyuncu, Murat; Pusatlı, Tolga. "Security Requirements in IoT Environments", International Conference on Internet of Things as a Service, pp. 84-96, 202.
    Security Requirements in IoT Environments
    (2022) Binglaw, Ftayem; Koyuncu, Murat; Pusatlı, Tolga; 51704
    The Internet of Things (IoT) is a relatively new concept as it connects things (or objects) that do not have high computational power. The IoT helps these things see, listen, and take action by interoperating with minimal human intervention to make people’s lives easier. However, these systems are vulnerable to attacks and security threats that could potentially undermine consumer confidence in them. For this reason, it is critical to understand the characteristics of IoT security and their requirements before starting to discuss how to protect them. In this scope, the present work reviews the importance of security in IoT applications, factors that restrict the use of traditional security methods to protect IoTs, and the basic requirements necessary to judge them as secure environments.