Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2025Scopus SJRQ1/r/bayburt/11549
  Multi-input hybrid face presentation attack detection method based on simplified Xception and channel attention mechanism

  Günay Yılmaz, Asuman; Turhal, Uğur; Nabiyev, Vasif

  Currently, biometric recognition systems, especially facial recognition systems, are frequently used for person authentication. Increasing facial image and video sharing on social media makes these systems vulnerable to attacks. For this reason, there is an increasing need for sensitive face presentation attack (FPA) detection systems. In this paper, a lightweight multi-input hybrid deep convolutional neural network model was proposed for FPA detection. For this purpose, a simplified version of the widely used Xception network was developed. This model was extended via squeeze and excitation blocks to weight the data in the feature encoding channels. In addition, a simple residual network architecture with attention blocks was designed. The FPA detection performances of these models were subsequently examined in cases where the input data consisted of the raw images or cropped facial images. According to the results, a deep learning architecture with three parallel connections was proposed. The raw images, cropped facial images, and face-weighted multi-color multi-level local binary pattern features were given as inputs to the proposed model. Therefore, a multi-input hybrid FPA detection model was created using both hand-crafted features and deep features. The proposed architecture has approximately 82% fewer parameters than the original Xception network does. The experimental results on the benchmark CASIA and REPLAY-ATTACK datasets demonstrate the model's effectiveness, achieving a 1.53% equal error rate (EER) on the CASIA dataset and 0.00% EER with a 0.07% half total error rate (HTER) on the REPLAY-ATTACK dataset. These results outperform many state-of-the-art methods while requiring significantly fewer computational resources, making the approach suitable for deployment in resource-constrained environments.

  AtıfScopus5·WoS4·OpenAlex2

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/bayburt/10894
  Energy-driven TBM health status estimation with a hybrid deep learning approach

  Li, Yongsheng; Zhang, Limao; Pan, Yue; Tapkın, Serkan; Song, Xieqing

  This study provides a hybrid deep learning approach that enables the researchers to estimate the Tunnel Boring Machine (TBM) health status during excavation process using energy consumption data. By analyzing the energy forms and paths in the tunneling process, the key energy factors' consumption performance behavior affecting TBM safety is identified. The Ensemble Empirical Mode Decomposition (EEMD) method and a Convolution neural network-Long short term memory-Depth Neural Network (CNN-LSTM-DNN, CLDNN) model are integrated for Health Performance Parameter (HPP) prediction. A classification method is constructed by the Generative Adversarial Imputation Networks (GAIN) model for health status estimation. The SHapley Additive exPlanations (SHAP) method is performed to quantify the relationships between energy consumption and system health status. A tunneling case in Singapore is used to test the effectiveness of the developed methodology. It is found that (1) the proposed method provides TBM cutter wear and cooling oil prediction with a R-square (R2) of 0.8591 and 0.8727, respectively, and (2) classification results indicate that the estimation F1-score of 0.9863 can be achieved based on health status evaluation indicator (HSEI). The novelty of the proposed approach lies in its capabilities of (1) fully extracting and utilizing the energy consumption information in every regard to predict TBM health performance; (2) providing a good one-step-ahead health status estimation for the TBM excavation process. Therefore, this study proposes a feasible direction for the application of energy consumption data in TBM health status estimation.

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  AtıfScopus17·WoS15·OpenAlex17

  İncele
• MakaleYalnız künye2015Scopus SJRQ1/r/bayburt/12264
  A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects

  Altuntas, Serkan; Dereli, Türkay

  The aim of this article is to propose a novel approach based on a method called Decision-Making Trial and Evaluation Laboratory (DEMATEL) and patent citation analysis for prioritizing the portfolio of investment projects. The proposed approach reflects the government's perspective and takes into account two important prioritization criteria. They are: (i) the decreasing rate of foreign trade deficits in the country and (ii) the potential to attract new investments which are not considered in the literature before. A case study from Turkey is presented to demonstrate the benefits of the proposed approach. The findings show that the proposed approach can be used easily and efficiently to prioritize investment projects among available project proposals.

  AtıfScopus118·WoS92

  İncele
• MakaleAçık Erişim2012Scopus SJRQ1/r/bayburt/13990
  Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation

  Altuntas, Serkan; Selim, Hasan

  Facility layout has considerable effects on the operational productivity and efficiency of a facility because of its direct effect on material handling costs. The objective of this study is to propose new weighted association rule-based data mining approaches for facility layout problem. Classic association rule-based approaches assume that each item has the same level of significance. On the other hand, in weighted association rule-based approaches, each item is assigned a weight according to its significance with respect to some user defined criteria. In this study, different weighted association rule-based data mining approaches, namely MINWAL(O), MINWAL(W), WARM and BWARM, are applied to facility layout problem. To confirm the viability of the proposed approaches, two case studies are presented. The approaches are compared in terms of general performance criteria for the facility layout problems using simulation. This is the first study that applies weighted association rule-based data mining approaches to facility layout problem. To address the needs in practice, "demand", "part handling factor" and "efficiency of material handling equipment" are used as the weighting criteria. Then, this study differs from the previous works in that it considers the three key location factors together.

  AtıfScopus45·WoS28·OpenAlex43

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