Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/2291
  Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals

  Uyanik, Hakan; Kokum, Mehmet; Senturk, Erman; Freeshah, Mohamed; Ozcelik, Salih T. A.; Halil Akpinar, Muhammed; Celik, Serenay; Sengur, Abdulkadir

  This study proposes a novel deep learning approach for predicting significant earthquakes (Mw ≥ 5.0 ) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices. ConvMixer is a lightweight CNN architecture that blends spatial and channel information using depthwise convolutions and pointwise layers. Inspired by vision transformers, it offers efficient image classification with fewer parameters and high performance. We developed a multi-input one-dimensional convolutional mixer (MI-1D-ConvMixer) model to classify TEC data from the preceding five consecutive days as either precursory to an earthquake on the 6th day or normal. The model incorporates six inputs: five 1D TEC signals and one 1D space-weather index array, including the global geomagnetic index (Kp), storm duration distribution (Dst), sunspot number (R), geomagnetic storm index (Ap-index), solar wind speed (Vsw), and solar activity index (F10.7) are also utilized to reveal non-seismic related pre-earthquake ionospheric variations. Our methodology involves two stages: 1) a preprocessing stage to enhance TEC signals and 2) an end-to-end training of the MI-1D-ConvMixer model. The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. We used 196 earthquakes data from Turkey from 2010-2023, and TEC data from the TNPGN-Active GNSS stations. The dataset was split into 75% for training and 25% for testing. Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. This research contributes to the field by introducing a novel deep learning architecture specifically designed for integrating TEC and space weather data for earthquake prediction. Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.

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  AtıfScopus7·OpenAlex5

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1945
  Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches

  Yilmaz, Mehmet; Yalcin, Erkut; Demir, Fatih; Ozdemir, Ahmet Munir; Atar, Muhammed; Gunes, Aysegul; Furtana Yalcin, Beyza; Cambay, Ertugrul

  This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.

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  AtıfScopus6·OpenAlex4

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• MakaleAçık Erişim2023Scopus SJRQ1/r/munzur/2778
  An Integrated Approach for Assessing Suppliers Considering Economic Sustainability Innovation

  Ahmadi, Hadi Badri; Pamucar, Dragan; Pourhejazy, Pourya; Kaya, Sema Kayapinar; Liou, James J. H.

  Innovation plays a key role for improving the sustainability performance of corporations. A truly sustainable innovative firm needs to take the social, environmental, and economic aspects of sustainable innovation into consideration. However, limited studies have investigated the economic aspect of sustainable innovation, especially in the context of supplier evaluation from emerging economies. To deal with this issue, this manuscript puts forward a criteria decision framework for evaluating economic sustainable innovative suppliers. A new methodology based on fuzzy Full Consistency Method (FFUCOM) and Improved Combinative Distance-based Assessment (ICODAS) is developed and extended using interval rough Dombi-Bonferroni operators. In the developed approach, the FFUCOM is employed to determine the criteria weights, and the ICODAS is responsible for assessing and ranking the suppliers. A case study from the manufacturing sector of an emerging economy is considered for validating the developed framework and decision model. The findings introduce the 'financial resumption of products' as the most critical economic innovation criterion for evaluating suppliers. This article significantly helps decision-makers and practitioners to better comprehend the underpinnings of sustainable innovation economic dimension, particularly when assessing their suppliers. Furthermore, the applicability and validity of the proposed model was confirmed by the results of sensitivity analysis. Findings demonstrated that the proposed method is a solid and useful MCDM framework, and its outputs are precise and acceptable.

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  AtıfScopus9·OpenAlex6

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• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2885
  Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network

  Imak, Andac; Celebi, Adalet; Siddique, Kamran; Turkoglu, Muammer; Sengur, Abdulkadir; Salam, Iftekhar

  Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.

  AtıfScopus107

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• MakaleAçık Erişim2021Scopus SJRQ1/r/munzur/2769
  A Method to Determine the Most Suitable Initial Conditions of Chaotic Map in Statistical Randomness Applications

  Acikkapi, Mehmet Sahin; Ozkaynak, Fatih

  The processes and systems in the real world actually contain order and symmetry. Understanding these order and symmetrical behavior has been a common effort of scientists. Chaos theory has been an interesting topic to understand these order and symmetrical behavior. One of the most important reasons for this interest is the rich dynamics of chaotic systems. A remarkable application of these systems is statistical randomness. Especially in random number generator designs based on discrete time chaotic systems, an important design parameter affecting the success of the generator (statistical randomness properties) is the initial conditions of chaotic maps. Obtaining different initial conditions that will meet the statistical requirements is important in terms of generating different random number sequences. In order to determine the initial conditions, an algorithm that updates the initial conditions depending on the number of successful statistical tests is proposed. Although the proposed design approach is similar to a back propagation neural network, it has a unique design approach. The NIST SP 800-22 test suite has been used to analyze the statistical properties of the proposed generator. It is known that the NIST SP 800-22 test suite is a hypothesis test. Therefore, in order to show the success of the proposed method in the best way, various additional analysis studies have been carried out proving that the generator outputs have a uniform distribution. Using the proposed method, six different initial conditions have been determined that provide statistical random properties for the discrete-time chaotic systems known as logistic map and tent map. 1,000,000 bits have been generated using the obtained initial conditions. These bit values are then converted to decimal values between 0-15. It is observed that the obtained numbers have a uniform distribution. These outputs are thought to be applicable in many areas such as games, simulation, modeling, determination of optimization parameters and cryptology. It is shown in a practical application for cryptographic substitution-box designs to examine the success of the outputs.

  AtıfScopus34·WoS23·OpenAlex29

  İncele
• MakaleAçık Erişim2019Scopus SJRQ1/r/munzur/3228
  Side-Channel Analysis of Chaos-Based Substitution Box Structures

  Acikkapi, Mehmet Sahin; Ozkaynak, Fatih; Ozer, Ahmet Bedri

  There is a theoretically strong relationship between chaos and cryptology. In practice, one of the most successful applications of this relationship is chaos-based s-box structures. However, the performance metrics of chaos-based s-box designs are worse compared to those for s-box structure based on algebraic techniques used in modern encryption algorithms, such as the AES algorithm. What is the reason for using chaos-based s-box structures, even though they have worse cryptographic characteristics? Researchers claim that they may be an alternative defense against implementation attacks, especially side-channel analyses. However, no studies have supported or refuted this claim so far. In this paper, side-channel analyses have been performed for two different chaos-based s-box structures. These two s-box structures have been selected for having with the best and worst performance measurements for s-box structures previously proposed in the literature. The results were compared with the AES s-box structure. Analysis of the results showed that chaos-based s-box structures are more resistant against side-channel attacks. Therefore, chaos-based designs may be an alternative defense against implementation attacks, as alleged. However, both algebraic and chaos-based s-box designs have been observed to be insecure if the attacker has more than 30 plaintexts in the side channel analyses. These results show that implementation analysis studies are required in the chaos-based cryptology literature.

  AtıfScopus48·WoS42·OpenAlex51

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