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- MakaleAçık Erişim2024Scopus SJRQ1Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features
Reis, Hatice Catal; Turk, Veysel; Karacur, Soner; Kurt, Ahmet Melih
A safe and healthy infrastructure is essential for humanity. Maintenance and repair of roads, which are of great importance, especially in transportation, is essential. Roads can sometimes be damaged due to natural disasters or human factors. In this study, roads damaged as a result of the natural disaster and the 6 February 2023 Gaziantep-Kahramanmaras earthquakes, which had devastating/fatal consequences, were studied. In recent years, the use of machine learning methods in the automatic detection process of road cracks has become widespread. Here, deep learning architectures generally based on multilayer convolutional neural networks have become a popular solution, mainly thanks to their high discrimination power in visual data. In this study, it was aimed to automatically detect cracks on the roads in Gaziantep (Nurdagi) and Adiyaman provinces after the earthquake. The data set used in the experimental process consists of two categories: uncracked and cracked, and was collected by the researchers. ResNet152 deep learning architecture, traditional machine learning algorithms, and an ensemble learning (EL) model were used to classify cracks. Among the machine learning algorithms used in the experiments are support vector machine (SVM), K-nearest neighbors, Adaptive Boosting, and Naive Bayes. In the experimental process, ResNet152 architecture was used for feature extraction, and classification was carried out with machine learning algorithms. In addition, the EL method has been used to improve the success of machine learning algorithms. According to experimental results, the SVM algorithm and EL method gave the most successful results with 98.68% accuracy values. The proposed method can benefit experts in the automatic classification of road cracks. The study can contribute to activating rapid coordination and transportation networks in natural disasters that cause fatal and destructive causes, such as earthquakes.
AtıfScopus19WoS16OpenAlex13İncele - MakaleYalnız künye2022Scopus SJRQ2Detection of 2D and 3D Video Transitions Based on EEG Power
Manshouri, Negin; Melek, Mesut; Kaylkcloglu, Temel
Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain's power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.
AtıfScopus6WoS4OpenAlex6İncele - MakaleYalnız künye2020Scopus SJRQ1An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching
Manshouri, Negin; Maleki, Masoud; Kayikcioglu, Temel
Despite knowing the reality of three-dimensional (3D) technology in the form of eye fatigue, this technology continues to be retained by people (especially the young community). To check the influences of the human brain's power spectrum density (PSD) pre and post a 2D and 3D movie watching, a five-member test group was arranged. In this study, electroencephalogram (EEG) was used as a neuroimaging method. EEG recordings of five individuals were taken both before and after watching 2D and 3D movies. The main framework of this study was to analyze the effects of power spectrum density (PSD) of the human brain by testing all EEG frequency bands before and after 2D and 3D movie watching, also to determine effective brain lobes, and consequently, EEG channels. After 2D/3D EEG recording, this record was divided into three stages for analysis. These stages consisted of Relax, Watching, and Rest. This benchmarking analysis included I) before and after watching the 2D movie (R2b and R2a), II) before and after watching the 3D movie (R3b and R3a), and III) after watching the 2D/3D movie (R2a and R3a). In the Relax and Rest stages, the 2D/3D EEG power differences in all channels of brain regions for the five EEG bands, including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), were analyzed and compared. The PSD based on short-time Fourier transform (STFT) was used to select the dominant bands in this study. Feature extraction was performed on the preprocessed EEG signals using STFT and discrete wavelet transform (DWT). In the 2D analysis, δ, θ, α, and β acted as dominant bands, in 3D, δ, θ, and α were dominant bands, and in 2D/3D, δ and α were selected as meaningful and dominant bands. Partial least-squares regression (PLSR) and support vector machine (SVM) classification algorithms were considered in order to classify the obtained R2a and R3a EEG signals. After the dominant band selection, the correct choice of effective channel combinations in these bands resulted in the percentage of high success of classifiers for Stage III. Taking into account the optimum number of channels, the best percentage of classification accuracy for the average of PLSR and SVM classification results by considering the best channels of SVM and DWT feature extraction method is 91.83% and 80.73% respectively.
AtıfScopus56WoS43İncele