Mardin Meslek Yüksekokulu

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Citation - WoS: 23
Citation - Scopus: 36
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(Biomedical Signal Processing and Control, 2021) Zan, Hasan; Yıldız, Abdulnasir; Said, Sherif
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Citation - WoS: 63
Citation - Scopus: 103
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
(ScienceDirect, 2022) Ahmetoglu, Huseyin; Das, Resul
Rapid developments in network technologies and the amount and scope of data transferred on networks are increasing day by day. Depending on this situation, the density and complexity of cyber threats and attacks are also expanding. The ever-increasing network density makes it difficult for cyber-security professionals to monitor every movement on the network. More frequent and complex cyber-attacks make the detection and identification of anomalies in network events more complex. Machine learning offers various tools and techniques for automating the detection of cyber attacks and for rapid prediction and analysis of attack types. This study discusses the approaches to machine learning methods used to detect attacks. We examined the detection, classification, clustering, and analysis of anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data sets used in each study we examined. We investigated which feature selection or dimension reduction method was applied to the data sets used in the studies. We presented in detail the types of classification carried out in these studies, which methods were compared with other methods, the performance metrics used, and the results obtained in tables. We examined the data sets of network attacks presented as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties encountered in machine learning applications used in network attacks and their solutions.
Physical properties of solution processable n-type Fe and Al co-doped ZnO nanostructured thin films: Role of Al doping levels and annealing
(ELSEVIER, 2018) BOZ, İbrahim; GÖKTAŞ, Abdullah; ASLAN, Ferhat; YEŞİLATA, Bülent
The role of annealing temperature and Fe and Al co-doping on structural, optical, electrical and magnetic properties of solution processable ZnO thin films were investigated. ZnO:Fe thin films fixed with 2% of typical ferrous component were obtained to examine the role of 1–10% Al doping. X-ray diffraction analyses clearly indicates that the films to be polycrystalline and preferentially oriented along the c-axis of the hexagonal wurtzite structure. The film thickness, homogeneous distribution and decreasing/increasing of grain size dependence on Al content/annealing temperature (TA) were assessed by scanning electron microscopy. X-ray photoelectron spectroscopy revealed that Al3+ and Fe2+ ions to substitute for Zn2+ without changing the wurtzite structure. A slight decrease in the optical band gap of ZnO at fixed Fe dopant and a considerable increase of the optical band gap with increased Al doping concentrations and TA were observed. The refractive index increases with the fixed Fe dopant level and then decreases by Al doping levels, whereas the extinction coefficient clearly increases depended on both of Fe and Al concentrations. The refractive index and extinction coefficient both decrease with TA. Hall measurements show n-type conductivity and the increase of charge carrier concentration by Al doping levels and TA. Magnetic studies indicate room temperature ferromagnetism in Al and Fe co-doped ZnO thin films, whereas no room temperature ferromagnetism for the Fe-doped ZnO thin films was observed. An enhanced room temperature ferromagnetism in Al and Fe co-doped ZnO thin films was observed to depend on TA.
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(2023) Zan, Hasan; Yıldız, Abdulnasır
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Citation - WoS: 12
Citation - Scopus: 14
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(ScienceDirect, 2023) Zan, Hasan; Yildiz, Abdulnasır
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen’s kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-ofthe-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Citation - WoS: 78
Citation - Scopus: 92
Fuels properties, characterizations and engine and emission performance analyses of ternary waste cooking oil biodiesel-diesel-propanol blends
(SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2019) Bencheikh, Kamel; Atabani, A. E; Shobana, Sutha; Mohammed, M. N.; Uguz, Gediz; Arpa, Orhan; Kumar, Gopalakrishnan; Ayanoglu, Abdulkadir; Bokhari, Awais
Application of biodiesel synthesized from waste-based raw materials with numerous solvents (higher chain alcohols) in diesel engines is a topic of great interest. This article examines the effect of biodiesel-diesel-propanol ternary blends. Physio-chemical properties, fatty acids composition (FAC), FT-IR, TGA, DSC, NMR along with some selected engine and emissions performance parameters were examined. Biodiesel was produced from waste cooking oil and exhibits excellent FAC that yields kinematic viscosity, cetane number, oxidation stability, higher heating value and iodine value of 3.93mm(2)/s, 58.88, 7.43 h, 39.45 MJ/kg and 64.92 g/100 g. Propanol blended biodiesel depicted an affirmative improvement in cold flow properties and decremented density. FT-IR and NMR results confirms the existence of biodiesel-diesel-propanol and prove their qualities as reliable methods. DSC and TGA results confirm that propanol reduces the onset and crystallization temperatures of the blends. Engine and emissions performance revealed that propanol addition further increased brake specific energy consumption (BSEC) and brake specific fuel consumption (BSFC) and reduced carbon monoxide (CO), exhaust gas temperature (EGT), nitrogen oxides (NOx) and smoke. This study proves the feasibility of the ternary blends with rewarding benefits in cold flow properties and densities besides acceptable engine and emissions performance results.

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