Publications by authors named "Sengul Dogan"

17 Publications

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PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition.

Comput Biol Med 2021 Sep 16;138:104867. Epub 2021 Sep 16.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

Nowadays, many deep models have been presented to recognize emotions using electroencephalogram (EEG) signals. These deep models are computationally intensive, it takes a longer time to train the model. Also, it is difficult to achieve high classification performance using for emotion classification using machine learning techniques. To overcome these limitations, we present a hand-crafted conventional EEG emotion classification network. In this work, we have used novel prime pattern and tunable q-factor wavelet transform (TQWT) techniques to develop an automated model to classify human emotions. Our proposed cognitive model comprises feature extraction, feature selection, and classification steps. We have used TQWT on the EEG signals to obtain the sub-bands. The prime pattern and statistical feature generator are employed on the generated sub-bands and original signal to generate 798 features. 399 (half of them) out of 798 features are selected using minimum redundancy maximum relevance (mRMR) selector, and misclassification rates of each signal are evaluated using support vector machine (SVM) classifier. The proposed network generated 87 feature vectors hence, this model is named PrimePatNet87. In the last step of the feature generation, the best 20 feature vectors which are selected based on the calculated misclassification rates, are concatenated. The generated feature vector is subjected to the feature selection and the most significant 1000 features are selected using the mRMR selector. These selected features are then classified using an SVM classifier. In the last phase, iterative majority voting has been used to generate a general result. We have used three publicly available datasets, namely DEAP, DREAMER, and GAMEEMO, to develop our proposed model. Our presented PrimePatNet87 model reached over 99% classification accuracy on whole datasets with leave one subject out (LOSO) validation. Our results demonstrate that the developed prime pattern network is accurate and ready for real-world applications.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104867DOI Listing
September 2021

Novel automated PD detection system using aspirin pattern with EEG signals.

Comput Biol Med 2021 10 6;137:104841. Epub 2021 Sep 6.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

Background And Objective: Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals.

Method: In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting.

Results: A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively.

Conclusion: Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104841DOI Listing
October 2021

Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model.

Comput Biol Med 2021 10 4;137:104828. Epub 2021 Sep 4.

Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey. Electronic address:

Background: Alzheimer's disease (AD) is one of the most commonly seen brain ailments worldwide. Therefore, many researches have been presented about AD detection and cure. In addition, machine learning models have also been proposed to detect AD promptly.

Materials And Method: In this work, a new brain image dataset was collected. This dataset contains two categories, and these categories are healthy and AD. This dataset was collected from 1070 subjects. This work presents an automatic AD detection model to detect AD using brain images automatically. The presented model is called a feed-forward local phase quantization network (LPQNet). LPQNet consists of (i) multilevel feature generation based on LPQ and average pooling, (ii) feature selection using neighborhood component analysis (NCA), and (iii) classification phases. The prime objective of the presented LPQNet is to reach high accuracy with low computational complexity. LPQNet generates features on six levels. Therefore, 256 × 6 = 1536 features are generated from an image, and the most important 256 out 1536 features are selected. The selected 256 features are classified on the conventional classifiers to denote the classification capability of the generated and selected features by LPQNet.

Results: The presented LPQNet was tested on three image datasets to demonstrate the universal classification ability of the LPQNet. The proposed LPQNet attained 99.68%, 100%, and 99.64% classification accuracy on the collected AD image dataset, the Harvard Brain Atlas AD dataset, and the Kaggle AD dataset. Moreover, LPQNet attained 99.62% accuracy on the Kaggle AD dataset using four classes.

Conclusions: Moreover, the calculated results from LPQNet are compared to other automatic AD detection models. Comparisons, results, and findings clearly denote the superiority of the presented model. In addition, a new intelligent AD detector application can be developed for use in magnetic resonance (MR) and computed tomography (CT) devices. By using the developed automated AD detector, new generation intelligence MR and CT devices can be developed.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104828DOI Listing
October 2021

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images.

Int J Environ Res Public Health 2021 07 29;18(15). Epub 2021 Jul 29.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore S599489, Singapore.

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
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http://dx.doi.org/10.3390/ijerph18158052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345793PMC
July 2021

Application of substitution box of present cipher for automated detection of snoring sounds.

Artif Intell Med 2021 07 6;117:102085. Epub 2021 May 6.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.

Background And Purpose: Snoring is one of the sleep disorders, and snoring sounds have been used to diagnose many sleep-related diseases. However, the snoring sound classification is done manually which is time-consuming and prone to human errors. An automated snoring sound classification model is proposed to overcome these problems.

Material And Method: This work proposes an automated snoring sound classification method using three new methods. These methods are maximum absolute pooling (MAP), the nonlinear present pattern, and two-layered neighborhood component analysis, and iterative neighborhood component analysis (NCAINCA) selector. Using these methods, a new snoring sound classification (SSC) model is presented. The MAP decomposition model is applied to snoring sounds to extract both low and high-level features. The presented model aims to attain high performance for SSC problem. The developed present pattern (Present-Pat) uses substitution box (SBox) and statistical feature generator. By deploying these feature generators, both textural and statistical features are generated. NCAINCA chooses the most informative/valuable features, and these selected features are fed to k-nearest neighbor (kNN) classifier with leave-one-out cross-validation (LOOCV). The Present-Pat based SSC system is developed using Munich-Passau Snore Sound Corpus (MPSSC) dataset comprising of four categories.

Results: Our model reached an accuracy and unweighted average recall (UAR) of 97.10 % and 97.60 %, respectively, using LOOCV. Moreover, a nocturnal sound dataset is used to show the universal success of the presented model. Our model attained an accuracy of 98.14 % using the used nocturnal sound dataset.

Conclusions: Our developed classification model is ready to be tested with more data and can be used by sleep specialists to diagnose the sleep disorders based on snoring sounds.
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http://dx.doi.org/10.1016/j.artmed.2021.102085DOI Listing
July 2021

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

Comput Biol Med 2021 07 10;134:104548. Epub 2021 Jun 10.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

Background: Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

Materials And Method: We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection.

Results: A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model.

Conclusions: The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104548DOI Listing
July 2021

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals.

Cogn Neurodyn 2021 Apr 25;15(2):223-237. Epub 2020 May 25.

College of Engineering, Department of Computer Science, Effat University, Jeddah, 21478 Saudi Arabia.

Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.
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http://dx.doi.org/10.1007/s11571-020-09601-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969686PMC
April 2021

A novel Covid-19 and pneumonia classification method based on F-transform.

Chemometr Intell Lab Syst 2021 Mar 29;210:104256. Epub 2021 Jan 29.

Institute of Biomedicine, Faculty of Medicine, University of Turku, 20520, Turku, Finland.

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.
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http://dx.doi.org/10.1016/j.chemolab.2021.104256DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844388PMC
March 2021

An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector.

Biomed Signal Process Control 2021 Jan 7;63:102173. Epub 2020 Sep 7.

Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.

In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method. leveled DWT transform, and LDOP are used together to generate low, medium, and high levels features. This feature generation network extracts 4096 features in total. RFINCA selects 95 the most discriminative and informative ones of these 4096 features. In the classification phase, kNN with leave one out cross-validation (LOOCV) is used. 95.53% classification accuracy and 94.65% unweighted average recall (UAR) have been achieved using this method. The proposed LDOP based SSC method reaches 22% better result than the best of the other state-of-the-art machine learning and deep learning-based methods. These results clearly denote the success of the proposed SSC method.
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http://dx.doi.org/10.1016/j.bspc.2020.102173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476581PMC
January 2021

1,3-Disubstituted urea derivatives: Synthesis, antimicrobial activity evaluation and in silico studies.

Bioorg Chem 2020 09 17;102:104104. Epub 2020 Jul 17.

Department of Basic Sciences, Faculty of Pharmacy, Erciyes University, Kayseri, Turkey. Electronic address:

The development of new antimicrobial compounds is in high demand to overcome the emerging drug resistance against infectious microbial pathogens. In the present study, we carried out the extensive antimicrobial screening of disubstituted urea derivatives. In addition to the classical synthesis of urea compounds by the reaction of amines and isocyanates, we also applied a new route including bromination, oxidation and azidination reactions, respectively, to convert 2-amino-3-methylpyridine to 1,3-disubstituted urea derivatives using various amines. The evaluation of antimicrobial activities against various bacterial strains, Candida albicans as well as Mycobacterium tuberculosis resulted in the discovery of new active molecules. Among them, two compounds, which have the lowest MIC values on Pseudomonas aeruginosa, were further evaluated for their inhibition capacities of biofilm formation. In order to evaluate their potential mechanism of biofilm inhibition, these two compounds were docked into the active site of LasR, which is the transcriptional regulator of bacterial signaling mechanism known as quorum sensing. Finally, the theoretical parameters of the bioactive molecules were calculated to establish their drug-likeness properties.
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http://dx.doi.org/10.1016/j.bioorg.2020.104104DOI Listing
September 2020

An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image.

Chemometr Intell Lab Syst 2020 Aug 18;203:104054. Epub 2020 May 18.

Department of Software Engineering, College of Engineering, Firat University, Elazig, Turkey.

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.
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http://dx.doi.org/10.1016/j.chemolab.2020.104054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233238PMC
August 2020

Design and synthesis of thiourea-based derivatives as Mycobacterium tuberculosis growth and enoyl acyl carrier protein reductase (InhA) inhibitors.

Eur J Med Chem 2020 Aug 4;199:112402. Epub 2020 May 4.

Department of Pharmacy, Birla Institute of Technology and Science-Pilani, 500078, Hyderabad, India.

Tuberculosis remains the most deadly infectious disease worldwide due to the emergence of drug-resistant strains of Mycobacterium tuberculosis. Hence, there is a great need for more efficient treatment regimens. Herein, we carried out rational molecular modifications on the chemical structure of the urea-based co-crystallized ligand of enoyl acyl carrier protein reductase (InhA) (PDB code: 5OIL). Although this compound fulfills all structural requirements to interact with InhA, it does not inhibit the enzyme effectively. With the aim of improving the inhibition value, we synthesized thiourea-based derivatives by one-pot reaction of the amines with corresponding isothiocyanates. After the structural characterization using H NMR, C NMR, FTIR and HRMS, the obtained compounds were initially tested for their abilities to inhibit Mycobacterium tuberculosis growth. The results revealed that some compounds exhibited promising antitubercular activity, MIC values at 0.78 and 1.56 μg/mL, combined with low cytotoxicity. Moreover, the most active compounds were tested against latent as well as dormant forms of the bacteria utilizing nutrient starvation model and Mycobacterium tuberculosis infected macrophage assay. Enzyme inhibition assay against enoyl-acyl carrier protein reductase identified InhA as the important target of some compounds. Molecular docking studies were performed to correlate InhA inhibition data with in silico results. Finally, theoretical calculations were established to predict the physicochemical properties of the most active compounds.
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http://dx.doi.org/10.1016/j.ejmech.2020.112402DOI Listing
August 2020

An exemplar pyramid feature extraction based humerus fracture classification method.

Med Hypotheses 2020 Mar 4;140:109663. Epub 2020 Mar 4.

Department of Digital Forensics Engineering, Faculty of Technology, Elazığ, Turkey.

Humerus fracture have been widely seen disease in the orthopedic clinics and classification of them is a hard process for orthopedist. The main aim of the proposed method is to classify humerus fracture by using a naïve and multileveled method. We collected a novel humerus fracture X-ray image dataset. This dataset consists of 115 images. In this paper, a novel stable feature extraction method is presented to classify humerus fractures. This method is called exemplar pyramid method and it is inspired by exemplar facial expression recognition methods. To classify humerus fractures, X-ray images were employed as input. In this study, X-ray images are resized to 512 × 512 sized image. Then, the used humerus fracture images are divided into 64 × 64 size of exemplars. To create levels, maximum pooling which has been mostly used in deep networks is used and four levels are created. Histogram of oriented gradients (HOG) and local binary pattern (LBP) are employed for feature generation. The most discriminative ones of the generated and concatenated features are selected by using ReliefF and Neighborhood Component Analysis (NCA) based two levelled feature selector (RFNCA). To emphasize success of the proposed exemplar pyramid model based feature generation, four conventional classifiers are chosen for classification and the proposed exemplar pyramid model achieved 99.12% classification accuracy by using leave one out cross validation (LOOCV). Results and tests clearly illustrates success of the proposed exemplar pyramid model based humerus fracture classification method. The results also shown that the proposed exemplar pyramid model achieved higher classification rate than Orthopedist specialized in shoulder.
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http://dx.doi.org/10.1016/j.mehy.2020.109663DOI Listing
March 2020

Discovery of hydrazone containing thiadiazoles as Mycobacterium tuberculosis growth and enoyl acyl carrier protein reductase (InhA) inhibitors.

Eur J Med Chem 2020 Feb 7;188:112035. Epub 2020 Jan 7.

Department of Chemistry, Faculty of Science, Erciyes University, 38039, Kayseri, Turkey.

Tuberculosis, caused by Mycobacterium tuberculosis, is a serious infectious disease and remains a global health problem. There is an increasing need for the discovery of novel therapeutic agents for its treatment due to the emerging multi-drug resistance. Herein, we present the rational design and the synthesis of eighteen new thiadiazolylhidrazones (TDHs) which were synthesized by intramolecular oxidative N-S bond formation reaction of 2-benzylidene-N-(phenylcarbamothioyl)hydrazine-1-carboximidamide derivatives by phenyliodine(III) bis(trifluoroacetate) (PIFA) under mild conditions. The compounds were characterized by various spectral techniques including FTIR, H NMR, C NMR and HRMS. Furthermore, the proposed structure of TDH12 was resolved by single-crystal X-ray analysis. The compounds were evaluated for their in vitro antitubercular activity against M. tuberculosis H37Rv. Among them, some compounds exhibited remarkable antimycobacterial activity, MIC = 0.78-6.25 μg/mL, with low cytotoxicity. Additionally, the most active compounds were screened for their biological activities against M. tuberculosis in the nutrient starvation model. Enzyme inhibition assays and molecular docking studies revealed enoyl acyl carrier protein reductase (InhA) as the possible target enzyme of the compounds to show their antitubercular activities.
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http://dx.doi.org/10.1016/j.ejmech.2020.112035DOI Listing
February 2020

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Med Hypotheses 2020 Jan 10;134:109519. Epub 2019 Dec 10.

Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey. Electronic address:

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation.
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http://dx.doi.org/10.1016/j.mehy.2019.109519DOI Listing
January 2020

A novel local senary pattern based epilepsy diagnosis system using EEG signals.

Australas Phys Eng Sci Med 2019 Dec 3;42(4):939-948. Epub 2019 Sep 3.

Department of Digital Forensic Engineering, Technology Faculty, Firat University, Elazig, Turkey.

Epilepsy is a critical and widely seen neurological disorder for people and electroencephalogram (EEG) signals are used to diagnose epilepsy. To accurately diagnose epilepsy, distinctive features of the EEG signals should be extracted. Therefore, a novel texture descriptor is presented for distinctive feature extraction in this study and an EEG recognition method is proposed. The proposed method consists of four main phases. These are feature extraction, feature concatenation, feature reduction and classification. Firstly, the EEG signal is divided into 1 × 25 size of overlapping blocks and these blocks are converted to 2 dimensional blocks with size of 5 × 5. Because, the proposed novel local senary pattern (LSP) uses 5 × 5 size of blocks for feature extraction. 1536 Features are extracted using the proposed LSP. The proposed LSP is used ternary function to extract features and as we know that the main problem of the ternary function is to find optimal threshold value. Therefore, we used 10 threshold values by using standard deviation function and 1536 × 10 = 15,360 features are extracted from an EEG signal. In the feature combining phase, these features are concatenated. In order to reduce these features, a neighborhood component analysis based feature reduction method is used. In the classification phase support vector machine, k nearest neighborhood, quadratic discriminant analysis and linear discriminant analysis are utilized as the classifiers. To test success of the proposed method, the widely used EEG signals dataset which is Bonn University EEG database is used and 7 cases are defined for testing using this database and the proposed method achieved 93.0% classification accuracy for 5 classes case. The obtained results and comparisons clearly indicated success of the proposed LSP based method.
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http://dx.doi.org/10.1007/s13246-019-00794-xDOI Listing
December 2019

A new Watermarking System based on Discrete Cosine Transform (DCT) in color biometric images.

J Med Syst 2012 Aug 3;36(4):2379-85. Epub 2011 May 3.

Department of Electronic and Computer Science, Firat University, Elazig, Turkey.

This paper recommend a biometric color images hiding approach An Watermarking System based on Discrete Cosine Transform (DCT), which is used to protect the security and integrity of transmitted biometric color images. Watermarking is a very important hiding information (audio, video, color image, gray image) technique. It is commonly used on digital objects together with the developing technology in the last few years. One of the common methods used for hiding information on image files is DCT method which used in the frequency domain. In this study, DCT methods in order to embed watermark data into face images, without corrupting their features.
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http://dx.doi.org/10.1007/s10916-011-9705-2DOI Listing
August 2012
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