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Article ; Online: Tent Map based Optimization Method

Türker TUNCER

Gazi Üniversitesi Fen Bilimleri Dergisi, Vol 6, Iss 4, Pp 909-

2018 Volume 918

Abstract: In the real life, some problems cannot be solved using mathematical methods. Meta-heuristic optimization methods are usually used to solve these problems. One of the solutions used to increase the performance of meta-heuristic optimization methods is the ...

Abstract	In the real life, some problems cannot be solved using mathematical methods. Meta-heuristic optimization methods are usually used to solve these problems. One of the solutions used to increase the performance of meta-heuristic optimization methods is the use of chaotic maps. Chaos is a phenomenon of nonlinear methods. In this article, a new chaotic optimization method is proposed by using a tent map which is one of the frequently used chaotic maps. The proposed method is a particle-based method, which consists of random particle creation, best-fit calculation, particle update, and best-value update. The tent map is used during the particle update phase. In order to test the performance of the proposed method, 12 numerical comparison functions, which are frequently used in the literature, were used and the results obtained were compared with previously proposed and widely used optimization methods in the literature. Experimental results and comparisons show that the proposed method is a successful optimization method.
Keywords	Chaotic optimization ; Tent map ; Numerical functions ; Swarm optimization ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q ; Science (General) ; Q1-390
Subject code	510
Language	English
Publishing date	2018-12-01T00:00:00Z
Publisher	Gazi University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A New Subtraction Based Simplifying Method on Karnaugh Map

Türker TUNCER

Gazi Üniversitesi Fen Bilimleri Dergisi, Vol 5, Iss 2, Pp 63-

2017 Volume 72

Abstract: Karnaugh map is one of the methods that are widely used to simplify boolean expressions by using neighborhoods. The most important advantage provided by K-Map is to realize an electronic circuit with a minimum number of physical gates. K-Map is one of ... ...

Abstract	Karnaugh map is one of the methods that are widely used to simplify boolean expressions by using neighborhoods. The most important advantage provided by K-Map is to realize an electronic circuit with a minimum number of physical gates. K-Map is one of the most important subjects of electronic and logic courses, but students sometimes can not identify groups when simplifying using K-Map. In this study, inputs were added to easily identify large groups with KMap. With the addition of elements, large groups can be detected easily. Once large groups have been identified, the added elements have been removed to obtain the true expression. Only logical addition (OR) and logical multiplication (AND) operations are used for simplification on the K-Map. This paper demonstrates that the subtraction process of K-Maps can be done and proved by using De-Morgan theorem. In this study, subtraction is performed on K-Map for the first time in the literature up to now and an effective subtraction-based simplification method is proposed for K-Maps. De Morgan theorem and experimental results show the correctness of the proposed method.
Keywords	Subtraction based simplification ; Karnaugh map ; Boolean expressions ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q ; Science (General) ; Q1-390
Subject code	516
Language	English
Publishing date	2017-06-01T00:00:00Z
Publisher	Gazi University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Large vessel occlusion detection by non-contrast CT using artificial ıntelligence.

Aytaç, Emrah / Gönen, Murat / Tatli, Sinan / Balgetir, Ferhat / Dogan, Sengul / Tuncer, Turker

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

2024

Abstract: Introduction: Computer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature ... ...

Abstract	Introduction: Computer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke. Methods: We use our dataset. which contains 324 patient's CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis-INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation. Results: By using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy. Conclusion: The obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance.
Language	English
Publishing date	2024-04-15
Publishing country	Italy
Document type	Journal Article
ZDB-ID	2016546-8
ISSN	1590-3478 ; 1590-1874
ISSN (online)	1590-3478
ISSN	1590-1874
DOI	10.1007/s10072-024-07522-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Coronary Angiography Print: An Automated Accurate Hidden Biometric Method Based on Filtered Local Binary Pattern Using Coronary Angiography Images.

Kobat, Mehmet Ali / Tuncer, Turker

Journal of personalized medicine

2021 Volume 11, Issue 10

Abstract: Background and purpose: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the ... ...

Abstract	Background and purpose: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. Material and method: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. Results: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. Conclusions: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.
Language	English
Publishing date	2021-10-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm11101000
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Article ; Online: Automated differential diagnosis method for iron deficiency anemia and beta thalassemia trait based on iterative Chi2 feature selector.

Erten, Mehmet / Tuncer, Turker

International journal of laboratory hematology

2021 Volume 44, Issue 2, Page(s) 430–436

Abstract: Introduction: The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer-based model to aid diagnosis.: Materials and methods: Our work presents a new ... ...

Abstract	Introduction: The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer-based model to aid diagnosis. Materials and methods: Our work presents a new feature selection-based automated disease diagnosis model. To create a testbed, a new corpus is collected retrospectively. Our data sets contain beta thalassemia trait, iron deficiency anemia, and healthy groups. Our presented automated ailment classification model consists iterative chi2 (IChi2) feature selection and classification phases. The used data set includes 25 features, and IChi2 selects the 20 most valuable of them. These are forwarded to 24 traditional classifiers. Results: In this work, two data sets have been used to test our proposal. In the classification phase of this model, 24 shallow classifiers have been used and the best accurate classifiers are Medium Gaussian Support Vector Machine (MGSVM) and Coarse Tree (CT) for the first and second data sets, respectively. These classifiers have been attained 97.48% and 99.73% classification accuracies using the first and second data sets, consecutively. These results are calculated using 10-fold cross-validation. Moreover, hold-out validation has been used in this work, and the results are given in the experiments. Conclusion: Our results denoted the success of IChi2-based classification model for diagnosis on the laboratory data set. We have found a new and robust model to differentiate iron deficiency anemia and beta thalassemia trait. This model may be beneficial for rational laboratory use.
MeSH term(s)	Anemia, Iron-Deficiency/diagnosis ; Diagnosis, Differential ; Humans ; Iron Deficiencies ; Retrospective Studies ; beta-Thalassemia/diagnosis
Language	English
Publishing date	2021-10-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2268590-X
ISSN	1751-553X ; 1751-5521 ; 0141-9854
ISSN (online)	1751-553X
ISSN	1751-5521 ; 0141-9854
DOI	10.1111/ijlh.13745
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Article ; Online: Coronary Angiography Print

Mehmet Ali Kobat / Turker Tuncer

Journal of Personalized Medicine, Vol 11, Iss 1000, p

An Automated Accurate Hidden Biometric Method Based on Filtered Local Binary Pattern Using Coronary Angiography Images

2021 Volume 1000

Abstract	Background and purpose: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. Material and method: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. Results: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. Conclusions: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.
Keywords	coronary angiography print ; hidden biometric ; filtered LBP ; NCA ; biometrics ; Medicine ; R
Subject code	006
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals.

Tasci, Burak / Tasci, Gulay / Dogan, Sengul / Tuncer, Turker

Cognitive neurodynamics

2022 Volume 18, Issue 1, Page(s) 95–108

Abstract: Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates ... ...

Abstract	Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.
Language	English
Publishing date	2022-12-20
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2276890-7
ISSN	1871-4099 ; 1871-4080
ISSN (online)	1871-4099
ISSN	1871-4080
DOI	10.1007/s11571-022-09918-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Vehicle Interior Sound Classification Based on Local Quintet Magnitude Pattern and Iterative Neighborhood Component Analysis

Erhan Akbal / Turker Tuncer / Sengul Dogan

Applied Artificial Intelligence, Vol 36, Iss

2022 Volume 1

Abstract: Nowadays, environmental sound classification (ESC) has become one of the most studied research areas. Sound signals that are indistinguishable from the human auditory systems have been classified by computer-aided systems and machine learning methods. ... ...

Abstract	Nowadays, environmental sound classification (ESC) has become one of the most studied research areas. Sound signals that are indistinguishable from the human auditory systems have been classified by computer-aided systems and machine learning methods. Therefore, ESC has been used in signal processing and sound forensics applications. A novel ESC type is presented in this paper, and it is named as vehicle interior sound classification (VISC). VISC is defined as one of the sub-branches of the ESC, and it is utilized as sound-based biometrics for vehicles. A hand-crafted feature-based VISC method is presented. The proposed method has multileveled feature generation by using maximum pooling and the proposed local quintet magnitude pattern (LQMP), feature selection with iterative neighborhood component analysis (INCA), and classification phases. A novel VISC dataset was collected from YouTube and the proposed LQMP and INCA based method applied to the collected sounds. The results denoted that following: the accuracy, F1-score, and geometric mean of the proposed LQMP and INCA based VISC method were calculated as 98.38%,98.23%, and 98.21% by using support vector machine classifier respectively. The contribution of the proposed VISC method is to denote that the vehicles can be classified by using sound.
Keywords	Electronic computers. Computer science ; QA75.5-76.95 ; Cybernetics ; Q300-390
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images.

Arslan, Sermal / Kaya, Mehmet Kaan / Tasci, Burak / Kaya, Suheda / Tasci, Gulay / Ozsoy, Filiz / Dogan, Sengul / Tuncer, Turker

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 22

Abstract: Background and Aim: ...

Abstract	Background and Aim:
Language	English
Publishing date	2023-11-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13223422
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Article ; Online: Automated anxiety detection using probabilistic binary pattern with ECG signals.

Baygin, Mehmet / Barua, Prabal Datta / Dogan, Sengul / Tuncer, Turker / Hong, Tan Jen / March, Sonja / Tan, Ru-San / Molinari, Filippo / Acharya, U Rajendra

Computer methods and programs in biomedicine

2024 Volume 247, Page(s) 108076

Abstract: Background and aim: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated ...

Abstract	Background and aim: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. Materials and methods: We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. Results: Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. Conclusions: The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.
MeSH term(s)	Humans ; Electrocardiography ; Wavelet Analysis ; Algorithms ; Anxiety/diagnosis ; Anxiety Disorders ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2024-02-10
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2024.108076
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