Gokhan Altan

Gokhan Altan
Iskenderun Technical University · Computer Engineering

PhD

About

61
Publications
20,507
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529
Citations
Citations since 2017
47 Research Items
513 Citations
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150

Publications

Publications (61)
Article
Full-text available
In machine learning models, one of the most popular models is artificial neural networks. The activation function is one of the important parameters of neural networks. In this paper, the sigmoid function is used as an activation function with a fractional derivative approach to minimize the convergence error in backpropagation and to maximize the...
Article
Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME on OCT images using a lightweight explainable Convo...
Article
Full-text available
The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, W...
Article
Full-text available
One of the major problems in wireless sensor networks (WSNs) is that resource-constrained sensor nodes consume their limited batteries quickly due to long-distance data communications. The communication distance of the nodes can be decreased using clustering architectures and multi-hop data transmissions; hence, the lifetime of the network can be i...
Conference Paper
Full-text available
Image processing techniques enable generating robust representations. However, it is still method-dependent and is computationally intensive due to high dimensionality in feature extraction. Generative models have been popular approaches in the last decades with the advantages of Deep Learning. The deep Belief Networks (DBN) classifier has a common...
Conference Paper
Full-text available
Deep Learning (DL) has become very popular, especially thanks to the towering achievements in classification, segmentation, and identification in many image types by the use of transfer learning and detailed feature learning stages. Although DL has been criticized by some researchers due to downsampling procedure, it has created a new enlightenment...
Article
Full-text available
Deep learning with convolutional neural networks (ConvNets) has dramatically improved the learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is a rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics with ConvNets. Our study foc...
Article
Full-text available
Deep learning (DL) is a special field of artificial intelligence that has increased its use in various fields and has proved its effectiveness in classification. The feasibility of using many hidden layers and many neurons for each layer in the DL architectures enables a detailed analyzing capability for classification and segmentation issues. Adva...
Preprint
Full-text available
Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimiz...
Preprint
Full-text available
Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is dir...
Preprint
Full-text available
Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded sy...
Preprint
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the...
Article
Full-text available
Deep Learning (DL) is a rising field of researches in last decade by exposing a hybrid analysis procedure including advanced level image processing and many efficient supervised classifiers. Robustness of the DL algorithms to the big data enhances the analysis capabilities of machine learning models by feature learning on heterogeneous image databa...
Article
Full-text available
Deep Learning (DL) is a high capable machine learning algorithm which composed the advanced image processing as feature learning and supervised learning with detailed models with many hidden layers and neurons. DL demonstrated its efficiency and robustness in many big data problems, computer vision, and more. Whereas it has an increasing popularity...
Chapter
Deep Learning (DL) is a high capable machine learning algorithm with the detailed analysis abilities on images. Although DL models achieve very high classification performances, the applications are trending on using and fine-tuning pre-trained DL models by transfer learning due to the dependence on the number of data, long train time, employments...
Article
Full-text available
Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is comparing multiple machine learning a...
Chapter
Modeling of deep learning (DL) structures with many hidden layers and a large number of neurons at each layer for the data analysis process has brought about the requirements for long training time and computation capacity depending on the increase in the number of optimization parameters. This chapter addresses the problem of how to reduce the tra...
Article
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the...
Conference Paper
Full-text available
In recent days, machine learning algorithms and method diversity have increased popularity with the spread of image processing applications. The most suitable parameters for noise elimination are calculated using the Block matching algorithm on the chest radiography image. The BM3D algorithm is used to reconstruct chest x-ray medical images. Signif...
Preprint
Full-text available
Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of elec-trocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models ; squared, circled, inclined and grid entropy measurement were introduced an...
Preprint
Full-text available
Deep learning with convolutional neural networks (ConvNets) have dramatically improved learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is rising curiosity in interpreting and analyzing electroen-cephalography (EEG) dynamics with ConvNets. Our study focused...
Preprint
Full-text available
As a type of pseudoinverse learning, extreme learning machine (ELM) is able to achieve high performances in a rapid pace on benchmark datasets. However, when it is applied to real life large data, decline related to low-convergence of singular value decomposition (SVD) method occurs. Our study aims to resolve this issue via replacing SVD with theor...
Preprint
Full-text available
As a type of pseudoinverse learning, extreme learning machine (ELM) is able to achieve high performances in a rapid pace on benchmark datasets. However , when it is applied to real life large data, decline related to low-convergence of singular value decomposition (SVD) method occurs. Our study aims to resolve this issue via replacing SVD with theo...
Preprint
Full-text available
Deep learning with convolutional neural networks (ConvNets) have dramatically improved learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics with ConvNets. Our study focused...
Article
Background and objective ECG is one of the biometric signals that has been studied in peer-reviewed over past years. The developments on the signal analysis methods show that the studies on the ECG would continue unabatedly. It has a common use on cardiac diseases with high rates of classification performances by integrating it with signal analysi...
Article
Full-text available
Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is dir...
Conference Paper
Full-text available
Fish recognition becomes a popular researching area for Marine sciences and computer engineering by the developments on image processing techniques and acquiring detailed databases. The fish images from different families and species enables computational and morphological assessments for fish recognition applications. This study aimed to separate...
Article
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the...
Article
Full-text available
Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimi...
Conference Paper
Full-text available
Lung sounds are the fundamental and effective diagnostic signals for the Chronic Obstructive Pulmonary Disease (COPD). In this study, the contribution of the heart sounds to the lung sounds is focused on to diagnose the COPD. The heart sounds have a significant characteristic as the symptoms leading to heart failure. The RespiratoryDatabase@TR that...
Article
Full-text available
Deep Learning (DL) is an effective way that reveals on computation capability and advantage of the hidden layer in the network models. It has pre-training phases which define the output parameters in unsupervised ways and supervised training for optimization of the pre-defined classification parameters. This study aims to perform high generalized f...
Conference Paper
Full-text available
In this study, Hilbert-Huang Transform (HHT) was applied to the lung sounds from RespiratoryDatabase@TR and the statistical features were calculated from the different modulations of the HHT. The statistical features were fed into the DBN to classify the lung sounds from Chronic Obstructive Pulmonary Disease (COPD) and healthy subjects.
Article
The second order difference plot (SODP) is a nonlinear signal analysis method that visualizes two consecutive data points for many types of biomedical signals. The proposed method is based on analysing quantization of 3D-space which is originated using three consecutive data points in signal. The obtained 3D-SODP space was segmented into 3-10 space...
Conference Paper
Full-text available
Asthma is one of the most common chronic complaints estimated to affect about 300 million people worldwide. The auscultation sounds including lung sounds and pathological breathing sounds are significant diagnostic tools for chronic respiratory diseases. 10s of lung sounds, recorded from 12-channels with right and left focal points of posterior and...
Conference Paper
Full-text available
Chronic Obstructive Pulmonary Disease (COPD) is a completely untreatable disease that results in exposure of lungs to harmful dusts, gases or micro-particles. The Lower-Upper decomposition based ELM Autoencoder kernel is adapted to the Deep ELM model and is tested on lung sounds. 10s of lung sounds, recorded from 12-channels with right and left foc...
Conference Paper
Full-text available
Chronic Obstructive Pulmonary Disease (COPD) is a completely untreatable disease that results in exposure of lungs to harmful dusts, gases or micro particles. In general practice, diagnosis of the COPD needs to be concretized with spirometry test and lung chest X-rays after auscultation of lung sounds [1]. In this study, it is aimed to diagnose the...
Article
Full-text available
Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded sy...
Conference Paper
Full-text available
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the l...
Conference Paper
Full-text available
The second order difference plot (SODP) is a data visualization method of two consecutive wave points that is inspired by the Chaos Theory for many erratic nonlinear biomedical signals. The proposed method plots three consecutive wave points in 3D space. The obtained 3D-SODP space is segmented into 3-10 sections plane sections using spheres and cub...
Article
Full-text available
This study is performed to classify fish species based on morphometric measurements between main points (fins, head and mouth) on fish image. Three species of Triglidae Family (Aspitrigla cuculus, Chelidonichthys lastoviza and Chelidonichthyslucernus) which has very similar in appearance of shape, color and the fin type are used for the classifica...
Article
Full-text available
In this study, a decision-support system is presented to aid cardiologists during the diagnosis and to create a base for a new diagnosis system which separates two classes (CAD and no-CAD patients) using an electrocardiogram (ECG). 24 hour filtered ECG signals from PhysioNet were used. 15 second short-term ECG segments were extracted from 24 hour...
Article
Full-text available
An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multi...
Article
Full-text available
An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical...
Poster
Full-text available
Solunum seslerinin oskültasyonu, pulmoner bozukluklar ve bazı kardiyak bozuklukların teşhisi için göğüs ve sırttan dinlenen sesler için kullanılan ucuz ve etkili bir yöntemdir. Günümüzde bilgisayar destekli analiz ve hastalık teşhis sistemlerinde meydana gelen gelişmelerle, izlenmesi zor ve büyük dikkat isteyen süreçlerin takibinde başarımları artı...
Article
Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert–Huang transform (HHT), whi...
Conference Paper
Full-text available
Introduction: An electrocardiogram (ECG) is a non-linear and non-stationary diagnostic signal that has a great importance for cardiac disorders. The computer-assisted analysis of biomedical signals has become an essential tool in recent years. Classifier: This study introduces a deep learning (DL) application on automatic arrhythmia classification...
Conference Paper
Full-text available
Introduction: An electroencephalographic (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded f...
Conference Paper
Full-text available
In this study, a decision-support system is presented to aid cardiologists during the diagnosis and to create a base for a new diagnosis system which separates two classes (CAD and no-CAD patients) using an electrocardiogram (ECG). 24 hour filtered ECG signals from PhysioNet were used. 15 second short-term ECG segments were extracted from 24 hour E...
Conference Paper
Full-text available
In this study, Second Order Difference Plot (SODP) features are used for ECG based human identification. SODP is a method that allows to determine the features with the statistical analysis of the situations obtained from distributions and the distribution of each of successive points on an unstable and linear signals. ECG records of 90 individuals...
Article
Full-text available
Dönüşümü (HHD) lineer olmayan ve sabit olmayan sinyaller üzerinde öznitelik belirleme, filtreleme ve benzeri işlemlere ön işleme olarak sıkça kullanılan bir yöntemdir. Bu çalışmada, HHD yönteminin kalp ritim sinyallerine uygulanması sonucu elde edilebilecek özniteliklerin belirlenmesi ve belirlenen bu özniteliklerin seçimi sonrası Kongestif Kalp Ye...
Conference Paper
Full-text available
Kongestif Kalp Yetmezliği (KKY) dünya çapında insanları etkileyen kardiyolojik bir hastalıktır. Elektrokardiyogram (EKG) işaretleri kullanılarak KKY teşhisi önemli bir araştırma alanıdır. Bu çalışmada; KKY teşhisi için İçsel Mod Fonksiyonu (İMF) ve İkinci Derece Fark (İDF) tabanlı akıllı sistem önerilmektedir. Görgül Kip Ayrışımı yöntemiyle (GKA) E...
Conference Paper
Full-text available
HHD lineer olmayan ve sabit olmayan sinyaller üzerinde öznitelik belirleme, filtreleme ya da benzeri işlemlere ön işleme olarak sıkça kullanılmaktadır. Bu çalışmada, Hilbert-Huang Transform (HHD) yönteminin kalp ritim sinyallerine uygulanması sonucu elde edilebilecek özniteliklerin belirlenmesi ve kullanılması üzerine çalışmalar yapılmıştır. Konjek...
Conference Paper
Full-text available
The aim of this study is to design a Fuzzy Expert System to trace vital functions of the patient for directing the courses of the surgery during the Coronary Bypass Surgery (CBS). The designed fuzzy expert system presents and monitors four of the vital functions (Blood pressure, Hemoglobin, Pulse and Beta-blocker) of the patient and also interprets...

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Projects (7)
Project
At the last layer of CNN (fully connected layer), we aim to remove classical backprop approach to avoid local minimal problem.