A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by ... more A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by using MCNPX and ANN Modeling H.O. Tekina,b,c,∗, U. Kara, T. Manici, E.E. Altunsoy, T.T. Erguzel Uskudar University, Vocational School of Health Services, Radiotherapy Department, Istanbul, Turkey Suleyman Demirel University, Vocational School of Health Service, Medical Imaging Department, Isparta, Turkey Uskudar University, Medical Radiation Research Center (USMERA), Istanbul, Turkey Uskudar University, Vocational School of Health Services, Medical Imaging Department, Istanbul, Turkey Uskudar University, Faculty of Engineering and Natural Sciences, Computer Engineering, Istanbul, Turkey
In this paper, radiation shielding parameters such as mass attenuation coefficients and half valu... more In this paper, radiation shielding parameters such as mass attenuation coefficients and half value layer (HVL) of some antioxidants are investigated using MCNPX (version 2.4.0). The validation of the generated MCNPX simulation geometry for antioxidant structures is provided by comparing the results with standard WinXcom data for radiation mass attenuation coefficients of antioxidants. Very good agreement between WİNXCOM and MCNPX was obtained. The results from the validated geometry were used to calculate the shielding parameters of different antioxidants. The radiation attenuation properties of each antioxidant were compared with each other. The results showed that, on average, the highest and the lowest radiation mass attenuation coefficients were observed on hesperidin and delphinidin chloride, respectively. It can be concluded that Monte Carlo simulation is a strong tool and an alternate method where experimental investigations are not possible and a standard simulation setup ca...
Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Ma... more Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.
2017 Intelligent Systems Conference (IntelliSys), 2017
In order to make effective works with the mobile robot and maximize its working performance, it i... more In order to make effective works with the mobile robot and maximize its working performance, it is necessary to estimate and track the current pose of the mobile robot. In this paper, under the assumption that the initial pose, kinematics and environmental model of a mobile robot are known, the localization and tracking of the mobile robot's position and orientation have been carried out. The odometry model with the problem of accumulation of unlimited errors is used for tracking the pose, and sensor fusion algorithms are applied to solve this problem. By using the odometry and laser range finder model, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Unscented Information Filter (UIF), Extended Information Filter (EIF) algorithms were tested on a graphical user interface (GUI) based on occupancy grid maps as an environment model, respectively. In this context, the pose tracking and estimation performances of the non-linear model based estimators are compared to ...
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for e... more Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a ...
Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential si... more Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response–based filtering process was employed rather than traditional time and frequency domain meth...
ABSTRACT Artificial life uses biological knowledge and techniques to solve different engineering, ... more ABSTRACT Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert’s experience.
This study aimed to determine the gamma shielding parameters of an Erbium Zinc Tellurite Glass Sy... more This study aimed to determine the gamma shielding parameters of an Erbium Zinc Tellurite Glass System. The mass attenuation coefficients of Erbium Zinc Tellurite Glass System xEr2O3:20ZnO : (80-x) TeO2 were computed using Monte Carlo simulation at photon energies 20, 30, and 40 keV and 60, 356, 662, 1,173, 1,274, and 1,332 keV. The half-value layer thickness was calculated using the mass attenuation coefficients. The obtained results were compared to standard XCOM data and found comparable. It was found that the erbium zinc tellurite glass with a higher content of erbium is superior shielding material to other glasses. Thus, the validation of a generated Monte Carlo N-Particle Transport Code System-Extended simulation setup has been provided. It can be concluded that results of the present investigation would be very useful for applications of medical diagnostic and nuclear technology.
Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment ... more Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be help...
Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in ... more Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroence...
The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes si... more The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial ne...
A non-pharmacological form of therapy for treating major depressive disorder (MDD) is repetitive ... more A non-pharmacological form of therapy for treating major depressive disorder (MDD) is repetitive transcranial magnetic stimulation (rTMS). For investigating the functional connectivity in the brain, using rTMS with electroencephalogram (EEG) is an efficient way. In this study pre-treatment cordance values are computed using absolute and relative power of EEG spectra and using these values MDD subjects were classified into responder or non- responder classes. An artificial neural network (ANN) based classification was carried out on pre-treatment cordance to predict the response of rTMS treatment and 10-fold cross- validation (CV) was used in evaluating the classification performance. The results show that it is possible to predict rTMS treatment responders with a sensitivity of 93.33%.
A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by ... more A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by using MCNPX and ANN Modeling H.O. Tekina,b,c,∗, U. Kara, T. Manici, E.E. Altunsoy, T.T. Erguzel Uskudar University, Vocational School of Health Services, Radiotherapy Department, Istanbul, Turkey Suleyman Demirel University, Vocational School of Health Service, Medical Imaging Department, Isparta, Turkey Uskudar University, Medical Radiation Research Center (USMERA), Istanbul, Turkey Uskudar University, Vocational School of Health Services, Medical Imaging Department, Istanbul, Turkey Uskudar University, Faculty of Engineering and Natural Sciences, Computer Engineering, Istanbul, Turkey
In this paper, radiation shielding parameters such as mass attenuation coefficients and half valu... more In this paper, radiation shielding parameters such as mass attenuation coefficients and half value layer (HVL) of some antioxidants are investigated using MCNPX (version 2.4.0). The validation of the generated MCNPX simulation geometry for antioxidant structures is provided by comparing the results with standard WinXcom data for radiation mass attenuation coefficients of antioxidants. Very good agreement between WİNXCOM and MCNPX was obtained. The results from the validated geometry were used to calculate the shielding parameters of different antioxidants. The radiation attenuation properties of each antioxidant were compared with each other. The results showed that, on average, the highest and the lowest radiation mass attenuation coefficients were observed on hesperidin and delphinidin chloride, respectively. It can be concluded that Monte Carlo simulation is a strong tool and an alternate method where experimental investigations are not possible and a standard simulation setup ca...
Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Ma... more Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.
2017 Intelligent Systems Conference (IntelliSys), 2017
In order to make effective works with the mobile robot and maximize its working performance, it i... more In order to make effective works with the mobile robot and maximize its working performance, it is necessary to estimate and track the current pose of the mobile robot. In this paper, under the assumption that the initial pose, kinematics and environmental model of a mobile robot are known, the localization and tracking of the mobile robot's position and orientation have been carried out. The odometry model with the problem of accumulation of unlimited errors is used for tracking the pose, and sensor fusion algorithms are applied to solve this problem. By using the odometry and laser range finder model, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Unscented Information Filter (UIF), Extended Information Filter (EIF) algorithms were tested on a graphical user interface (GUI) based on occupancy grid maps as an environment model, respectively. In this context, the pose tracking and estimation performances of the non-linear model based estimators are compared to ...
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for e... more Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a ...
Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential si... more Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response–based filtering process was employed rather than traditional time and frequency domain meth...
ABSTRACT Artificial life uses biological knowledge and techniques to solve different engineering, ... more ABSTRACT Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert’s experience.
This study aimed to determine the gamma shielding parameters of an Erbium Zinc Tellurite Glass Sy... more This study aimed to determine the gamma shielding parameters of an Erbium Zinc Tellurite Glass System. The mass attenuation coefficients of Erbium Zinc Tellurite Glass System xEr2O3:20ZnO : (80-x) TeO2 were computed using Monte Carlo simulation at photon energies 20, 30, and 40 keV and 60, 356, 662, 1,173, 1,274, and 1,332 keV. The half-value layer thickness was calculated using the mass attenuation coefficients. The obtained results were compared to standard XCOM data and found comparable. It was found that the erbium zinc tellurite glass with a higher content of erbium is superior shielding material to other glasses. Thus, the validation of a generated Monte Carlo N-Particle Transport Code System-Extended simulation setup has been provided. It can be concluded that results of the present investigation would be very useful for applications of medical diagnostic and nuclear technology.
Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment ... more Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be help...
Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in ... more Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroence...
The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes si... more The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial ne...
A non-pharmacological form of therapy for treating major depressive disorder (MDD) is repetitive ... more A non-pharmacological form of therapy for treating major depressive disorder (MDD) is repetitive transcranial magnetic stimulation (rTMS). For investigating the functional connectivity in the brain, using rTMS with electroencephalogram (EEG) is an efficient way. In this study pre-treatment cordance values are computed using absolute and relative power of EEG spectra and using these values MDD subjects were classified into responder or non- responder classes. An artificial neural network (ANN) based classification was carried out on pre-treatment cordance to predict the response of rTMS treatment and 10-fold cross- validation (CV) was used in evaluating the classification performance. The results show that it is possible to predict rTMS treatment responders with a sensitivity of 93.33%.
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