Yazar "Barisci, Necaattin" seçeneğine göre listele

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    The adaptive ARMA analysis of EMG signals
    (Springer, 2008) Barisci, Necaattin
    In this study, Adaptive auto regressive-moving average (A-ARMA) analysis of EMG signals recorded on the ulnar nerve region of the right hand in resting position was performed. A-ARMA method, especially in the calculation of the spectrums of stationary signals, is used for frequency analysis of signals, which give frequency response as sharp peaks and valleys. In this study, as the result of A-ARMA method analysis of EMG signals frequency-time domain, frequency spectrum curves (histogram curves) were obtained. As the images belonging to these histograms were evaluated, fibrillation potential widths of the muscle fibers of the ulnar nerve region of the people (material of the study) were examined. According to the degeneration degrees of the motor nerves, 22 people had myopathy, 43 had neuropathy, and 28 were normal.
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    Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method
    (Blackwell Publishing, 2007) Barisci, Necaattin; Hardalac, Firat
    In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.
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    Comparison of Multi Layer Perceptron and Jordan Elman Neural Networks for Diagnosis of Hypertension
    (Tsi Press, 2015) Turk, Fuat; Barisci, Necaattin; Ciftci, Aydin; Ekmekci, Yakup
    In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. This data was transferred to the computer by processing methods of quantitative analysis. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Multi Layer Perceptron (MLP) neural network, 80.4% of patient individuals and 81.8% of normal individuals were classified correctly. Using Jordan Elman neural network, 85.3% of the patient individuals and 87.8% of normal individuals were classified correctly.
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    Comparison of Principal Component Analysis and Radial Basis Function Network for Diagnosis of Hypertension
    (Turgut Ozal Univ, 2012) Turk, Fuat; Barisci, Necaattin; Ciftci, Aydin; Ekmekci, Yakup
    In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Principal Component Analysis (PCA), 89% of patient individuals and 88% of normal individuals were classified correctly. Using Radial Basis Function Networks (RBFN), 89% of the patient individuals and 84% of normal individuals were classified correctly.
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    Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller
    (Taylor & Francis Inc, 2016) Civelek, Zafer; Luy, Murat; Cam, Ertugrul; Barisci, Necaattin
    This article presents a study on set of PID parameters of blade pitch angle controller of wind turbine with fuzzy logic algorithm. Three individual control methods were used to control the wind turbine pitch angle. These control methods are conventional PI, fuzzy and fuzzy PID. With the use of these control methods, the system was protected from possible harms in high wind speed region and maintained changing of nominal output power. It was aimed to the control the wind turbine blade pitch angle in different wind speeds and to hold the output power stable in the set point by simulation of controllers with Matlab/Simulink Software. By evaluating the steady state time of output power received from the simulation results and steady state errors, the performances of the control systems have been measured and compared with one another. As a result of these simulation comparisons, it is clear that fuzzy PID controller performed better than PI and Fuzzy Controller.
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    Improving detection and classification of diabetic retinopathy using CUDA and Mask RCNN
    (Springer London Ltd, 2023) Erciyas, Abdussamed; Barisci, Necaattin; Unver, Halil Murat; Polat, Huseyin
    Diabetic retinopathy (DR) is an eye disease caused by diabetes and can progress to certain degrees. Because DR's the final stage can cause blindness, early detection is crucial to prevent visual disturbances. With the development of GPU technology, image classification and object detection can be done faster. Particularly on medical images, these processes play an important role in disease detection. In this work, we improved our previous work to detect diabetic retinopathy using Faster RCNN and attention layer. In the detection phase, firstly non-used area of DR image was extracted using compute unified device architecture with gradient-based edge detection method. Then Mask RCNN was used instead of faster region-based convolutional neural networks (Faster RCNN) to detect lesion areas more successful. With the proposed method, more successful results were obtained than the our previous work in DenseNet, MobileNet and ResNet networks. In addition, more successful results were obtained than other works in the literature in ACC and AUC metrics obtained by using VGG19.
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    Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model
    (MDPI, 2020) Turk, Fuat; Luy, Murat; Barisci, Necaattin
    Kidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation.
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    Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture
    (Tech Science Press, 2022) Turk, Fuat; Luy, Murat; Barisci, Necaattin; Yalcinkaya, Fikret
    Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney tumors may not show any serious symptoms until the last stage. Therefore, early diagnosis of the tumor is important. This study aimed to develop support systems that could help physicians in the segmentation of kidney tumors. In the study, improvements were made on the encoder and decoder phases of the V-Net model. With the double-stage bottleneck block structure, the architecture was transformed into a unique one, which achieved an 86.9% kidney tumor Dice similarity coefficient. The results show that the model gives applicable and accurate results for kidney tumor segmentation.
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    Optimization of indoor thermal comfort values with fuzzy logic and genetic algorithm
    (Ios Press, 2023) Balci, Sonay Gorgulu; Ersoz, Suleyman; Luy, Murat; Turker, Ahmet Kursad; Barisci, Necaattin
    It is known that in crowded environments such as educational institutions and workplaces, keeping indoor air quality and climate within certain limits contributes to success and production. For this purpose, a system has been developed to ensure air quality well-being in working environments. In our study, the Arduino processor managed by the fuzzy logic control system (FLC) starts to work with the trigger of the motion sensor HC-SR501. The inputs of the FLC system are defined as LM-35 sensor for temperature, DHT-11 for humidity, MQ-135 for air quality, MQ-9 sensor for CO and explosive gas. The designed system evaluates the instantaneous data obtained from the fuzzy logic system rule base and decides which of the output air filter, heater and alarm systems will operate at what speed. In order to increase system efficiency, fuzzy logic input membership values are optimized by genetic algorithm.
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    Prediction of coronary angiography requirement of patients with Fuzzy Logic and Learning Vector Quantization
    (Ieee, 2013) Akbulut, Harun; Barisci, Necaattin; Arinc, Huseyin; Topal, Taner; Luy, Murat
    In this study, prediction of coronary angiography (CA) requirement of patients is presented using Fuzzy Logic (FL) and Learning Vector Quantization (LVQ). Data sets of patients are received from 200 patients, half of whom undergo CA, the other half doesn't undergo CA, the numbers of both men and women patients are selected equal. Input data sets and output data sets are determined and tested for FL. The correct classification rate of FL is measured 86% for prediction of CA requirement of patients. Training data sets and testing data sets are determined and tested for LVQ. The correct classification rate of LVQ is measured 88% for prediction of CA requirement of patients. These results show that LVQ is more effective than FL at prediction of CA requirement of patients.