Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 5Citation - Scopus: 6A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification(MDPI, 2024) Gürkan Kuntalp, D.; Özcan, N.; Düzyel, Okan; Kababulut, F.Y.; Kuntalp, M.The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy. © 2024 by the authors.Article Citation - WoS: 4Citation - Scopus: 4A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases(MDPI, 2023) Kababulut, Fevzi Yasin; Kuntalp, Damla Gurkan; Düzyel, Okan; Özcan, Nermin; Kuntalp, MehmetThe aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.Article Citation - WoS: 5Citation - Scopus: 7Adaptive Resizer-Based Transfer Learning Framework for the Diagnosis of Breast Cancer Using Histopathology Images(Springer, 2023) Düzyel, Okan; Çatal, Mehmet Sergen; Kayan, Ceyhun Efe; Sevinç, Arda; Gümüş, AbdurrahmanBreast cancer is a major global health concern, and early and accurate diagnosis is crucial for effective treatment. Recent advancements in computer-assisted prediction models have facilitated diagnosis and prognosis using high-resolution histopathology images, which provide detailed information on cancerous tissue. However, these high-resolution images often require resizing, leading to potential data loss. In this study, we demonstrate the effect of a learnable adaptive resizer for breast cancer classification using the BreakHis dataset. Our approach incorporates the adaptive resizer with various convolutional neural network models, including VGG16, VGG19, MobileNetV2, InceptionResnetV2, DenseNet121, DenseNet201, and EfficientNetB0. Despite producing visually less appealing images, the learnable resizer effectively improves classification performance. DenseNet201, when jointly trained with the adaptive resizer, achieves the highest accuracy of 98.96% for input images of 448x448 resolution. Our experimental results demonstrate that the adaptive resizer performs better at a magnification factor of 40x compared to higher magnifications. While its effectiveness becomes less pronounced as image resolution increases to 100x, 200x, and 400x, the adaptive resizer still outperforms bilinear interpolation. In conclusion, this study highlights the potential of adaptive resizers in enhancing performance for medical image classification. By outperforming traditional image resizing methods, our work contributes to the advancement of deep neural networks in the field of breast cancer diagnostics.Article Enhancing Thickness Determination of Nanoscale Dielectric Films in Phase Diffraction-Based Optical Characterization Systems With Radial Basis Function Neural Networks(IOP Publishing, 2023) Ataç, Enes; Karatay, Anıl; Dinleyici, Mehmet SalihAccurate determination of the optical properties of ultra-thin dielectric films is an essential and challenging task in optical fiber sensor systems. However, nanoscale thickness identification of these films may be laborious due to insufficient and protracted classical curve matching algorithms. Therefore, this experimental study presents an application of a radial basis function neural network in phase diffraction-based optical characterization systems to determine the thickness of nanoscale polymer films. The non-stationary measurement data with environmental and detector noise were subjected to a detailed analysis. The outcomes of this investigation are benchmarked against the linear discriminant analysis method and further verified by means of scanning electron microscopy. The results show that the neural network has reached a remarkable accuracy of 98% and 82.5%, respectively, in tests with simulation and experimental data. In this way, rapid and precise thickness estimation may be realized within the tolerance range of 25 nm, offering a significant improvement over conventional measurement techniques.Article Citation - WoS: 2Citation - Scopus: 2Subwavelength Thickness Characterization of Curved Dielectric Films Exploiting Spatially Structured Entangled Photons(Optica Publishing Group, 2023) Ataç, Enes; Dinleyici, Mehmet SalihPrecise determination of thin dielectric film optical properties is a critical issue for fiber optic sensor technologies. However, conventional methods for the optical characterization of these films not only are generally complex and tedious processes on curved surfaces but also require well-calibrated and overly sophisticated devices. We, on the other hand, propose a novel and practical quantum-based phase diffraction scheme to characterize the thickness of ultra-thin transparent dielectric films coated on an optical fiber beyond the classical diffraction limits in this paper. The approach is implemented by evaluating the effect of thickness variations on the highly visible two-photon diffraction pattern's zero crossings and amplitudes. The mathematical model and numerical simulations con-tribute to a better understanding of how the spatially structured entangled photons improve thickness precision with the help of intensity correlations and a confocal aperture. To prove the impact of the proposed system, it is compared with the classical phase diffraction method in the literature via simulations. According to the results, the thickness of the transparent dielectric films can be accurately estimated below one-twentieth of the wavelength of interest. & COPY; 2023 Optica Publishing GroupArticle Citation - WoS: 3Citation - Scopus: 4Cost-Effective Experiments With Additively Manufactured Waveguide and Cavities in the S-Band(Iop Publishing Ltd, 2023) Karatay, Anıl; Yilmaz, Hasan Önder; Özkal, Ceren; Yaman, FatihThis study demonstrates the applicability of additively manufactured components that are metalized with conductive tape for two different microwave experiments. We focus on dielectric measurements and prototyping elliptical accelerator cavities at a low power regime for 2.45 GHz. To illustrate the accuracy of our results for the commonly used solid/liquid materials in engineering and to compare the fundamental accelerator cavity parameters with previous research rectangular and elliptic 3D-printed cavities coated with aluminum-type tape were employed in the experiments. Results reported for the complex-valued permittivities and specific design parameters for the cavity prototype are consistent with the literature. Various approaches to obtain the conductivity value of the tape and the effect of the roughness/thickness of the coating on the reflection parameter are discussed in detail. We confirm the effectiveness of the proposed approach, which reduces costs and provides a high degree of accuracy for investigated applications.Article Citation - WoS: 1Citation - Scopus: 1Resting Electroencephalography Differences Between Eyes-Closed and Eyes-Open Conditions in Children With Subclinical Hypothyroidism(AVES, 2023) Bayazıt, Onur; Kahya, Mehmet Cemal; Çatlı, Gönül; Kocaaslan Atlı, Sibel; Olgaç Dündar, Nihal; Erdoğan, Uğraş; Evirgen Esin, NurObjective: Electroencephalography changes that occur during the transition from eyes-closed to the eyes-open state in resting condition are related to the early phase of sensory processing and are defined as activation. The present study aimed to reveal the potential deteriorations that may occur in the initial period of sensory processing in resting electroencephalography between children with subclinical hypothyroidism and a control group. Materials and Methods: Electroencephalographies of 15 children with subclinical hypothy-roidism and 15 healthy children aged 10 to 17 years were recorded for 2 minutes for EC and 2 minutes for eyes-open conditions in resting state. Absolute electroencephalography band powers (μV2) within the delta, theta, alpha, and beta frequency bands were calculated in Fz, Cz, Pz, and Oz electrodes, respectively, for eyes-closed and eyes-open conditions. Results: The results show that, although there was no noteworthy difference between the powers of the electroencephalography frequency bands of children with subclinical hypothyroidism and healthy children during the eyes-open condition, the alpha powers of the control group were significantly higher in all electrodes during the eyes-closed condition. Furthermore, the powers of all frequency bands were observed to decrease in the eyes-open condition in the control group. However, the same net decrease was not observed in the frequency powers of children with subclinical hypothyroidism. Conclusion: According to the results of this study, children with subclinical hypothyroidism may experience information processing impairments starting in the early stages of sensory processing. © 2023, AVES. All rights reserved.Article Citation - WoS: 7Citation - Scopus: 10Analysis of Crosstalk Effects in Phase-Otdr System Using Fiber Bragg Grating Array(Elsevier, 2023) Koçal, Ertunga Burak; Yüksel, Kıvılcım; Wuilpart, MarcIn this paper, the parasitic components (i.e., multi-reflections, Rayleigh scattering, photodetector noise, and phase variations due to external perturbations) are analysed and based on this analysis, a new signal to noise ratio (SNR) definition is provided suitable for the FBG-assisted Phase-OTDR system. A detailed analysis of performance parameters in the presence of multi reflection crosstalk (including its first- and second-order components) and spectral shadowing crosstalk is presented. SNR was calculated for different reflectivity and spacing lengths showing that the maximum number of cascaded FBGs can be significantly increased by using lower FBG reflectivity. It was also observed that the spacing length distance does not have a significant impact on the maximum number of FBGs that can be interrogated. By comparing single-pulse and double-pulse configurations, the use of double pulse was shown to provide higher SNR values when the number of FBGs is around 100 FBGs. The multi-reflection crosstalk when combined with the spectral-shadowing effect was demonstrated to create secondary crosstalk components making the interpretation of spectral analysis more difficult.Article Citation - WoS: 3Citation - Scopus: 3Current Sensing Using a Phase-Sensitive Optical Time Domain Reflectometer: Feasibility Study(Elsevier, 2022) Wuilpart, Marc; Şirin, Şamil; Yüksel Aldoğan, KıvılcımA novel method for distributed current sensing using an FBG-assisted Phase-OTDR with Mach-Zehnder Interferometer is proposed. The detrimental effect of the intrinsic linear birefringence of the sensing fiber is solved by calibration. An FBG pair is written at the two ends of the spun fiber coil to eliminate phase fading and increase the measurement accuracy. A simulation tool was developed to reveal the feasibility of the approach by investigating the impact of the detector noise as well as the effects of bending- and FBG-induced linear birefringence on the sensing performance.Article Citation - WoS: 4Citation - Scopus: 4Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission(Elsevier, 2022) Güleç, Fatih; Atakan, Barış; Dressler, FalkoA number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.