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: 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: 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.Article Citation - WoS: 101Citation - Scopus: 129Scientific Applications of Distributed Acoustic Sensing: State-Of Review and Perspective(MDPI, 2022) Gorshkov, Boris G.; Yüksel, Kıvılcım; Fotiadi, Andrei A.; Wuilpart, Marc; Korobko, Dmitry A.; Zhirnov, Andrey A.; Lobach, Ivan A.This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.Article Citation - WoS: 3Citation - Scopus: 4Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [article](Wiley-VCH Verlag, 2021) Çakı, Onur; Karaçalı, BilgeIn-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.Article Citation - WoS: 10Citation - Scopus: 13On the Characterization of Cognitive Tasks Using Activity-Specific Short-Lived Synchronization Between Electroencephalography Channels(Elsevier, 2021) Olcay, B. Orkan; Özgören, Murat; Karaçalı, BilgeAccurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Delta t, the time lag between maximally synchronized signal segments t, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the interchannel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes. (C) 2021 Elsevier Ltd. All rights reserved.Article Citation - WoS: 3Citation - Scopus: 3Automated Labeling of Cancer Textures in Larynx Histopathology Slides Using Quasi-Supervised Learning(Science Printers and Publishers Inc., 2014) Önder, Devrim; Sarıoğlu, Sülen; Karaçalı, BilgeOBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasisupervised learning algorithm. RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.Article Citation - WoS: 24Citation - Scopus: 26Fiber Bragg Grating Regeneration at 450°c for Improved High Temperature Sensing(The Optical Society, 2019) Chah, Karima; Yüksel, Kıvılcım; Kinet, Damien; Yazd, Nazila Safari; Megret, Patrice; Caucheteur, ChristopheType-I fiber Bragg gratings photo-inscribed in hydrogen-loaded B/Ge co-doped silica single-mode optical fibers have been regenerated efficiently at 450 degrees C, which is the lowest temperature reported so far. The mechanical strength of the annealed fiber is preserved while ensuring temperature sensing of the regenerated gratings up to 900 degrees C. Unlike low temperature cycles (<= 600 degrees C), an annealing process at higher temperatures revealed faster regeneration for strong gratings. Changes in grating strength were also measured before the regeneration cycle. These behaviors suggest the contribution of different mechanisms to the regeneration process with different relative dynamics. (C) 2019 Optical Society of America.Article Citation - WoS: 10Citation - Scopus: 10The Effect of Additional Telerounding on Postoperative Outcomes, Patient and Surgeon Satisfaction Rates in the Patients Who Underwent Percutaneous Nephrolithotomy(Iniestares, S.A., 2019) Aydoğdu, Özgü; Şen, Volkan; Yarımoğlu, Serkan; Aydoğdu, Canan; Bozkurt, İbrahim H.; Yonguç, TarıkINTRODUCTION: We wanted to investigate the potential effect of additional telerounding system on postoperative outcomes, patient and surgeon satisfaction rates in the patients who underwent percutaneous nephrolithotomy (PNL). METHODS: Eighty patients who underwent PNL were included in the study. The patients were randomly divided to two groups. Group 1 included 40 patients who were followed-up with standard rounds and group 2 included 40 patients who were followed-up with telerounding in addition to standard rounds. Patient and surgeon satisfaction rates were assessed with a visual analog scale (VAS) where 0 point represents very dissatisfied and 100 points very satisfied. RESULTS: Mean time of preoperative telerounding visit was 3.65 +/- 0.59 (2-4) minutes. Mean time of telerounding visits on the postoperative 1st and 2nd days was 3.80 +/- 0.62 and 2.9 +/- 0.91 minutes respectively. The VAS score evaluating the surgeon's satisfaction rate for telerounding was 91 +/- 11.2 and patients expressed a high level of satisfaction with 72.5%. CONCLUSION: The use of additional telerounding in urological patient care provides high satisfaction rates for both the patients and the surgeon. However the findings of the present study don't underestimate the importance of personal surgeon-patient interaction.
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