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Publication Metadata only Expert System for Diagnosis of Multiple Neuromuscular Disorders using EMG Signals(IEEE, 2022) Khan, Muhammad Umar; Hanbali, Raneem; Sharma, Siddhant; Iqtidar, Khushbakht; Aziz, Sumair; Farooq, Adil; University of Engineering & Technology Taxila; Bahcesehir University; University System of Ohio; Wright State University Dayton; National University of Sciences & Technology - Pakistan; University of CyprusAge factors and muscular diseases like amyotrophic lateral sclerosis (ALS) and myopathy significantly reduce muscle activity. Early and accurate diagnosis of ALS and myopathy is of great significance for maintaining better life quality. Electromyogram (EMG) signals of the Biceps Brachii muscles are widely used for the diagnosis of ALS and myopathy through a computer-aided automated system. This results in early diagnosis of the diseases which is helpful in symptom management in the patients. In this article, EMG signals were first filtered using Empirical Mode Decomposition (EMD) through reconstruction of the signal using appropriate Intrinsic Mode Functions (IMFs). Preprocessed signals were reconstructed using relative energy-based thresholding of IMFs. Important features of preprocessed EMG signals were extracted using Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficient (GTCC). The final feature vector was constructed by a combination of MFCC and GTCC features. These features were used to train and test Cubic Support Vector Machine (C-SVM). C-SVM yielded the best results of 91.1% mean accuracy for distinguishing between Normal, ALS, and Myopathy signals. The proposed method was compared with a range of other state-of-the-art classification methods. The results of this research advocate the effectiveness of the proposed framework for the accurate diagnosis of neuromuscular diseases in clinical environments.Publication Metadata only Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets(SPRINGER INTERNATIONAL PUBLISHING AG, 2025) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; DelBue, A; Canton, C; Pont-Tuset, J; Tommasi, T; Bahcesehir University; State University System of Florida; Florida Gulf Coast UniversityVisual Field Loss (VFL) is characterized by blind spots or scotomas that poses detrimental impact on fundamental movement activities of individuals. Addressing the challenges (e.g., low video quality, content loss, high levels of contradiction, and limited mobility assessment) faced by existing Extended Reality (XR) systems as vision aids, we introduce a groundbreaking method that enriches the real-time navigation using Augmented Reality (AR) glasses. Our novel vision aid employs advanced video processing techniques to enhance visual perception in individuals with moderate to severe VFL, bridging the gap to healthy vision. A unique optimal video remapping function, tailored to our selected AR glasses characteristics, dynamically maps live video content to the largest intact region of the Visual Field (VF) map. Our method preserves video quality, minimizing blurriness and distortion. Through a comprehensive empirical user study involving 29 subjects with artificially induced scotomas, statistical analyses of object counting and multi-tasking walking track tests demonstrate the promising performance of our method in enhancing visual awareness and navigation capability in real-time.Publication Metadata only Performance Comparison of Oral, Laryngeal and Thoracic Sounds in the Detection of COVID-19 by Employing Machine Learning Techniques(IEEE, 2022) Gozuacik, Necip; Serbes, Gorkem; Kara, Eyup; Atar, Eren; Sakar, C. Okan; Yener, H. Murat; Borekci, Sermin; Korkmazer, Bora; Karaali, Ridvan; Kara, Halide; Gulmez, Zuleyha; Cogen, Talha; Atas, Ahmet; Bahcesehir University; Yildiz Technical University; Istanbul University; Istanbul University - CerrahpasaCOVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region.Publication Metadata only Design and Simulation of MEMS Electrostatic Resonator for Ammonia Gas Detection Based on SOIMUMPs(IEEE, 2021) Hashwan, Saeed S. Ba; Khir, M. H. Md; Al-Douri, Y.; Yousif, A.; Ramza, H.; Arjo, S.; Universiti Teknologi Petronas; Universiti Malaya; Bahcesehir University; Universitas Muhammadiyah Prof. Dr. HamkaThe analytical modeling, design, and simulation of micromachined MEMS resonator for ammonia gas detection is presented in this paper. The MEMS resonator is designed to be vibrated electrostatically using interdigitated comb fingers. The demonstrated device is designed to be capable to carry micro-ring resonator and vibrated in-plane laterally to enhance the sensitivity of the gas detection. This MEMS resonator working principle is based on the changes in the output signal wavelength due to the change in the effective refractive index introduced by the ammonia gas. The resonant frequency of the actuator and the pull-in voltage have been calculated theoretically and found to be 11.15 kHz and 79.7 V respectively. The design and simulation of the micromachined microresonator has been carried out using CoventorWare software. Furthermore, the mathematically modeled results were verified using the finite element analysis software and the result shows a good agreement within 1.06% error between the modeled and simulated frequencies where the modeled and the simulated frequencies are found to be 11.15 kHz and 11.27 kHz respectively.Publication Metadata only Comparison of Estimation Methods for Missing Value Imputation of Gene Expression Data(IEEE, 2015) Sarikas, Ali; Odabasioglu, Niyazi; Altay, Gokmen; Istanbul University; Istanbul University - Cerrahpasa; Bahcesehir UniversityControl and correction process of missing values (imputation of MVs) is the first stage of the preprocessing of microarray datasets. This paper focuses on a comparison of most reliable and up to date estimation methods to control and correct the missing values. Imputation of MVs has a very high priority because of its impact on next pre-processing and post-processing stages of microarray data analysis namely, quality control, normalization, differential gene expression, classification, clustering, and pathway analysis, etc. Normalized root mean square error (NRMSE) value is used to evaluate the performances of most popular five methods (k-nearest neighbors, Bayesian principal component analysis, local least squares, mean and median). When NRMSE values of methods were compared, it has observed that local least squares (LLS) and Bayesian principal component analysis (BPCA) methods outperformed all other methods in all percentages of MVs (1%, 5%, 10%, and 20%). BPCA method has given the best results in all percentages of MVs over the number of probes or genes, whereas LLS method has given the best results in all percentages of MVs over the number of samples. The advantage of these two methods over others is that they are least affected by the complexity of the data set.Publication Metadata only Design and analysis of a label-free water-soluble glucose sensor based on suspended hybrid plasmonic micro-ring resonator(IEEE, 2022) Moeinimaleki, Kaveh; Moeinimaleki, Babak; Zarean, Rana; Abedashtiani, Alireza; Karamzadeh, Saeid; Akdeniz University; Bahcesehir UniversityIn this paper, the design and analysis of a label-free water-soluble glucose sensor based on a suspended hybrid plasmonic micro-ring resonator with an external radius of 940 nm in the third telecommunication window to sense the concentration of water-soluble glucose is presented. The simulation results obtained using three-dimensional FDTD show that the sensitivity and the figure of merit values obtained for glucose measurements with concentrations of 0 to 50% in water solution are 218.5 (nm/RIU) and 16.11 (1/RIU), respectively. Comparing the results of the proposed sensor with the previous work, which has four times more footprint, shows that the structure's sensitivity has increased by 17 percent.Publication Metadata only Advancing Retinal Image Segmentation: A Denoising Diffusion Probabilistic Model Perspective(IEEE COMPUTER SOC, 2024) Alimanov, Alnur; Islam, Md Baharul; Bahcesehir University; State University System of Florida; Florida Gulf Coast UniversityRetinal images and vessel trees play a crucial role in aiding ophthalmologists to identify and diagnose various illnesses related to the eyes, blood vessels, and brain. However, manual retinal image segmentation is a laborious and highly skilled procedure, posing challenges in terms of both difficulty and time consumption. This study proposes a novel approach to retinal image segmentation, leveraging the Denoising Diffusion Probabilistic Model (DDPM) for precise performance. To our best knowledge, DDPM is being applied in this domain for the first time. Our approach incorporates a novel constraint to prevent DDPM from generating vessel structures that not present in the original retinal images during the segmentation process. Additionally, our model is not limited to the original DDPM size of 64 x 64 pixels. Instead, we train it to effectively segment images sized 256 x 256 pixels. This is a significant advancement since the original DDPM works exclusively with 64x64 image sizes and is primarily designed for generating random image samples. In our work, we address both limitations with a novel, efficient approach for accurate retinal image segmentation. A comprehensive evaluation of our methodology includes both quantitative and qualitative assessments. Our proposed method demonstrates competitive performance compared to state-of-the-art techniques, as indicated by both qualitative and quantitative scores. The source code of our method can be accessed at https://github.com/AAleka/DDPM-segmentation.Publication Metadata only Bearing fault detection via wavelet packet decomposition with spectral post processing(IEEE, 2007) Eren, Levent; Teotrakool, Kaptan; Devaney, Michael J.; Bahcesehir University; University of Missouri System; University of Missouri ColumbiaWe present a method for detecting motor bearing fault conditions via wavelet packet decomposition (WPD) of induction motor current. This method involves the decomposition of motor current into equally spaced frequency bands by using all pass implementation of Elliptic IIR half-band filters in the filter bank structure to obtain wavelet packet coefficients (WPC). Then, the bias in WPCs for each frequency band is removed to suppress leakage from adjacent frequency bands. Fourier analysis is applied to wavelet packet coefficients to provide higher frequency resolution within each frequency band. The changes in the energy levels of frequency bands in which motor fault related current frequencies lie are monitored to detect motor fault conditions.Publication Metadata only A Holistic Methodology for Digital Governance Transition and Measuring Its Effect on Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansDigital transformation has become one of the most emphasized issues for organizations today. Organizations have a long way to go in this difficult road. First of all, they should develop and measure their governance skills, and then they need to create the necessary studies in both technological and managerial terms for IT systems to become a value. While doing all these, they should also create a risk management structure with a correct approach by performing consultancy, supervision, and evaluation services completely. In line with this whole process, there are different standards and practices in every field they need to follow. This paper presents a methodology proposition in a general perspective that can be applied end-to-end in the field of digitalization, starting from the governance structure to the IT value-adding strategy. Thanks to this new methodology, it is possible to see many processes such as governance, consultancy, digital transformation, auditing, internal control, which are disconnected from each other, in the big picture and arrange all activities accordingly. With long-term follow-up, the strategic and tactical perspectives of institutions can be created and their progress over time can be followed by this new methodology. This paper performs both qualitative and quantitative measurements of the applications of the new methodology to compare them with existing ones. This methodology introduces a holistic methodology lacking in the market.Publication Metadata only Analyzing Playability in Multi-platform Games: A Case Study of the Fruit Ninja Game(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Aker, Cakir; Rizvanoglu, Kerem; Inal, Yavuz; Yilmaz, Alan Sarp; Marcus, A; Bahcesehir University; Galatasaray University; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)Video games offer new perspectives for discussions and studies on user experience, which results in a change of the relevant terms in the context of gaming, replacing 'usability' with 'playability' and 'user experience' (UX) with 'player experience' (PX). PX can be inspected in various gaming platforms, which present diverse interaction methods through different peripherals, consequently uncovering the complex nature of video games. Therefore, it is critical to understand the nature of PX through user research. However, limited number of studies investigated PX and playability in detail in order to create an analysis framework for entertainment systems by referring to former UX and usability methodologies. Majority of those studies presented a set of playability heuristics on theoretical basis, which still required to be tested through empirical research in various gaming platforms. In this context, this study focuses on the qualitative analysis of multi-platform PX through a proposed playability heuristics framework derived from relevant literature. This study aims to test the proposed framework in a multi-platform game setting and thus seek ways to contribute to the establishment of a new comprehensive analysis framework to understand multi-platform PX. For this purpose, a qualitative multi-method study based on game platform diversity is designed to measure player experience with 8 users in two different gaming platforms which is based on mobile and full body gesture based interaction. Besides revealing the effect of On-Screen elements on PX such as game interface, mechanics and gameplay, the study also presents promising findings for the effect of Off-Screen aspects such as the environmental and social factors.