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Publication Metadata only Prevalence, etiology, and biopsychosocial risk factors of cervicogenic dizziness in patients with neck pain: A multi-center, cross-sectional study(BAYCINAR MEDICAL PUBL-BAYCINAR TIBBI YAYINCILIK, 2021) Vural, Meltem; Karan, Ayse; Gezer, Ilknur Albayrak; Caliskan, Ahmet; Atar, Sevgi; Aydin, Filiz Yildiz; Benlidayi, Ilke Coskun; Goksen, Aylin; Dogan, Sebnem Koldas; Karacan, Gulcin; Erdem, Rana; Kurt, Emine Eda; Kesiktas, Fatma Nur; Aydin, Tugba; Sahin, Nilay; Aydin, Zafer; Ordahan, Banu; Turkoglu, Gozde; Resorlu, Hatice; Doner, Davut; Yilmaz, Figen; Bertan, Huseyin; Dulgeroglu, Deniz; Karaahmet, Ozgur Zeliha; Tur, Birkan Sonel; Moustafa, Esra; Borman, Pinar; Iskender, Oner; Ay, Saime; Kurtaran, Aydan; Sirzai, Hulya; Evcik, Deniz; Capan, Nalan; Erhan, Belgin; Alptekin, Hasan Kerem; Ural, Halil Ibrahim; University of Health Sciences Turkey; Bakirkoy Dr. Sadi Konuk Research & Training Hospital; Istanbul University; Selcuk University; University of Health Sciences Turkey; Istanbul Okmeydani Training & Research Hospital; Cukurova University; University of Health Sciences Turkey; Antalya Training & Research Hospital; Kirsehir Ahi Evran University; University of Health Sciences Turkey; Balikesir University; University of Health Sciences Turkey; Konya Egitim Training & Research Hospital; Canakkale Onsekiz Mart University; University of Health Sciences Turkey; Istanbul Sisli Hamidiye Etfal Training & Research Hospital; Diskapi Yildirim Beyazit Training & Research Hospital; Ankara University; Hacettepe University; Ufuk University; Guven Hastanesi; Istanbul Medeniyet University; Istanbul Goztepe Training & Research Hospital; Bahcesehir UniversityObjectives: This study aims to investigate the prevalence, etiology, and risk factors of cervicogenic dizziness in patients with neck pain. Patients and methods: Between June 2016 and April 2018, a total of 2,361 patients (526 males, 1,835 females, mean age: 45.0 +/- 13.3 years, range, 18 to 75 years) who presented with the complaint of neck pain lasting for at least one month were included in this prospective, cross-sectional study. Data including concomitant dizziness, severity, and quality of life (QoL) impact of vertigo (via Numeric Dizziness Scale [NDS]), QoL (via Dizziness Handicap Inventory [DHI]), mobility (via Timed Up-and-Go [TUG] test), balance performance [via Berg Balance Scale [BBS]), and emotional status (via Hospital Anxiety Depression Scale [HADS]) were recorded. Results: Dizziness was evident in 40.1% of the patients. Myofascial pain syndrome (MPS) was the most common etiology for neck pain (58.5%) and accompanied with cervicogenic dizziness in 59.7% of the patients. Female versus male sex (odds ratio [OR]: 1.641, 95% CI: 1.241 to 2.171, p=0.001), housewifery versus other occupations (OR: 1.285, 95% CI: 1.006 to 1.642, p=0.045), and lower versus higher education (OR: 1.649-2.564, p<0.001) significantly predicted the increased risk of dizziness in neck pain patients. Patient with dizziness due to MPS had lower dizziness severity scores (p=0.034) and milder impact of dizziness on QoL (p=0.005), lower DHI scores (p=0.004), shorter time to complete the TUG test (p=0.001) and higher BBS scores (p=0.001). Conclusion: Our findings suggest a significant impact of biopsychosocial factors on the likelihood and severity of dizziness and association of dizziness due to MPS with better clinical status.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 On the Source Identification Problem for Hyperbolic-Parabolic Equation with Nonlocal Conditions(AMER INST PHYSICS, 2021) Ashyraliyev, Maksat; Ashyralyev, Allaberen; Zvyagin, Viktor; Ashyralyev, A; Ashyralyyev, C; Erdogan, AS; Lukashov, A; Sadybekov, M; Bahcesehir University; Near East University; Peoples Friendship University of Russia; Institute of Mathematics & Mathematical Modeling; Voronezh State UniversityIn the present paper, we establish the well-posedness of an identification problem for determining the unknown space-dependent source term in the hyperbolic-parabolic equation with nonlocal conditions. The difference scheme is constructed for the approximate solution of this source identification problem. The stability estimates for the solution of the difference scheme are presented.Publication Metadata only A note on the hyperbolic-parabolic identification problem with involution and Dirichlet boundary condition(KARAGANDA STATE UNIV, 2020) Ashyraliyev, Maksat; Ashyralyyeva, Maral A.; Ashyralyev, Allaberen; Bahcesehir University; Turkmen State University; Near East University; Peoples Friendship University of Russia; Institute of Mathematics & Mathematical ModelingIn the present paper, a source identification problem for hyperbolic-parabolic equation with involution and Dirichlet condition is studied. The stability estimates for the solution of the source identification hyperbolic-parabolic problem are established. The first order of accuracy stable difference scheme is constructed for the approximate solution of the problem under consideration. Numerical results are given for a simple test problem.Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only A Variational Joint Segmentation and Registration Framework for Multimodal Images(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Ademaj, Adela; Rada, Lavdie; Ibrahim, Mazlinda; Chen, Ke; Zheng, Y; Williams, BM; Chen, K; Ruprecht Karls University Heidelberg; Bahcesehir University; Universiti Pertahanan Nasional Malaysia; University of LiverpoolImage segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with a mutual information smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.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 SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International UniversitySkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.