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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 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 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 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 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 Real-time Restoration of Quality Distortions in Mobile Images using Deep Learning(IEEE, 2020) Kocak, Taskin; Ciloglu, Cagkan; University of Louisiana System; University of New Orleans; Bahcesehir UniversityFrames provided by camera on mobile devices may be distorted because of camera defects and/or weather conditions such as rain and snow. These distortions affect image classifiers. This paper proposes using deep-learning architectures to restore quality distortions in real-time mobile video for image classifiers. An iOS based app is developed using CoreML to show that deep convolutional auto-encoder (CAE) based methods can be used to restore picture quality.Publication Metadata only Comparison of Photothermal Activity of Iron- and Manganese-Chelated Natural Melanin Nanoparticles(IEEE, 2024) Yektar, Seyma; Kaleli-Can, Gizem; Baysoy, Engin; Ruhi, Mustafa Kemal; Uskudar University; Bogazici University; Izmir Democracy University; Bahcesehir UniversityMelanin is a pigment abundance in many living organisms and has been studied for its nanoparticle form in applications such as biosensors, drug delivery, imaging, and photothermal therapy. Previous research indicated that natural melanin nanoparticles (MNPs) conjugated with metal ions could serve as contrast agents for T1-weighted magnetic resonance imaging due to their high T1 relaxivity rates. MNPs have excellent ultraviolet (UV) and near-infrared irradiation absorption and photothermal activity in a wide range of wavelengths. This study investigated whether the photothermal properties of melanin nanoparticles persist after metal-ion chelation, comparing the efficacy of natural and metal-conjugated forms at different concentrations. The results demonstrate that metal ion chelation used for MR imaging does not affect the photothermal efficiency of MNPs, and the photothermal activity of the nanoparticles increase with nanoparticle concentration. The solution consisting of a 2 mg/ml concentration of manganese-MNP complexes (MNP-Mn2+) showed the highest increase with 7 degrees C temperature compared to distilled water under 633 nm wavelength laser irradiation for 30 minutes.Publication Metadata only ASD-EVNet: An Ensemble Vision Network based on Facial Expression for Autism Spectrum Disorder Recognition(IEEE, 2023) Jaby, Assil; Islam, Md Baharul; Ahad, Md Atiqur Rahman; Bahcesehir University; University of East LondonAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects individuals' social interaction, communication, and behavior. Early diagnosis and intervention are critical for the well-being and development of children with ASD. Available methods for diagnosing ASD are unpredictable (or with limited accuracy) or require significant time and resources. We aim to enhance the precision of ASD diagnosis by utilizing facial expressions, a readily accessible and limited time-consuming approach. This paper presents ASD Ensemble Vision Network (ASD-EVNet) for recognizing ASD based on facial expressions. The model utilizes three Vision Transformer (ViT) architectures, pre-trained on imageNet-21K and fine-tuned on the ASD dataset. We also develop an extensive collection of facial expression-based ASD dataset for children (FADC). The ensemble learning model was then created by combining the predictions of the three ViT models and feeding it to a classifier. Our experiments demonstrate that the proposed ensemble learning model outperforms and achieves state-of-the-art results in detecting ASD based on facial expressions.Publication Metadata only Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.Publication Metadata only Consumer Preference Estimation Using EEG Signals and Deep Learning(IEEE, 2024) Ceylan, Burak; Cekic, Yalcin; Akan, Aydin; Bahcesehir University; Izmir Ekonomi UniversitesiEmotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies.