جامعة بلاد الرافدين - Bilad Alrafidain University - م.د.مصعب علي حسن

م.د.مصعب علي حسن

Dr.Mosab A.Hassan

تدريسي : هندسة تقنيات الاجهزة الطبية

Teaching : DEPARTMENT OF MEDICAL INSTRUMENTS ENGINEERING TECHNIQUES

الدكتوراة في هندسة الالكترونية والاتصالات

Doctor of Philosophy in Electronic and Communication Engineering

mosab@bauc14.edu.iq

mosab82hassan@gmail.com

نشاطات التدريسي

البحوث المنشورة

المؤلفات

المقررات المكلف بها

المحاضرات

مشاريع التخرج

المقالات داخل المجلة

البحوث

	2024	Engineering and Technology Journal
"People need a communication channel and a way to communicate with a person or group. Deaf people communicate with others through sign language. The remarkable and rapid development in image and video recognition systems has made researchers use this development to solve many problems, including sign language for the deaf, and reduce their suffering in communicating with ordinary people. This work aims to use and apply machine learning technology to build an automatic recognition system for Arabic Sign Language (ArSL). In this work, images of ArSL characters were recognized using four classification techniques (Naïve Bayes (NB), Decision Trees (DTs), and Adaptive Boosting), and K-Nearest Neighbor (KNN)) with the Python library and using two feature extraction algorithms (PCA & LDA). Data pre-processing steps, including grayscale conversion, Gaussian blur, histogram equalization, and resizing, are applied to enhance the data's suitability for training and testing. The work was tested with five experiments chosen with multiple ratios for training and test data. The first training is 90%, the second training is 80% of the data, the third is 75%, the fourth is 70%, and the last is 60%. The work also played a good role in interpreting ArSL, and the accuracy of the work considers the KNN algorithm more accurate in prediction."

	2024	open engineering
"This study explores the field of sign language recognition through machine learning, focusing on the development and comparative evaluation of various algorithms designed to interpret sign language. With the prevalence of hearing impairment affecting millions globally, efficient sign language recognition systems are increasingly critical for enhancing communication for the deaf and hard-of-hearing community. We review several studies, showcasing algorithms with accuracies ranging from 63.5 to 99.6%. Building on these works, we introduce a novel algorithm that has been rigorously tested and has demonstrated a perfect accuracy of 99.7%. Our proposed algorithm utilizes a sophisticated convolutional neural network architecture that outperforms existing models. This work details the methodology of the proposed system, which includes preprocessing, feature extraction, and a multi-layered CNN approach. The remarkable performance of our algorithm sets a new benchmark in the field and suggests significant potential for real-world application in assistive technologies. We conclude by discussing the impact of these findings and propose directions for future research to further improve the accessibility and effectiveness of sign language recognition systems."

	2024	BIO Web of Conferences
"The advancement of assistive communication technology for the deaf and hard-ofhearing community is an area of significant research interest. In this study, we present a Convolutional Neural Network (CNN) model tailored for the recognition of Arabic Sign Language (ArSL). Our model incorporates a meticulous preprocessing pipeline that transforms input images through grayscale conversion, Gaussian blur, histogram equalization, and resizing to standardize input data and enhance feature visibility. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are employed for feature extraction to retain critical discriminat ive information while reducing dimensionality. The proposed CNN architecture leverages a blend of one-dimensional convolutional layers, max pooling, Leaky ReLU activation functions, and Long Short-Term Memory (LSTM) layers to efficiently capture both spatial and temporal patterns within the data. Our experiments on two separate datasets—one consisting of images and the other of videos—demonstrate exceptional recognition rates of 99.7% and 99.9%, respectively. These results significantly surpass the performance of existing models referenced in the literature. This paper discusses the methodologies, architectural considerations, and the training approach of the proposed model, alongside a comparative analysis of its performance against previous studies. The research outcomes suggest that our model not only sets a new benchmark in sign language recognition but also offers a promising foundation for the development of real-time, assistive sign language translation tools. The potential applications of such technology could greatly enhance communication accessibility, fostering greater inclusion for individuals who rely on sign language as their primary mode of communication. Future work will aim to expand the model's capabilities to more diverse datasets and investigate its deployment in practical, everyday scenarios to bridge the communication gap for the deaf and hard of hearing community."

	2018	Engineering and Technology Journal
"This work consists of three stages: the first stage is the collection of data (images) for 26 letters alphabet, which is 1040 images, 40 for each class (letter). The second stage is the Preprocessing (segmentation, filtering, crop, convert to a binary image and feature extraction) and the last stage is the classy one. Two filters have been used median and wiener2 to treatment two types of noise; the salt and pepper and Gaussian noise respectively. The classification has been satisfied by using an n-tuple classifier algorithm is 93.8462%, while it is 93.2692% with salt and pepper noise. In this context, the recognition is 94.2308% when the median filter is used for salt & Pepper noise, while, it is 93.8462% when to use a Wiener2 filter with Gaussian noise. The optimal tuple size is 4, and suitable training pattern is 40 per each class. "

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