Ali Sami Azeez Al-Itbi
@mtu.edu.iq
Informatics Department
Middle Technical University
EDUCATION
Bs.c. Computer science 2007.
M.Sc. Computer 2014.
PhD. Computer Science student.
RESEARCH INTERESTS
NLP, Deep Learning, Machine Learning.
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Salam Muhsin Arnoos, Ali Mohammed Sahan, Alla Hussein Omran Ansaf, and Ali Sami Al-Itbi
Springer Nature Singapore
Ali Sahan Sahan, Nisreen Jabr, Ahmed Bahaaulddin, and Ali Al-Itb
International Journal of Advances in Soft Computing and its Applications Alzaytoonah University of Jordan
Abstract Many studies refer that the figure knuckle comprises unique features. Therefore, it can be utilized in a biometric system to distinguishing between the peoples. In this paper, a combined global and local features technique has been proposed based on two descriptors, namely: Chebyshev Fourier moments (CHFMs) and Scale Invariant Feature Transform (SIFT) descriptors. The CHFMs descriptor is used to gaining the global features, while the scale invariant feature transform descriptor is utilized to extract local features. Each one of these descriptors has its advantages; therefore, combining them together leads to produce distinct features. Many experiments have been carried out using IIT-Delhi knuckle database to assess the accuracy of the proposed approach. The analysis of the results of these extensive experiments implies that the suggested technique has gained 98% accuracy rate. Furthermore, the robustness against the noise has been evaluated. The results of these experiments lead to concluding that the proposed technique is robust against the noise variation. Keywords: finger knuckle, biometric system, Chebyshev Fourier moments, scale invariant feature transform, IIT-Delhi knuckle database.
Ali Sami Al-Itbi, Ahmed Bahaaulddin A. Alwahhab, and Ali Mohammed Sahan
Computer Systems Science and Engineering Computers, Materials and Continua (Tech Science Press)
Notwithstanding the discovery of vaccines for Covid-19, the virus's rapid spread continues due to the limited availability of vaccines, especially in poor and emerging countries. Therefore, the key issues in the present COVID-19 pandemic are the early identification of COVID-19, the cautious separation of infected cases at the lowest cost and curing the disease in the early stages. For that reason, the methodology adopted for this study is imaging tools, particularly computed tomography, which have been critical in diagnosing and treating the disease. A new method for detecting Covid-19 in X-rays and CT images has been presented based on the Scatter Wavelet Transform and Dense Deep Neural Network. The Scatter Wavelet Transform has been employed as a feature extractor, while the Dense Deep Neural Network is utilized as a binary classifier. An extensive experiment was carried out to evaluate the accuracy of the proposed method over three datasets: IEEE 80200, Kaggle, and Covid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810, respectively. The analysis of the result refers that the proposed methods achieved high accuracy of 98%. The proposed model results show an excellent outcome compared to other methods in the same domain, such as (DeTraC) CNN, which achieved only 93.1%, CNN, which achieved 94%, and stacked Multi-Resolution CovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.
Ali Mohammed Sahan, Ali Sami Al-Itbi, and Jawad Sami Hameed
Periodicals of Engineering and Natural Sciences International University of Sarajevo
Ali Mohammed Sahan and Ali Sami Al-Itbi
Lecture Notes in Electrical Engineering Springer Singapore