Dr. Yasmine Mazin Tabra
@nahrainuniv.edu.iq
college of information engineering
al nahrain university
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yasmine M. Tabra and Furat N. Tawfeeq
Institute of Advanced Engineering and Science
Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.
Yasmine M. Tabra and Bayan Mahdi Sabbar
Institute of Advanced Engineering and Science
<span lang="EN-US">The revolution in 5G mobile systems require changes to how image is handled. These changes are represented by the required processing time, the amount of space for uploading and downloading. In this paper, a development on WT (Wavelet Transform) along with LM-SPIHT (Listless-Modified Set Partitioning in Hierarchical tree) coding and with additional level of Runlength encoding for image compression has been proposed. The new implementation reduces the amount of data needed to be stored in several stages, also the amount of time required for processing. The compression has been implemented using VHDL (Very High Descriptive Language) on netFPGA-1G-CLM Kintex-7 board. The new implementation results show a reduction in the complexity as processing time.</span>
Yasmine M. Tabra and Bayan Sabbar
Institute of Advanced Engineering and Science
<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>
Bayan Mahdi Sabar and Yasmine M. Tabra
Universitas Ahmad Dahlan
Providing simple and low complexity algorithms for estimating the direction of arrival in large systems using Massive MIMO is considered an important issue. In this paper a method with reduced complexity was proposed to estimate the direction of arrival in FD- MMIMO. The Separated Steering Matrix (SSM) algorithm uses two separated equations for estimating elevation and azimuth angles of Multi-users. This method reduces the complexity of calculating the covariance matrix by decreasing the size of this matrix. This technique is tested using 2D-MUSIC algorithm. Since the mobility of devices affects the accuracy of direction estimation, thus the effect of the initial phase of transmitted signal from mobile device is tested.
Mumtaz M. Al Mukhtar and Yasmine M. Tabra
Inderscience Publishers
The volume of mass unsolicited e-mail, often known as spam, has recently increased enormously and has become a serious threat to not only internet but also to society. It is challenging to develop spam filters that can effectively eliminate the increasing volume of unwanted e-mails automatically. The present work presents a combination of support vector machine classifier for non-linear data (using an eligible kernel function) with appropriate data pre-processing as a spam filter. Data pre-processing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels. The pre-processing steps include HTML removal, HTML replacement, de-obfuscation and stop-word-remover. The results obtained using the pre-processing level showed an improvement in the classification level. The estimated training and classification time for different document sizes indicate that the adopted method is practical and computationally efficient. Experimental results show that the approach can enhance the filtering performance effectively.