Alyaa mahdi
@alzahraa.edu.iq
IT Center
Al-Zahraa University for Women
EDUCATION
Master degree in Information Technology
RESEARCH INTERESTS
Artificial Intelligent, Deep Learning, Convolutional Neural Networks, Image Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Sarah R. Hashim, Rusul A. Enad, Alyaa M. Al-khafagi, and Noor Kamil Abdalhameed
Institute of Advanced Engineering and Science
Wireshark is easy for using as a packet inspection tool, in additional the feature of packets colorizing is easy for a various type of traffic. This paper exemplifies how Wireshark is used in networks as a tool. To clarify the effectiveness of malicious packet identification in any network, an experiment was conducted. Using the Wireshark program, testing was carried out in real time through experimentation and analysis. Inferences were drawn that clearly show Wireshark's capabilities as a tool in a powerful system for discovering the breach. The functionality of Wireshark is to analyze the network protocol and its open-source features for enabling the addition of likely tasks in the detecting devices were emphasized. Wireshark's skills for handling and interpreting packet data have been highlighted and the access control list (ACL) filtering has been the main application of Wireshark.
Alyaa Mahdi Al-khafagy, Sarah Rafil Hashim, and Rusul Ali Enad
Institute of Advanced Engineering and Science
Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse Transcription- Polymerase Chain Reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and Reverse Transcription- Polymerase Chain Reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5 %. The proposed strategy appears excellent in terms of accuracy when compared with later strategies.
Israa Hadi and Alyaa Mahdi
Institute of Advanced Engineering and Science
<p>One of the most common approaches to address the partial face recognition challenge is to crop the full face image into segments. The problem is how the full face image must be cropped in a uniform way to generate informative segments. The un-blindly strategy was applied in this paper to generate informative segments, it depends on localizing the facial landmarks and selecting the more informative facial points as a key points, as more as the k-nearest neighbor concept was explored to select the k nearest landmark points to the key points. Two landmark localization techniques were experimented, the suitable technique resulted in segments which are overlapped due to the supervised clustering technique that explored in this paper to cover important biometric face regions, not repeated and covered most probabilities in which it is possible to distinguish the query face from the available part of it.<strong><em></em></strong></p>