Localizing-ground Transmitters Using Airborne Antenna Array by Daffalla, Mirghani Moutaman

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Fuzzy Logic Based Patients' Monitoring System by Al-Dmour, Jumana

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Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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Lung nodule classification utilizing support vector machines by Mousa, W.A.H.

“…Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such as neural network algorithms. …”
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Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice by Turker Tuncer (16677966)

“…The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. …”

An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic by Saeed Al-Maroof, Rana

“…The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. …”
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Predicting stability of classes in an object-oriented system by Azar, D.

“…Many metrics have been proposed to assess these software attributes and for this purpose, prediction models have been widely used. However, in almost all cases, these models were not efficient when used to predict the quality characteristics (stability or other) of new unseen software as their prediction accuracy decreases significantly. …”
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UML-based regression testing for OO software by Mansour, Nashat

“…We assume a test suite that contains both unit and system test cases. Based on the software changes reflected in the class and the interaction overview diagrams, our proposed technique selects test cases in phases. …”
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Approximate XML structure validation based on document–grammar tree similarity by Tekli, Joe

“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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A simple approach for testing web service based applications by Tarhini, Abbas

“…Three sets of test sequences are generated from the WSDL files, the TLTS and the TPG representing the integrated components and the whole web application. Test cases are executed automatically using a test execution algorithm and a test framework is also presented. …”
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Approximate XML structure validation technical report by Tekli, Joe

“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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DAP: A dataset-agnostic predictor of neural network performance by Sui Paul Ang (18460605)

“…To this end, we propose a dataset-agnostic regression framework that uses a novel dual-LSTM model and a new dataset difficulty feature. The experimental results show that both tasks above are indeed feasible, and the proposed method outperforms the existing techniques in all experimental cases. …”

Image-CNN Based Process Control of Profile by Zeinab, Zeinab Jihad

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Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems by Jaber Al-Khori (16855470)

“…We evaluate the system performance in terms of secrecy capacity (SC) and outage probability (OP) for two network scenarios, namely non-cooperative (NCPS) and cooperative power saving (CPS) models. The NCPS case assumes fixed power at both source and relay while the CPS case assumes total average power shared between the source and relay. …”

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis by Hassan, Ali

“…We experiment with multiple attackers—Text-bugger, Text-fooler, PWWS—on several architectures—MLP, CNN, LSTM, Hybrid CNN-RNN, BERT—trained for different classification tasks—IMDB sentiment classification, fake-news classification, AG news topic classification—under different threat models—Con-Detect-blind attacks, Con-Detect-aware attacks, and Con-Detect-adaptive attacks—and show that Con-Detect can reduce the attack success rate (ASR) of different attacks from 100% to as low as 0% for the best cases and ≈70% for the worst case. …”
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The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

“…To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. …”

Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…We experiment with multiple attackers—Text-bugger, Text-fooler, PWWS—on several architectures—MLP, CNN, LSTM, Hybrid CNN-RNN, BERT—trained for different classification tasks—IMDB sentiment classification, fake-news classification, AG news topic classification—under different threat models—Con-Detect-blind attacks, Con-Detect-aware attacks, and Con-Detect-adaptive attacks—and show that Con-Detect can reduce the attack success rate (ASR) of different attacks from 100% to as low as 0% for the best cases and ≈70% for the worst case. …”

Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

“…To categorize electronic text in these two cases, deep learning models such as convolutional neural networks and recurrent neural networks and a combination of CNN-RNN were trained on this data. …”
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