Department of Electrical Engineering

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    Numerical computing in engineering mathematics
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kamalov, Firuz ; Leung, Ho-Hon
    The rapid advances in technology over the last decade have significantly altered the nature of engineering knowledge and skills required in the modern industries. In response to the changing professional requirements, engineering institutions have updated their curriculum and pedagogical practices. However, most of the changes in the curriculum have been focused on the core engineering courses without much consideration for the auxiliary courses in mathematics and sciences. In this paper, we aim to propose a new, augmented mathematics curriculum aimed at meeting the requirements of the modern, technology-based engineering workplace. The proposed updates require minimal resources and can be seamlessly integrated into the existing curriculum. © 2022 IEEE.
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    Machine learning applications to Covid-19: a state-of-the-art survey
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kamalov, Firuz ; Cherukuri, Aswani Kumar ; Thabtah, Fadi
    There exists a large and rapidly growing body of literature related to applications of machine learning to Covid-19. Given the substantial volume of research, there is a need to organize and categorize the literature. In this paper, we provide the most up-to-date review as of the beginning of 2022. We propose an application-based taxonomy to group the existing literature and provide an analysis of the research in each category. We discuss the progress as well as the pitfalls of the existing research, and propose keys for improvement. © 2022 IEEE.
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    Evaluation of Arabic-Based Contextualized Word Embedding Models
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yagi, Sane Mo ; Mansour, Youssef ; Kamalov, Firuz ; Elnagar, Ashraf
    The distributed representation of words, as in Word2Vec, FastText, and GloVe, results in the production of a single vector for each word type regardless of the polysemy or homonymy that many words may have. Context-sensitive representation as implemented in deep learning neural networks, on the other hand, produces different vectors for the multiple senses of a word. Several contextualized word embeddings have been produced for the Arabic language (e.g., AraBERT, QARiB, AraGPT, etc.). The majority of these were tested on a few NLP tasks but there was no direct comparison between them. As a result, we do not know which of these is most efficient and for which tasks. This paper is a first step in an endeavor to establish evaluation criteria for them. It describes 24 such embeddings, then conducts exploratory intrinsic and extrinsic evaluation of them. Afterwards, it tests relational knowledge in them, covering four semantic relations: colors of fruits, capitals of countries, causation, and general information. It also evaluates the utility of these models in Named Entity Recognition and Sentiment Analysis tasks. It has been demonstrated here that AraBERTv02 and MARBERT are the best on both types of evaluation; therefore, both are recommended for fine-tuning Arabic NLP tasks. The ultimate conclusion is that it is feasible to test higher order reasoning relations in these embeddings. © 2021 IEEE
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    Autoencoder-based Intrusion Detection System
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz ; Zgheib, Rita ; Leung, Ho Hon ; Al-Gindy, Ahmed ; Moussa, Sherif
    Given the dependence of the modern society on networks, the importance of effective intrusion detection systems (IDS) cannot be underestimated. In this paper, we consider an autoencoder-based IDS for detecting distributed denial of service attacks (DDoS). The advantage of autoencoders over traditional machine learning methods is the ability to train on unlabeled data. As a result, autoencoders are well-suited for detecting unknown attacks. The key idea of the proposed approach is that anomalous traffic flows will have higher reconstruction loss which can be used to flag the intrusions. The results of numerical experiments show that the proposed method outperforms benchmark unsupervised algorithms in detecting DDoS attacks. © 2021 IEEE.
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    Time series signal recovery methods: Comparative study
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz ; Sulieman, Hana
    Signal data often contains missing values. Effective replacement (imputation) of the missing values can have significant positive effects on processing the signal. In this paper, we compare three commonly employed methods for estimating missing values in time series data: forward fill, backward fill, and mean fill. We carry out a large scale experimental analysis using 3, 600 AR(1)-based simulated time series to determine the optimal method for estimating missing values. The results of the numerical experiments show that the forward and backward fill methods are better suited for times series with large positive correlations, while the mean fill method is better suited for times series with low or negative correlations. The extensive and exhaustive nature of the numerical experiments provides a definitive answer to the comparison of the three imputation methods. © 2021 IEEE.