Department of Electrical Engineering
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Item A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process(Springer Science and Business Media Deutschland GmbH, 2022) Kamalov, Firuz; Gurrib, Ikhlaas; Moussa, Sherif; Nazir, AmrilThe Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A computational numerical performance for solving the mathematical epidemiological model based on influenza disease(Elsevier B.V., 2022-09) Jain, Sonal; Leung, Ho-Hon; Kamalov, FiruzUnderstanding epidemic propagation patterns and assessing disease control measures require the use of mathematical and computational methodologies. In recent years, complexity science, management science, sociology, and computer science have all been progressively merged with epidemiology. The interdisciplinary collaboration has sped up the development of computational and mathematical methods for simulating epidemics. The model with the classical time derivative in the influenza disease model is formulated with the Caputo (power-law kernel), Caputo–Fabrizio (exponential kernel), and the novel Atangana–Baleanu fractional derivatives which combined both nonlocal and non-singular properties. Also this article presents the boundness and positiveness Solutions for the influenza model. The analysis of the equilibrium point is also given. Various published articles have utilized the reproductive number notion to investigate disease-spread stability. There were certain conditions proposed to predict whether there would be stability or instability. It was also advised that an analysis be conducted to discover the conditions under which infectious classes will grow or die out. Some authors pointed out that the reproductive number is limited, including its inability to fairly aid in understanding distribution patterns. The concept of strength number and analysis of derivatives of mathematical models were presented to help in understanding the disease model. Further, the stability of disease-free and endemic equilibrium is presented. Finally, a numerical solution with simulation is given. We hope to use these extra studies in a basic model to forecast the future of this research. © 2022 The Author(s)Item A Future Approach For Energy Harvesting In Trains Using Piezoelectricity(Institute of Electrical and Electronics Engineers Inc., 2023) Majeed, Salih Rashid; Al-Thaedan, Abbas; Shakir, Zaenab; Shafy, Amir A. Omran; Alsabah, Ruaa; Al-Sabbagh, AliItem A graphical user interface simulator for wireless sensor networks lifetime estimation.(2010) Ben Salem M.; Hamam H.Item A new watermarking scheme for digital videos using DCT(Walter de Gruyter GmbH, 2022-01-01) Al-Gindy, Ahmed; Omar, Aya Al-Chikh; Mashal, Omar; Shaker, Yomna; Alhogaraty, Eslam; Moussa, SherifWith the advent of high-speed broadband Internet access, the need to protect digital videos is highly recommended. The main objective of this study is to propose an adaptive algorithm for watermarked digital videos in the frequency domain based on discrete cosine transform (DCT). The watermark signature image is embedded into the whole frame of the video. The green channel of the RGB frame is selected for the embedding process using the DCT algorithm as it shows the recommended quality of the watermarked frames. The experiment results indicate that the proposed algorithm shows robustness and high quality of the watermarked videos by testing various strength values Δ for different videos. It offers resistance against different types of attacks. © 2022 Ahmed Al-Gindy et al., published by De Gruyter.Item A NOTE ON THE AUTOCOVARIANCE OF p-SERIES LINEAR PROCESS(Canadian University of Dubai, 2020-12-01) Kamalov, FiruzIn this note, we provide tight boundaries for the autocovariance function of a stochastic linear process with p-series coefficients. © 2020, Canadian University of Dubai. All rights reserved.Item A note on time series differencing(Canadian University of Dubai, 2021-05-11) Kamalov, FiruzDifferencing is one of the key tools time series analysis. It is com-monly used to obtain stationary time series. In this note, we show that the nth difference of a weakly stationary time series is weakly stationary. Similarly we prove that the nth difference of a strictly stationary time series is strictly stationary. We also consider the effect of differencing on the time series auto-covariance. © 2021, Canadian University of Dubai. All rights reserved.Item A Secure Peer-to-Peer Image Sharing Using Rubik's Cube Algorithm and Key Distribution Centre(Sciendo, 2023-09-01) Cherukuri, Aswani Kumar; Sannuthi, Shria; Elagandula, Neha; Gadamsetty, Rishita; Singh, Neha; Jain, Arnav; Sumaiya Thaseen I.; Priya V.; Jonnalagadda, Annapurna; Kamalov, FiruzIn this work, we build upon an implementation of a peer-to-peer image encryption algorithm: "Rubik's cube algorithm". The algorithm utilizes pixel-level scrambling and XOR-based diffusion, facilitated through the symmetric key. Empirical analysis has proven this algorithm to have the advantage of large key space, high-level security, high obscurity level, and high speed, aiding in secure image transmission over insecure channels. However, the base approach has drawbacks of key generation being handled client-side (at nodes) and the process is time-consuming due to dynamically generating keys. Our work solves these issues by introducing a Key Distribution Center (KDC) to distribute symmetric keys for transmission, increasing confidentiality, and reducing key-generation overhead on nodes. Three approaches utilizing the KDC are presented, communicating the dimensions with KDC to generate keys, standardizing any image to fixed dimensions to standardize key-generation, and lastly, using a single session key which is cyclically iterated over, emulating different dimensions. © 2023 Aswani Kumar Cherukuri et al., published by Sciendo.Item A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning(Springer, 2023) Elreedy, Dina; Atiya, Amir F.; Kamalov, FiruzClass imbalance occurs when the class distribution is not equal. Namely, one class is under-represented (minority class), and the other class has significantly more samples in the data (majority class). The class imbalance problem is prevalent in many real world applications. Generally, the under-represented minority class is the class of interest. The synthetic minority over-sampling technique (SMOTE) method is considered the most prominent method for handling unbalanced data. The SMOTE method generates new synthetic data patterns by performing linear interpolation between minority class samples and their K nearest neighbors. However, the SMOTE generated patterns do not necessarily conform to the original minority class distribution. This paper develops a novel theoretical analysis of the SMOTE method by deriving the probability distribution of the SMOTE generated samples. To the best of our knowledge, this is the first work deriving a mathematical formulation for the SMOTE patterns’ probability distribution. This allows us to compare the density of the generated samples with the true underlying class-conditional density, in order to assess how representative the generated samples are. The derived formula is verified by computing it on a number of densities versus densities computed and estimated empirically. © 2023, The Author(s).Item A visualization approach to multiplicative reasoning and geometric measurement for primary-school students-a pilot study(City University of New York, 2022-12) Jain, Sonal; Leung, Ho-Hon; Kamalov, FiruzUnderstanding the concept of area requires an understanding of the relationship between geometry and multiplication. The multiplicative reasoning required to find the areas of regular figures is used in many courses in elementary mathematical education. This paper explores various methods in which multiplicative reasoning is incorporated into the measurement of area. The main goal is to provide tasks that encourage the application of multiplicative reasoning when students are asked to measure the areas of geometric figures. Student performance is analyzed in two pilot studies of the relationship between geometric measurement and multiplicative reasoning. © 2022 City University of New York. All rights reserved.Item An Ensemble-Based Machine Learning Model for Emotion and Mental Health Detection(World Scientific, 2022) Jonnalagadda, Annapurna; Rajvir, Manan; Singh, Shovan; Chandramouliswaran S.; George, Joshua; Kamalov, FiruzRecent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person's emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person's emotions. However, these solutions often rely on techniques that invade people's privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people's emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user's typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user's emotions. The proposed solution has various applications in today's world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members. © 2023 World Scientific Publishing Co.Item Arithmetic properties of complex fibonacci numbers and fibonacci quaternions(SAS International Publications, 2021-09) Leung, Ho-Hon; Kamalov, FiruzIn this paper, we investigate certain arithmetic properties of complex Fibonacci numbers and Fibonacci quaternions. More specifically, we look at the divisibility properties of complex Fibonacci numbers and Fibonacci quaternions. Our results make use of some well-known Fibonacci identities. Since quaternions are non-commutative algebra, extra care has been taken to investigate the various divisibility properties of the Fibonacci quaternions. © SAS International Publications.Item Audio steganalysis based on lossless data-compression techniques(Springer Nature Switzerland AG, 2012) Djebbar, Fatiha; Ayad, BeghdadIn this paper, we introduce a new blind steganalysis method that can reliably detect modifications in audio signals due to steganography. Lossless data-compression ratios are computed from the testing signals and their reference versions and used as features for the classifier design. Additionally, we propose to extract additional features from different energy parts of each tested audio signal to retrieve more informative data and enhance the classifier capability. Support Vector Machine (SVM) is employed to discriminate between the cover- and the stego-audio signals. Experimental results show that our method performs very well and achieves very good detection rates of stego-audio signals produced by S-tools4, Steghide and Hide4PGP. © 2012 Springer-Verlag.Item Auditory-based subband blind source separation using sample-by-sample and Infomax algorithms(2010) Salem, Abderraouf Ben; Selouani, Sid Ahmed; Hamam, HabibWe present a new subband decomposition method for the separation of convolutive mixtures of speech. This method uses a sample-by-sample algorithm to perform the subband decomposition by mimicking the processing performed by the human ear. The unknown source signals are separated by maximizing the entropy of a transformed set of signal mixtures through the use of a gradient ascent algorithm. Experimental results show the efficiency of the proposed approach in terms of signal-to-interference ratio. Compared with the fullband method that uses the Infomax algorithm, our method shows an important improvement of the output signal-to-noise ratio when the sensor inputs are severely degraded by additive noise.Item Autism screening: an unsupervised machine learning approach(Springer, 2022-12) Thabtah, Fadi; Spencer, Robinson; Abdelhamid, Neda; Kamalov, Firuz; Wentzel, Carl; Ye, Yongsheng; Dayara, ThanuItem Autocorrelation for time series with linear trend(Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz; Thabtah, Fadi; Gurrib, IkhlaasThe autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection. © 2021 IEEE.Item Autoencoder-based Intrusion Detection System(Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz; Zgheib, Rita; Leung, Ho Hon; Al-Gindy, Ahmed; Moussa, SherifGiven 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.Item Autoregressive and neural network models: A comparative study with linearly lagged series(Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz; Gurrib, Ikhlaas; Thabtah, FadiTime series analysis such as stock price forecasting is an important part of financial research. In this regard, autoregressive (AR) and neural network (NN) models offer contrasting approaches to time series modeling. Although AR models remain widely used, NN models and their variant long short-term memory (LSTM) networks have grown in popularity. In this paper, we compare the performance of AR, NN, and LSTM models in forecasting linearly lagged time series. To test the models we carry out extensive numerical experiments based on simulated data. The results of the experiments reveal that despite the inherent advantage of AR models in modeling linearly lagged data, NN models perform just as well, if not better, than AR models. Furthermore, the NN models outperform LSTMs on the same data. We find that a simple multi-layer perceptron can achieve highly accurate out of sample forecasts. The study shows that NN models perform well even in the case of linearly lagged time series. © 2021 IEEE.Item Batch-based power-controlled channel assignment for improved throughput in software-defined networks(Institute of Electrical and Electronics Engineers Inc., 2019) Salameh, Haythem Bany; Musa, Ahmed; Outoom, Ruba; Halloush, Rami; Aloqaily, Moayad; Jararweh, YaserSoftware-defined networking (SDN) along with transmission power control (TPC) have a great potential in enabling efficient wireless networking. Power control aims at increasing network throughput, while SDN provides cognition and intelligent capabilities to network devices. The key challenge in enabling efficient operation of such networks is how to perform efficient power-controlled MAC protocols that includes channel assignment and power allocation such that network throughput is enhanced while using the least number of channels. Traditional MAC protocols for SDNs employ an exclusive channel-occupancy between neighboring secondary users (SUs), which significantly limits network performance. In this paper, we develop a novel power-controlled spectrum access protocol for SDNs based on the interference-channel occupancy model with the objective of increasing network throughput. It allows several concurrent interference-limited transmissions to simultaneously proceed over the same channel in the same neighborhood. Unlike most of previous power-control MAC protocols that perform the channel assignment and power allocation sequentially, our protocol simultaneously makes distributed channel and power assignment decisions for multiple SU transmissions (batch-based method). Batching can be achieved by using an admission control window for SUs to exchange their collision-avoidance control information. Simulation results reveal that compared with CSMA/CA variants, our protocol greatly improve spectrum efficiency, which improves network throughput while reducing energy consumption. © 2019 IEEE.Item Cluster aware mobility encounter dataset enlargement(Institute of Electrical and Electronics Engineers Inc., 2019) Haldar, Rajarshi; Bacanli, Salih Safa; Aloqaily, Moayad; Mnaouer, Adel Ben; Turgut, DamlaThe recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset. © 2019 IEEE.