Faculty of Engineering, Applied Science and Technology

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A 3G/WiFi-enabled 6LoWPAN-based U-healthcare system for ubiquitous real-time monitoring and data logging
(IEEE Computer Society, 2014) Tabish, Rohan; Ghaleb, Abdulaziz M.; Hussein, Rima; Touati, Farid; Mnaouer, Adel Ben; Khriji, Lazhar; Rasid, Mohd Fadlee A.
Ubiquitous healthcare (U-healthcare) systems are expected to offer flexible and resilient high-end technological solutions enabling remote monitoring of patients health status in real-time and provisioning of feedback and remote actions by healthcare providers. In this paper, we present a 6LowPAN based U-healthcare platform that contributes to the realization of the above expectation. The proposed system comprises two sensor nodes sending temperature data and ECG signals to a remote processing unit. These sensors are being assigned an IPv6 address to enable the Internet-of-Things (IoT) functionality. A 6LowPAN-enabled edge router, connected to a PC, is serving as a base station through a serial interface, to collect data from the sensor nodes. Furthermore, a program interfacing through a Serial-Line-Internet-Protocol (SLIP) and running on the PC provides a network interface that receives IPv6 packets from the edge router. The above system is enhanced by having the application save readings from the sensors into a file that can be downloaded by a remote server using a free Cloud service such as UbuntuOne. This enhancement makes the system robust against data loss especially for outdoor healthcare services, where the 3G/4G connectivity may get lost because of signal quality fluctuations. The system provided a proof of concept of successful remote U-healthcare monitoring illustrating the IoT functionality and involving 3G/4G connectivity while being enhanced by a cloud-based backup. © 2014 IEEE.
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, Amril
The 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.
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, Firuz
Understanding 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)
A graphical user interface simulator for wireless sensor networks lifetime estimation.
(2010) Ben Salem M.; Hamam H.
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, Sherif
With 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.
A survey of blockchain-based solutions for IoTs, VANETs, and FANETs
(IGI Global, 2021-06-11) Abdelhafidh, Maroua; Charef, Nadia; Mnaouer, Adel Ben; Chaari, Lamia
Recently, the internet of things (IoT) has gained popularity as an enabling technology for wireless connectivity of mobile and/or stationary devices providing useful services for the general public in a collaborative manner. Mobile ad-hoc networks (MANETs) are regarded as a legacy enabling technology for various IoT applications. Vehicular ad-hoc networks (VANETs) and flying ad-hoc networks (FANETs) are specific extensions of MANETs that are drivers of IoT applications. However, IoT is prone to diverse attacks, being branded as the weakest link in the networking chain requiring effective solutions for achieving an acceptable level of security. Blockchain (BC) technology has been identified as an efficient method to remedy IoT security concerns. Therefore, this chapter classifies the attacks targeting IoT, VANETs, and FANETs systems based on their vulnerabilities. This chapter explores a selection of blockchain-based solutions for securing IoT, VANETs, and FANETs and presents open research directions compiled out of the presented solutions as useful guidelines for the readers. © 2021, IGI Global.
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
(Springer, 2023) Elreedy, Dina; Atiya, Amir F.; Kamalov, Firuz
Class 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).
Advanced key distribuation center algorithm (AKDC)
(2010) Abuazab, Hussam Hussein; Dahmane, Adel Omar; Hamam, Habib
AI-based Energy Model for Adaptive Duty Cycle Scheduling in Wireless Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Charef, Nadia; Mnaouer, Adel Ben; Bouachir, Ouns
The vast distribution of low-power devices in IoT applications requires robust communication technologies that ensure high-performance level in terms of QoS, light-weight computation, and security. Advanced wireless technologies (i.e. 5G and 6G) are playing an increasing role in facilitating the deployment of IoT applications. To prolong the network lifetime, energy harvesting is an essential technology in wireless networks. Nevertheless, maintaining energy sustainability is difficult when considering high QoS requirements in IoT. Therefore, an energy management technique that ensures energy efficiency and meets QoS is needed. Energy efficiency in duty cycling solutions needs novel energy management techniques to address these challenges and achieve a trade-off between energy efficiency and delay. Predictive models (i.e., based on AI and ML techniques) represent useful tools that encapsulate the stochastic nature of harvested energy in duty cycle scheduling. The conventional predictive model relies on environmental parameters to estimate the harvested energy. Instead, Artificial Intelligence (AI) allows for recursive prediction models that rely on past behavior of harvested and consumed energy. This is useful to achieve better precision in energy estimation and extend the limit beyond predictive models directed solely for energy sources that exhibit periodic behavior. In this paper, we explore the usage of a ML model to enhance the performance of duty cycle scheduling. The aim is to improve the QoS performance of the proposed solution. To assess the performance of the proposed model, it was simulated using the INET framework of the OMNet++ simulation environment. The results are compared to an enhanced IEEE 802.15.4 MAC protocol from the literature. The results of the comparative study show clear superiority of the proposed AI-based protocol that testified to better use of energy estimation for better management of the duty cycling at the MAC sublayer. © 2021 IEEE.
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, Firuz
Recent 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.
Arithmetic properties of complex fibonacci numbers and fibonacci quaternions
(SAS International Publications, 2021-09) Leung, Ho-Hon; Kamalov, Firuz
In 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.
Artificial intelligence implication on energy sustainability in Internet of Things: A survey
(Elsevier Ltd, 2023-03) Charef, Nadia; Ben Mnaouer, Adel; Aloqaily, Moayad; Bouachir, Ouns; Guizani, Mohsen
Audio steganalysis based on lossless data-compression techniques
(Springer Nature Switzerland AG, 2012) Djebbar, Fatiha; Ayad, Beghdad
In 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.
Auditory-based subband blind source separation using sample-by-sample and Infomax algorithms
(2010) Salem, Abderraouf Ben; Selouani, Sid Ahmed; Hamam, Habib
We 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.
Autism screening: an unsupervised machine learning approach
(Springer, 2022-12) Thabtah, Fadi; Spencer, Robinson; Abdelhamid, Neda; Kamalov, Firuz; Wentzel, Carl; Ye, Yongsheng; Dayara, Thanu
Autocorrelation for time series with linear trend
(Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz; Thabtah, Fadi; Gurrib, Ikhlaas
The 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.
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.
An Autonomous Multi-Variable Outdoor Air Quality Mapping Wireless Sensors IoT Node for Qatar
(Institute of Electrical and Electronics Engineers Inc., 2020-06) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al-Hitmi, Mohammed Abdulla E; Crescini, Damiano; Mnaouer, Adel Ben
In all outdoor long haul air quality monitoring and mapping applications, sensing accuracy, the sustainability of telemetry, and resilience in node systems and operation are major challenges. The severity of these challenges varies depending on the moderateness and harshness of the climate of observation. In this work, an autonomous environmentally powered, sensors self-diagnostic/calibration, and context-aware IoT-based telemetry for multi-variable sensing node is being proposed. In this node, photo-voltaic and piezoelectric energy harvesters contributed to self-calibration and sustainable measurement of temperature (in °C), humidity (in %), pressure (in bar), geo-position (in NMEA format), volatile organic compounds-VOC (in ppm), particulate matter PM (in ppm), ozone (in Dobson Unit), Carbon mono-oxide (in ppm), Nitrogen dioxide (in ppm), and Sulphur dioxide (in ppm). Results have shown that the proposed system worked autonomously for days and optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University. © 2020 IEEE.
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, Fadi
Time 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.
An autoregressive time delay neural network for speech steganalysis
(Institute of Electrical and Electronics Engineers Inc., 2012) Rekik, Siwar; Selouani, Sid-Ahmed; Guerchi, Driss; Hamam, Habib
Hiding a secret message in speech signal, called steganography, is used to provide secure communication. The detection of hidden information in the transmitted message called steganalysis. The purpose of steganalysis is to identify the presence of embedded information, and does not actually attempt to extract or decode the hidden data. An automated method is required for detecting the existence of hidden message, since the huge amount of channeled information. However, the development and evaluation of steganalysis algorithms is a challenging task. In this paper we advocate a new steganalysis technique to classify a speech as having hidden information or not, using a powerful and sophisticated classifier called Autoregressive Time Delay Neural Network (AR-TDNN). The originality of this AR-TDNN is its quite ability to detect secret messages hidden with different steganographic algorithms, although the variation of detection rate depends on the particular hiding techniques and amount of hidden information. © 2012 IEEE.

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