Canadian University Dubai Faculty Publications

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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.
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    Adaptively Intelligent Meta-search Engine with Minimum Edit Distance
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kanwal, Asma ; Septyanto, Arif Wicaksono ; Muhammad, Muhammad Hassan Ghulam ; Said, Raed A. ; Farrukh, Muhammad ; Ibrahim, Muhammad
    In current era retrieval of information has attained high demand due to spectra from websites has abundantly increased. Search Engines are basic tools to get information from the web of data and show irrelevant data causing wastage of time. Considering the fact that time is a precious commodity and is the hallmark of everything around us. To overcome the wastage of time and for its optimum utilization meta-search engines are design. Meta-Search Engine use to fetch relevant data. Existing meta-search engine shows their relevant data based on keywords as well as a semantic query. Semantic query-based results still have some irrelevancy in the results. In this paper, we analyze the semantic query based on machine learning algorithms. This paper hypothesizes improved results through the query expansion mechanism. Author also remove duplicated URLs that come from multiple search engines. Minimum Edit Distance algorithm is used to measure the similarity between titles, snippets and if measuring similarity is more than 0.6 then it must remove that title and snippet. Ranking process, generated retrieval of the relevant document at the top relevant document. Comparative analysis of proposed work is done with existing meta-search engines, overall performance of Intelligent Meta-Search Engine (IMSE) remains 74.17%. © 2022 IEEE.
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    Advertising literacy measurement scale from students' perspective
    (preprints.org, 2021) Rasekh, Nazanin ; Ghasemi, Hamid ; Mataruna-Dos-Santos, Leonardo Jose ; ABdolmaleki, Hossein ; Soheili, Behzad
    Although advertising literacy leads to critical thinking in the face of advertising, but so far, no action has been taken in Iran regarding a tool to measure this type of literacy. And after the investigations, it was determined that although much research has been done on advertising, but the lack of appropriate measurement tools to measure the level of advertising literacy is clearly evident. Therefore, this research provides a valid tool for measuring advertising literacy from students' perspective. In this study, referring to the dimensions of advertising literacy from the perspective of Malmelin (2010) and the views of related professors, a questionnaire was developed and to determine the validity of the structure, confirmatory factor analysis was used. In this study, the statistical population was high school students, considering the impact of advertising in this age range; finally, a tool with four dimensions of informational literacy, aesthetic literacy, rhetorical literacy and promotional literacy was obtained. According to the confirmation of this tool in the present study, it can be used to examine the status of items, their order and prioritization from the perspective of the mentioned population.
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    Ambition at work and career satisfaction : the mediating role of taking charge behavior and the moderating role of pay
    (Emerald Group Publishing Ltd., 2017) El Baroudi, Sabrine ; Fleisher, Chen ; Khapova, Svetlana N. ; Jansen, Paul ; Richardson, Julia
    Purpose: The purpose of this paper is to examine the moderating role of pay in the relationship between employee ambition and taking charge behavior, and its subsequent effects on employee career satisfaction. Design/methodology/approach: A two-wave quantitative investigation was conducted among alumni of a large public university in the Netherlands. Findings: The results show that taking charge behavior mediates the positive relationship between employee ambition and career satisfaction. They also show that pay positively moderates this mediation, such that the relationship between employee ambition and taking charge behavior is stronger when ambitious employees receive an increase in pay, leading to increased career satisfaction. Conversely, a decrease in pay does not moderate ambitious employees’ taking charge behavior and the impact on their career satisfaction. Research limitations/implications: The study draws on self-report data collected in one country: the Netherlands. Practical implications: The study highlights the importance of pay for higher job involvement, demonstrating its impact on taking charge behavior among employees with higher levels of ambition. Originality/value: This is the first empirical study to examine the impact of pay on employees’ taking charge behavior and the subsequent implications for career satisfaction. © 2017, © Emerald Publishing Limited.
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    Analytic Hierarchy Process and Sensitivity Analysis Approach for Social Media Impact on Pharmaceutical Relationship Marketing Tactics
    (Decision Science Institute, 2013) Enyinda, Chris I. ; Ogbuehi, Alphonso O. ; Hamouri, Suhair
    Pharmaceutical relationship marketing (PRM) bodes well with the social media environment. Pharmaceutical industry can build and maintain relationships with consumers through social media. Firms that leverage social media to enhance their PRM tactics will be viewed favorably in terms of trust, transparency, openness, and honesty. This paper explores the sensitivity analysis (SA) of PRM tactics within the social media environment using analytic hierarchy process (AHP) approach. Results revealed customer engagement as the most important PRM tactic, followed by communication, and trust. The performance SA carried out on the PRM tactics showed that the ranking associated with social media channel options remained robust or insensitive to small perturbations.
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    Application of Computational Intelligence and Machine Learning to Conventional Operational Research Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ali, Atif ; Said, Raed A. ; Rizwan, Hafiz Muhammad Amir ; Shehzad, Khurram ; Naz, Imran
    Machine learning and computational intelligence are two methods for achieving this (CI); traditional operational research methods are combined with machine learning-based computational techniques (OR). Students can handle complex decision-making problems thanks to the synergy between those methods and techniques. This research's primary goal is to present and demonstrate potential connections amid the two computational arenas. Using applications, we show how machine learning techniques like fuzzy logic, neural networks and reinforcement learning can be combined to provide a simpler solution to more complex problems than traditional OR methods., which is a research contribution in and of itself. © 2022 IEEE.
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    Are energy block chain currencies affected by the major us energy markets?
    (Econjournals, 2019) Gurrib, Ikhlaas
    While various economies have started to embark on a gradual shift towards renewable sources of energy, energy block chain based crypto currencies have emerged. The purpose of this study is to shed fresh light into whether an energy commodity price index (ENFX) and energy block chain based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Using principal component analysis over daily data of crude oil, heating oil, natural gas, and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only the lagged ENCX was significant in estimating current ENFX values. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX, and one standard deviation shock in ENCX left ENFX unaffected. Both indices had 1 structural break on different dates. Overall findings suggest that while the ENFX and ENCX are good representative of commodity energy prices and energy block chain based cryptos respectively, the two markets are not robust determinants of each other. © 2018, Econjournals. All rights reserved.
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    Are key market players in currency derivatives markets affected by financial conditions?
    (LLC CPC Business Perspectives, 2018) Gurrib, Ikhlaas
    Tis study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous fnancial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efciency, this study compares the impact of current, 1 and 2 week lags of fnancial conditions onto foreign currency futures players' net positions. Te fnancial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. Te US dollar index futures is included as a benchmark, since the fnancial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers' net positions, lagged NFCI captured the highest correlation with both players' net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the fnancial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. Te latter could be explained by FCIs not being affected during this period's event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. Tis suggests the need to look into other components like non-reportable long or short positions in future analysis. © 2018 Ikhlaas Gurrib.
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    Assessing views towards energy sources with social media data: The case of nuclear energy in the UAE
    (MDPI, 2021-11) Contu, Davide ; Elshareif, Elgilani Eltahir ; Gurrib, Ikhlaas
    Insights from the analysis of views towards energy sources are of paramount importance for the setting of successful energy policies, especially in instances where the public might be reluctant towards certain projects’ implementations. This work presents an analysis of social media comments data given in response to posts around the connection to the grid of a nuclear plant reactor in the United Arab Emirates (UAE). We assessed comments on Facebook posts of local and international media, as well those written in response to a post of a social media influencer. We extracted the main themes and performed sentiment analysis. The results indicate the presence of mixed views towards nuclear energy when focusing on comments on international media’s posts as well as on the social media influencer’s post considered, whilst they were very positive when assessing comments to local media. All in all, nuclear waste and previous nuclear accidents appear to be as the top of the mind; at the same time, solar energy is often suggested in the comments as a viable energy source for the UAE. Implications for the communication of nuclear energy developments in social media are discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
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    An assessment of the potential VAT revenue collection for the United Arab Emirates
    (Routledge, 2017) Gurrib, Ikhlaas
    This study analyses the effect of a 5% VAT in the UAE for the period 2018–2022. The methodology includes collection efficiency, standard tax rate and the final consumption expenditure (FCE). Various scenarios are analysed, including a constant 5% VAT for 2018–2022; increasing it by 2.39% yearly; increasing it to reach the maximum 2014 country tax rate of 27%; or increasing it to reach an average tax rate of 19.1%. The collection efficiency values of 0.4–0.7 result in a 2018–22 tax revenue to GDP range of between 1.75 and 7.84%. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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    Associative classification approaches : review and comparison
    (World Scientific Publishing Co. Pte Ltd, 2014) Abdelhamid, Neda ; Thabtah, Fadi
    Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions. © 2014 World Scientific Publishing Co.
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    Associative classification common research challenges
    (Institute of Electrical and Electronics Engineers Inc., 2016) Abdelhamid, Neda ; Jabbar, Ahmad Abdul ; Thabtah, Fadi
    Association rule mining involves discovering concealed correlations among variables often from sales transactions to help managers in key business decision involving items shelving, sales and planning. In the last decade, association rule mining methods have been employed in deriving rules from classification dataset in different business domains. This has resulted in an emergence of new classification approach called Associative Classification (AC), which often produces higher predictive classifiers than classic approaches such as decision trees, greedy and rule induction. Nevertheless, AC suffers from noticeable challenges some of which have been inherited from association rules and others have been resulted from building the classifier phase. These challenges are not limited to the massive numbers of candidate ruleitems found, the very large classifiers derived, the inability to handle multi-label datasets, and the design of rule pruning, ranking and prediction procedures. This article highlights and critically analyzes common challenges faced by AC algorithms that are still sustained. Hence, it opens the door for interested researchers to further investigate these challenges hoping to enhance the overall performance of this approach and increase it applicability in research domains. © 2016 IEEE.
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    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.
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    Audit Committee Effectiveness and Accounting Conservatism a Test of Lagged Effect
    (IGI Global, 2018) Khan, Saif Ur-Rehman ; Khan, Faisal ; Elshareif, Elgilani
    This article examines the effect of audit committee effectiveness on two measures of accounting conservatism. In addition, this article also investigates the interaction effect of four endogenous variables (i.e. firm's operating risks, leverage, managerial influence, firm's size) and three exogenous variables on relationship between audit committee effectiveness and two measures of accounting conservatism. A total of 543 sample firms are selected from the Bursa Malaysia for the period from 2004 to 2013. In addition, some information relating to audit committee and auditor quality are collected from firms' annual reports. For data analysis, panel data methodology is employed, and multiple regression analysis technique is used to test the developed hypotheses of this study. Results show that interaction effect of firm's operating risks, managerial influence, external auditor quality and capital market uncertainty found to be significant with two-year-lagged effect on both measures of conservatism. Whereas, the interaction effect of firm's leverage, firm's size and product market completion are found to be insignificant. The findings of this study contribute to the signaling theory, agency theory, reputation theory and accounting conservatism literature with lagged effect in emerging economies settings.
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    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.
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    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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    The benefits of applying GIS in spiritual tourism management and promotion
    (IGI Global, 2018) Haq, Farooq ; Medhekar, Anita
    The main contribution of this chapter is to critically discuss the benefits of applying geographic information systems (GIS) as a tool for management and promotion of spiritual tourism circuits (STC) and ST destinations. This research-based chapter also examines the extent to which GIS can be used in spiritual tourism management and promotion, proposes a model for the use and benefits of GIS in spiritual tourism management and promotion in India and Pakistan, and proposes GIS connected STC. This chapter identifies the socio-economic and business benefits of applying GIS to spiritual tourism circuits (STC). In this research, the spiritual tourism product is exclusively based on spiritual place, sites, or destinations, which is also called spatial or geographical data. © 2018 by IGI Global. All rights reserved
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    Beyond Borders: The Euro Crisis and Public Support for Monetary Integration in East Africa
    (Blackwell Publishing Ltd, 2020) Bizuneh, Menna ; Buigut, Steven ; Valev, Neven
    We show that economic experiences in one part of the world affect proposed policies elsewhere. Specifically, we find that the recent crisis in the European Monetary Union (EMU) has impacted negatively the public support for the new proposed monetary union in the East African Community (EAC), with a more pronounced effect for less educated Kenyans. That external effect is robust to controlling for an array of other factors such as the expected economic benefits from the union, the desire to gain international influence as part of a larger community and the memory of an earlier failed EAC monetary union. © 2020 Economic Society of South Africa
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    Big Data Analyses and New Technology Applications in Sport Management, an Overview
    (Association for Computing Machinery, 2020-05-15) Mataruna-Dos-Santos, Leonardo Jose ; Faccia, Alessio ; Helú, Hussein Muñoz ; Khan, Mohammed Sayeed
    Technology has profoundly changed our lives, especially in the past two decades. The introduction of the internet and PCs, first of all, cell phones and later smartphones, has changed our daily habits, leading us to be always connected for many hours of the day. Changes that have affected all fields, not least sporting activity, always focused on performance maximization. Technology in sport has made great strides, allowing both amateurs and even more professionals to use innovative technical solutions that can improve performance: first of all during training and then, consequently, in official competitions. Innovations both in the field of materials, but above all in terms of tools for verifying correct training through the collection of a large number of data, turned into carefully analysed useful information. There are sports that have benefited most from these new technologies, based on their particular characteristics. This research focused on a systematic analysis of the most important technologies that are currently allowing great progress in sports performance and in the impartiality of competitions through the analysis of the collected data. In particular, the research highlighted three particular areas of interest: A) video assistant data collectors; b) Wearable technologies; c) Scouting tech-based techniques. © 2020 ACM.
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    Can an energy futures index predict us stock market index movements?
    (Econjournals, 2018) Gurrib, Ikhlaas
    This paper investigates if an energy futures conditions index (EFCI) can predict movements of US major stock market indices. While various financial conditions indices provide information about the financial stress of a country, the existence of an energy conditions index, using futures markets, is scarce. Using weekly data over 1992-2017, this paper proposes an energy futures index using principal component analysis and test its predictability. The EFCI captures 95% of the variability inherent in the crude oil, heating oil and natural gas futures total reportable positions. Stability in forecast errors over different lags suggests 1 week lag is sufficient in forecasting weekly Nasdaq Composite Index, Nasdaq 100 and Russell 3000 values. 95% prediction levels support that the estimated model captures all actual market indices values, except for the 2000 technology bubble. The inability of the energy futures index in predicting stock market indices during the 2000 bubble can be explained by the poor sensitivity of energy futures to this specific event. Distributions were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. © 2018, Econjournals. All rights reserved.