SUAD Institutional Repository
by SUAD Library

Your reference for the Sorbonne University Abu Dhabi research output and research impact

 
Research outputs
525
Disciplines
9
Researchers
75
Recent Additions
  • Publication
    I’m no casual: exploring the consumer behaviour of Fantasy Premier League hardcore international managers.
    (2023)
    Ahmed, Amar
    The internet has caused a worldwide exponential increase in the number of fantasy sports consumers in general, and Fantasy Premier League (FPL) in particular. This paper investigates the overall experiences of hardcore ‘managers’. Using interviews and applying Grounded Theory, this study offers a description regarding managers’ 1) perceptions of what makes a hardcore manager as against a casual one; 2) motivations to participate in FPL at hardcore levels; 3) media usage; 4) the impact FPL has on their consumption of the sport and its products; and 5) the marketability of specific players. Subsequently, we develop an understanding of international FPL hardcore managers’ consumption behaviour. This understanding helps teams, marketers, and sponsors both directly and indirectly to establish a more effective reach to this growing psychographic group. Moreover, it contributes to the body of knowledge of cultural consumer research.
      8
  • Publication
    Hybrid model of alternating least squares and root polynomial technique for color correction
    (2023)
    Babbar, Geetanjali
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    Bajaj, Rohit
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    Mittal, Nitin
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    Mahajan, Shubham
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    Abualigah, Laith
    Color correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Many approaches have already been proposed by various researchers; however, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein alternate least square (ALS) and root polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to enhance the image quality and make them look realistic. To achieve this objective, the proposed model used the Amsterdam library of object images which contains a picture of single objects captured under various illumination angles and colors. The main contribution of this paper is a hybrid ALS + RP color correction technique, implemented on the dataset image that fixes its color as per the reference image and enhances its quality. The target image is then converted into three color models, i.e., LAB, LUV, and RGB into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB, to prove its supremacy over other models.
      6
  • Publication
    An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
    (2023) ;
    Panja, Madhurima
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    Nadim. Sk Shahid
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    Ghosh, Indrajit
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    Kumar, Uttam
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    Liu, Nan
    Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
      12
  • Publication
    A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods
    (2023) ;
    Braik, Malik Sh.
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    Awadallah, Mohammed A.
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    Al-Betar, Mohammed Azmi
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    Hammouri, Abdelaziz I.
    An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lévy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems’ analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems.
      14
  • Publication
    Comprehensive assessment of the capacity of sand and sandstone from aquifer vadose zone for the removal of heavy metals and dissolved organic
    (2023)
    Ali, Jisha
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    Ghaleb, Hala
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    Arangadi, Abdul
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    Le, Tu Phuong Pham
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    Moraetis, Daniel
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    Alhseinat, Emad
    Due to the drastic effect of produced water on the environment and its large quantity produced by the oil and gas industry, produced water treatment is a significantly growing challenge that requires serious attention. Produced water can be used as unconventional source of water in arid regions for underground water aquifer recharging through soil aquifer treatment (SAT), however, this requires sophisticated studies to avoid the contamination of the underground water. The present study investigates the efficacy of sand and sandstone from aquifer vadose zone for removing heavy metals and dissolved organic that are common contaminants in oil produced water. The removal of performance of soil samples has been evaluated on the laboratory scale at neutral pH at room temperature using synthetic oil produced water which contains heavy metals (Ni and Zn) and dissolved organics (phenol). The various experimental parameters were monitored and results indicated the sandstone displayed the highest removal of 98%–99% for both heavy metals and 26% for phenol than sand. The experimental data were fitted using four isotherm models, the Langmuir adsorption isotherm, the Freundlich isotherm, the Temkin isotherm model and the D–R isotherm. The Langmuir adsorption isotherm fitted well in a monolayer adsorption conceptual model on sand and sandstone. Kinetic modelling and analysis indicated that both soil samples followed the pseudo-second- order kinetics for metal ions and phenol. The 2D-COS FTIR was applied to analyse the interaction mechanism between the contaminants and sand and sandstone particles. The asymmetric Si–O band in sand minerals plays the prime response in Ni and Zn removal mechanisms whereas the asymmetric CO2− 3 band decides for the removal mechanisms in sandstone. In the case of phenol adsorption, the interaction between phenol and Si–O bond is the predominant mechanism. Overall, these results summarize that sand and sandstone are effective for heavy metals removal than dissolved organic compounds.
      6