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  • Publication
    Comparative Study of Polyethylene Films Embedded with Oxide Nanoparticles of Granulated and Free-Standing Nature
    ( 2022) ;
    Le Guyon, Valerie
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    ; ; ;
    El-Rifai, Joumana
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    Dmitrenko, Mariia
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    Penkova, Anastasia
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    Zou, Lusi
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    Angastiniotis, Nicos
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    Koutsokeras, Loukas
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    Duponchel, Benoît
    Nanocomposite polymer films are a very diverse research field due to their many applications. The search for low-cost, versatile methods, producing regulated properties of the final products, has thus become extremely relevant. We have previously reported a bulk-scale process, dispersing granulated metal oxide nanoparticles, of both unary and multi-component nature, in a low-density polyethylene (LDPE) polymer matrix, establishing a reference in the produced films’ optical properties, due to the high degree of homogeneity and preservation of the primary particle size allowed by this method. In this work, unmodified, free-standing particles, namely zinc oxide (ZnO) , titanium dioxide (TiO2), aluminum oxide (Al2O3), and silicon dioxide (SiO2) are blended directly with LDPE, and the optical properties of the fabricated films are compared to those of films made using the granulation process. The direct blending process evidently allows for control of the secondary particle size and ensures a homogeneous dispersion of the particles, albeit to a lesser extent than the granulation process. Despite the secondary particle size being comparatively larger than its granulated counterpart, the process still provides a regulated degree of deagglomeration of the free-standing oxide particles, so it can be used as a low-cost alternative. The regulation of the secondary particle size tunes the transmission and reflection spectra, in both unary and mixed oxide compositions. Finally, the direct blending process exhibits a clear ability to tune the energy band gap in mixed oxides.
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  • Publication
    Towards Safeguarding Users' legitimate rights in Learning Management Systems (LMS): A case study of Blackboard at Sorbonne University Abu Dhabi
    This paper sought to establish the extent to which users’ legitimate rights are safeguarded in Learning management systems (LMS), specifically, on the Blackboard system, used for teaching at Sorbonne University, Abu Dhabi (SUAD) Firstly, users’ legitimate rights that must be protected were identified. Subsequently, the security and privacy guarantees afforded by Blackboard were assessed.  Lastly, policy gaps and technological deficiencies undermining protection of users’ legitimate rights were identified. The study adopted a qualitative research approach and a case study research design. Data was collected through content analysis, document review and interviews The research revealed that to a large extent Blackboard, LMS safeguarded most of the users’ legitimate rights. However, the system is silent on some legitimate rights such as storage limitation and data sharing arrangements.  Further, it emerged that Blackboard’s privacy practices are to a large extent informed by educational institutions using its products. The study concludes that safeguarding user’s legitimate rights is a collective responsibility between the learning management services providers and the educational institutions. As such, there is need for educational institutions using Blackboard and other learning management systems to craft robust data protection regimes. Keywords:
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  • Publication
    Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications
    ( 2022) ;
    Hussie, Abdelazim
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    Abualig, Laith
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    Hashim, Fatma
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    Amin, Mohamed
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    Saber, Abeer
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    Almotair, Khaled
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    Gandomi, Am
    The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
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  • Publication
    Encryption technique based on chaotic neural network space shift and color‑theory‑induced distortion
    ( 2022) ;
    Al-Muhammed, Muhammed J.
    Protecting information privacy is likely to promote trust in the digital world and increase its use. This trust may go a long way toward motivating a wider use of networks and the internet, making the vision of the semantic web and Internet of Things a reality. Many encryption techniques that purport to protect information against known attacks are available. However, since the security challenges are ever-growing, devising effective techniques that counter the emerging challenges seems a rational response to these challenges. This paper proffers an encryption technique with a unique computational model that inspires ideas from color theory and chaotic systems. This mix offers a novel computation model with effective operations that (1) highly confuse plaintext and (2) generate key-based enormously complicated codes to hide the resulting ciphertext. Experiments with the prototype implementation showed that the proposed technique is effective (passed rigorous NIST/ENT security tests) and fast.
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  • Publication
    An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
    ( 2022) ;
    Awadallah, Mohammed A.
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    Al-Betar, Mohammed Azmi
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    Braik, Malik Shehadeh
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    Hammouri, Abdelaziz I.
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    Abu Doush, Iyad
    In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
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