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  • Publication
    Karst development in different tectonic settings (Middle East, Greece, South China), concept analysis and first findings towards hydrology modeling reconsideration
    ( 2022) ;
    Moraetis, Daniel
    ;
    Fassoulas, Charalambos
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    Mattern, Frank
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    Scharf, Andreas
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    Yu, Xuan
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    Pennos, Christos
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    Adamopoulos, Kostas
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    Zacharias, Stylianos
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    Hamdan, Hamdan
    ;
    Nikolaidis, Nikolaos
      12
  • Publication
    Searching for a Martian soil simulant in UAE & Al Hajar Mountains-First simulants CUOS and MUOS
    ( 2021) ;
    Fikri, Mira
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    Alhazba, Ali
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    Pracejus, Bernhard
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    Fernini, Illias
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    Manousakis, Antonios
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    Suleiman, Bashir
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    Shameer, Mohamed
    ;
    Moraetis, Daniel
    This study presents the first results for creating a Martian soil simulant from rocks in UAE and Al Hajar mountains including Sultanate of Oman. The Martian soil composition is now well known from rover missions and it is a regolith of oceanic crust composition (basaltic). The presence of the most extensive oceanic crust in parts Al Hajar mountains (Semail Ophiolites) is a triggering fact to search the possibility to create Martian soil simulants. Inhere we have collected 13 samples from volcanic basalts, gabbro harzburgite laterites and we assessed the mineral composition. We demonstrate the mixing process of the various rocks to create soil simulants. We have created two simulants, CUOS and MUOS following the compositions of Curiosity Mission measurements and MMS-1 soil simulant supplied by the Martian Garden company. The CUOS soil simulant showed moderate to good fitting in the mineralogy with the Curiosity Mission analysis in Rocknest Portage, while we were not able to attain similar mineralogical composition between MUOS and MMS-1 due to lower plagioclase content in our samples.
      3
  • Publication
    Machine Learning Approach for the Design of an Assessment Outcomes Recommendation System
    ( 2021) ;
    Fatime Al-Zahra
    ;
    Shaimaa Mounir
    ;
    Lamees Dalbah
    It is believed that the evaluation of the outcomes of the course, based on grades, is necessary to improve the teaching and learning process. Our research processes and workflows supported by AI utilize machine learning technology in order to interpret big data, analyze broad data sets and recognize associations with more reliably. The course learning outcomes will be assessed on the basis of QF-Emirates guidelines and use it to suggest teaching and learning measures. It will be used to determine courses learning results based on the empirical knowledge presented. We research and test the design of the right neural networks that achieves our goal. A modern algorithm was improvised for this reason. For our proposed recommendation system, a database program was created to store data and include details in the analysis of course learning outcomes. As a machine-learning system, the proposed approach is tested and results are competitive.
      8
  • Publication
    A Review for the Genetic Algorithm and the Red Deer Algorithm Applications
    The Red Deer algorithm (RD), a contemporary population-based meta heuristic algorithm, applications are thoroughly examined in this paper. The RD algorithm blends evolutionary algorithms' survival of the fittest premise with the productivity and richness of heuristic search approaches. On the other a well-known and relatively older evolutionary based algorithm called the Genetic Algorithm applications are also shown. The contemporary algorithm; the RDA, and the older algorithm; the GA have wide applications in computer science and engineering. This paper sheds the light on all those applications and enable researchers to exploit the possibilities of adapting them in any applications they may have either in engineering, computer science, or business.
      11