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Name : Abdelraouf Ishtaiwi

Academic Rank: Assistant Prfoessor

Administrative Position : Faculty Academic Member

Office 7304       Ext No 7304

Email : aishtaiwi@uop.edu.jo

Specialization: Computer Science

Graduate Of: Griffith University

Qualification

    Qualification

    University

    Country

    Year

    Master's
    Griffith University
    Australia
    2001
    Ph.D
    Griffith University
    Australia
    2007



  • Journal Paper





      A. HajYasein an and A. Ishtaiwi, " Survey on Privacy Preserving in Data Mining Tasks " , "albalqa journal for research",Vol.,No., , Amman, jordan, 01/01/2009




      A. Ishtaiwi, A. HajYasien, " The Occurrence of Frozen Weights in Dynamic Local Search for SAT " , "International Journal of Computer Science and Information Security (IJCSIS)",Vol.,No., Thomson Reuters, , 09/07/2016 Abstract:
      Abstract—A key characteristic in Dynamic Local Search Techniques (DLS) is the use of weights while searching for a solution to a Satisfiability problems (SAT). Thus, weights handling plays a major role in the over all performance of a given DLS technique. Unfortunately, there are no general appr Download




      Wael Hadi, Ghassan Issa, Abdelraouf Ishtaiwi, " ACPRISM: Associative classification based on PRISM algorithm " , "Information Sciences",Vol.,No., Elsevier, , 11/01/2017 Abstract:
      Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from as




      Abdelraouf Ishtaiwi1*, Ghassan Issa1 and Wael Hadi1, " Reducing the Cost of Exploring Neighborhood Areas in Dynamic Local Search for SAT " , "British Journal of Applied Science & Technology",Vol.,No., SCIENCEDOMAIN international, , 01/01/2018 Abstract:
      Stochastic Local Search (SLS) algorithms are of great importance to many fields of Computer Sciences and Artificial Intelligence. This is due to their efficient performance when applied for solving randomly generated satisfiability problems (SAT). Our focus in the current work is on one of the SLS d


  • Conference paper





      Ishtaiwi, A., Thornt, " Neighbourhood Clause Weight Redistribution in Local Search for SAT. " , "Principles and Practice of Constraint Programming - CP 2005, 11th International Conference",Vol.,No., Springer, Sitges, Spain, 03/16/2005



      Pham, D. N., Thornto, " SAT-based versus CSP-based Constraint Weighting for Satisfiability. " , "Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005",Vol.,No., AAAI Press / The MIT Press 2005, Pittsburgh, Pennsylvania, USA, 03/16/2005



      Ishtaiwi, A., Thornt, " Adaptive Clause Weight Redistribution. " , "",Vol.,No., , , 04/15/2006



      Ishtaiwi, A., Thornt, " Weight Redistribution for Unweighted MAX-SAT " , "AI 2007: Advances in Artificial Intelligence, 20th Australian Joint Conference on Artificial Intelligence.",Vol.,No., Springer, Gold Coast, Australia, 09/04/2007



      Abdelraouf Ishtaiwi, " Weights Stagnation in Dynamic Local Search for SAT " , "Sixth International Conference on Computer Science, Engineering and Information Technology (CCSEIT 2016)",Vol.,No., (Academy & Industry Research Collaboration Center (AIRCC, , 05/21/2016 Abstract:
      Abstract—Since 1991, tries were made to enhance the stochastic local search techniques (SLS) in many different ways. Some researchers turned their focus on studying the structure of the satisfiability problems to better understand their complexity in order to come up with better algorithms that Download




      Abdelraouf M. Ishtaiwi, Marco J. Baron+, and Ghassan F. Issa, " Multi Level Weight Distribution Dynamic Local Search for SAT " , "Int'l Journal of Computing, Communications & Instrumentation Engg. (IJCCIE)",Vol.,No., IJCCIE, , 03/08/2017 Abstract:
      In recent years, dynamic clause weighting stochastic local search algorithms have emerged as the-state-of-the-art for solving satisfiability problems. In our study we experimentally investigated the weights behaviors and movements during searching for satisfiability. Firstly, We show that, second le




      ,Marco Javier Surez Baron,Abdelraouf Ishtaiwi, " Acquisition and Analyses of Lessons Learned from Social Network R&D Using Machine Learning " , "HUSO 2018 : The Fourth International Conference on Human and Social Analytics",Vol.,No., IARIA, , 06/22/2018 Abstract:
      This article presents the development of a computational framework used for the extraction and recovery of lessons learned that have been extracted from academic and research related social networks; the lexical analysis applied is focused to Spanish language. The algorithm executes the lexical analysis using Natural Language Programming (NLP) techniques. The final result of the process shows that the use of this type of lexical and semantic analysis is a key component in tasks of social analysis, text mining and semantic enrichment.
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