2.4AIDec 13, 2021
Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy LogicAhmad B. Hassanat, Ghada A. Altarawneh, Ahmad S. Tarawneh et al.
The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiations scenarios such as the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.
2.3DBFeb 18, 2015
Rule-and Dictionary-based Solution for Variations in Written Arabic Names in Social Networks, Big Data, Accounting Systems and Large DatabasesAhmad B. A. Hassanat, Ghada Awad Altarawneh
This paper investigates the problem that some Arabic names can be written in multiple ways. When someone searches for only one form of a name, neither exact nor approximate matching is appropriate for returning the multiple variants of the name. Exact matching requires the user to enter all forms of the name for the search, and approximate matching yields names not among the variations of the one being sought. In this paper, we attempt to solve the problem with a dictionary of all Arabic names mapped to their different (alternative) writing forms. We generated alternatives based on rules we derived from reviewing the first names of 9.9 million citizens and former citizens of Jordan. This dictionary can be used for both standardizing the written form when inserting a new name into a database and for searching for the name and all its alternative written forms. Creating the dictionary automatically based on rules resulted in at least 7% erroneous acceptance errors and 7.9% erroneous rejection errors. We addressed the errors by manually editing the dictionary. The dictionary can be of help to real world-databases, with the qualification that manual editing does not guarantee 100% correctness.