Nevin Mahmoud Darwish

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In this paper we aim to estimate the differential student knowledge model in a probabilistic domain within an intelligent tutoring system. The suggested algorithm aims to estimate the actual student model through the student answers to questions requiring diagnosing skills. Updating and verification of the model are conducted based on the matching between(More)
This paper addresses the problem of coordinating a group of agents involved in a team. To achieve flexible teamwork, agents should synchronize their work and monitor their performance to avoid redundant work. Generalized Partial Global Planning (GPGP) is one of the most common techniques used in coordinating cooperative agents, however, no technique is(More)
In this paper, we present a new approach for measuring the expected runtimes (hardness) of SMT problems. The required features, the statistical hardness model used and the machine learning technique which we used are presented. The method is applied to estimate the hardness of problems in the Quantier Free Bit Vector (QFBV) theory and we used four of the(More)
abstract This paper presents a new method for solving systems of Boolean equations. The method is based on converting the equations so that we operate in the integer domain. In the integer domain better and more efficient methodologies for solving equations are available. The conversion leads us to a system of polynomial equations obeying certain(More)
In this paper a question generation approach for adaptive assessment is purposed to estimate the student knowledge model in a probabilistic domain within an intelligent tutoring system. Assessing questions are generated adap-tively according to the student knowledge based on two factors (i) the student misconceptions that are entailed in the student(More)
In this paper an Incremental GA techniques is proposed tosolve the problem of small disjuncts in classification trees. It is once applied on the disjuncts sorted in ascending orderand once in descending order with respect to their coverage. Both versions of the technique have been tested using benchmark datasets and the results are compared with thoseof(More)
Feature Filtering is an approach that is widely used for dimensionality reduction in text categorization. In this approach feature scoring methods are used to evaluate features leading to selection. Thresholding is then applied to select the highest scoring features either locally or globally. In this paper, we investigate several local and global feature(More)