Hamdy M. Mousa

Argumentation based negotiation in multiagent system

Negotiation is a process of reaching an agreement on the terms of a transaction such as price, quantity, for two or more parties in multi-agent systems such as E-Commerce. It tries to maximize the benefits for all parties. Argumentation-based negotiation has been proposed as an alternative to proposalbased approaches such as game theory and heuristic. The main advantage is that it allows agents to exchange additional information rather than just simple proposals. This property of argumentation protocols can lead to beneficial agreements when used for complex multi agent negotiation. This paper presents an empirical comparison of argumentation-based negotiation to proposal-based negotiation in a strategic two-player scenario. It is implemented through a well established FIPA-complaint Agent Toolkit, JADE. Our experiments show that the argumentation-based approach outperforms the proposal-based approach with respect to the quality of the agreements found and the quantity of unsuccessful negotiations.

Enhanced semantic arabic Question Answering system based on Khoja stemmer and AWN

Due to the great amount of information available on the web, Question/Answering systems have become a focus for researchers and users as well. This paper introduces a proposed design for an Arabic Question Answering system based on Query Expansion ontology and an Arabic Stemmer. A set of factoid CLEF and TREC questions used to evaluate the system. Improved results obtained using AWN as a semantic Query Expansion and Khoja stemmer as a stemming system. Two experiments conducted using AWN the first using one level of expansion and the second using two level of expansion. Three measures are performed: Accuracy, Mean Reciprocal Rank, and Answered Questions, and the obtained results are 35.5%, 20.2%, and 65.33% respectively when using one level of expansion. But when using two level of expansion we get 38.77%, 16.2%, and 65.55% respectively.

Clustering of chemical data sets for drug discovery

Chemoinformatics clustering algorithms are important issues for drug discovery process. So, there are many clustering algorithms that are available for analyzing large chemical data sets of medium and high dimensionality. The quality of these algorithms depends on the nature of data sets and the accuracy needed by the application. The applications of clustering algorithms in the drug discovery process are compound selection, virtual library generation, High-Throughput Screening (HTS), Quantitative Structure-Activity Relationship (QSAR) analysis and Absorption, Distribution, Metabolism, Elimination and Toxicity (ADMET) prediction. Based on Structure-Activity Relationship (SAR) model, compounds with similar structure have similar biological activities. So, clustering algorithms must group more similar compounds in one cluster. K-Means, bisecting K-Means and Ward clustering algorithms are the most popular clustering algorithms that have a wide range of applications in chemoinformatics. In this paper, a comparative study between these algorithms is presented. These algorithms are applied over homogeneous and heterogeneous chemical data sets. The results are compared to determine which algorithms are more suitable depending on the nature of data sets, computation time and accuracy of produced clusters. Accuracy is evaluated using standard deviation metric. Experimental results show that K-Means algorithm is preferable for small number of clusters for homogeneous and heterogeneous data sets in terms of time and standard deviation. Bisecting K-Means and Ward algorithms are preferable for large number of clusters for homogeneous and heterogeneous data sets in term of standard deviation, but bisecting K-Means algorithm is preferable in term of time.

Design and implementation of course timetabling system based on genetic algorithm

University course timetabling is one of the most important and time-consuming problem which takes place frequently in all the educational institutes. This paper proposes design and implementation system to generate timetable based on genetic algorithm using different combinations selection algorithm and mutation types. Two cases small problem and big problem are studied. The results show that two cases tournament selection is giving solutions better than roulette wheel Selection. The worst pair is roulette wheel selection and mutation. Mutation error method helps to reach to the best solution faster. In case of conflicts and no solution, our system generates a report, containing conflict constraints that must be remove or modified.

Syntactic open domain Arabic question/answering system for factoid questions

Information Retrieval systems have become a crucial part of any search engine, taking into consideration the increased number of pages and documents added to the World Wide Web every day. Question/Answering systems are specific to retrieve the most accurate answer among several documents; this paper will present an improved methodology and implementation of an open domain Arabic Question Answering System for factoid questions. The proposed system is based on Query Expansion ontology and an Arabic Stemmer. Basically the system mainly depends on AWN as a semantic Query Expansion and Khoja stemmer as a stemming system, to evaluate the system we used 56 translated CLEF and TREC questions, four types of test scenarios are performed. Four evaluation attributes were considered: Accuracy, Mean Reciprocal Rank, Answered Questions and recall; and encouraging results were reached with the proposed system.

Image compression ratio enhancement based on conformal mapping

The main goal of compression is to reduce the amount of data required to represent a digital image. Conformal mapping has long been an important topic in complex analysis. In this paper, the proposed image compression technique based on conformal mapping transformation is introduced. The most recent standard compression technique, JPEG2000 compression algorithm, is used. The proposed technique is tested with various images types. Two categories of image compression techniques (lossless and lossy) and with/without conformal mapping are studied. The experimental results show that the compression ratio improves by 14% in average, and in case lossy image compression using JPEG2000 image quality gains over 2 dB in average.

Parallel Ward Clustering for Chemical Compounds Using MapReduce

The availability of chemical libraries with millions of compounds makes the process of identifying lead compounds very hard. The identification of these compounds is the backbone step of drug discovery process. Hierarchical clustering algorithms are used for that purpose. One of the most popular hierarchical clustering algorithms that are used in many applications in the drug discovery process is ward clustering algorithm. A main problem with the previous implementations of ward algorithm is its limitation to handle large data sets within a reasonable time and memory resources. In this paper, OpenCL is used to implement ward algorithm. The first two steps of ward (1) proximity matrix computation; (2) finding minimum distance are modified to run in parallel. Four subsets of National Cancer Institute (NCI) dataset are used. The smallest subset contains 500 compounds and largest subset contains 10,000 compounds. The results show that parallel proximity matrix computation saves 92% of time for smallest subset and 99% of time for largest subset. The parallel minimum distance saves 76% of time for smallest subset and 99% of time for largest subset.

Secured steganography algorithm based random function

In this paper, we propose modified least significant bit steganography technique in order to make the technique more secure and hence less predictable. In this technique, the function and threshold value are randomly generated that used to encrypt the secret message using bit exchange method. The encrypted message is embedded into host image. Experimental results demonstrate that proposed technique has multilayer protection stages against different attacks and it can defeat many existing steganalytic attacks and higher level of security based on the function parameters and threshold values. Stego images quality is acceptable because of the difference between histograms of the host image and its stego-image is negligible and all obtained PSNR values exceed 50 dB.

Improving Jarvis-Patrick algorithm for drug discovery

Clustering algorithms play an important role in chemoinformatics and especially in the drug discovery process. Clustering methods may be hierarchical or non-hierarchical. Non-hierarchical algorithms have fast processing for clustering chemical data sets than hierarchical algorithms. One of the most popular non-hierarchical clustering algorithms that are used in many applications in the drug discovery process is Jarvis-Patrick algorithm. The applications of Jarvis-Patrick in the drug discovery process are compound selection, compound acquisition, low-throughput screening and Quantitative Structure-Activity Relationship (QSAR) analysis. Jarvis-Patrick groups compounds in a cluster based on a three neighborhood conditions. These three conditions groups compounds, which are not similar enough, in the same cluster. Adding dissimilar compounds in the same cluster will lead to poor compound selection, compound acquisition and QSAR analysis. In this paper, standard Jarvis-Patrick is modified by adding a fourth condition which computed only if the three standard conditions are true. This condition computes the increasing in the value of Square Error (SE) of the cluster by adding a compound and compares it with expected increasing in SE to determine whether to add a compound to the cluster or not. The result shows that our modification produces clusters with less SE values in the produces clusters.