Information Retrieval (IR) is a field of computer science that deals with storing, searching, and retrievingdocuments that satisfy the user need. The modern standard Arabic language is rich in multiple meanings (senses) for manywords and this is substantially due to lack of diacritical marks. The task for finding appropriate meanings is a key demand inmost of the Arabic IR applications. Actually, the successful system should not be interested only in the retrieval quality andoblivious to the system efficiency. Thus, this paper contributes to improve the system effectiveness by finding appropriatestemming methodology, word sense disambiguation, and query expansion for addressing the retrieval quality of AIR. Also, itcontributes to improve the system efficiency through using a powerful metaheuristic search called Harmony Search (HS)algorithm inspired from the musical improvisation processes. The performance of the proposed system outperforms the one inthe traditional system in a rate of 19.5% while reduces the latency in an approximate rate of 0.077 second for each query.
Distributed information retrieval DIR is a model enables a user to access many searchable databases reside in different locations. DIR is more complex than the centralized information retrieval IR . It requires addressing two significant additional problems that are the resource selection and the results merging. Many techniques for addressing the two problems have been published in the literature. However they still have a negative impact on retrieving quality and response time. This paper aims to improve the DIR efficiency through using a meta heuristic algorithm and improving the result quality through a query expansion. The algorithm has been strengthened using the nearest neighbor graph in order to improve the search performance.
Computer security depends mainly on passwords to protect human users from attackers. Therefore, manual and alphanumerical passwords are the most frequent type of computer authentication. However, creating these passwords has significant drawbacks. For example, users often tend to choose passwords based on personal information so that they can be memorable and therefore weak and guessable. In contrast, it is often difficult to remember if the password is difficult to guess. We propose an intelligent security model for password generation and estimation to address these problems using the ensemble learning approach and hand gesture features. This paper proposes two intelligent stages: the first is the password generation stage based on the ensemble learning approach and the proposed S-Box. The second is the password strength estimation stage, also based on the ensemble learning approach. Four well-known classifiers are used: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest Tree (RFT), and AdaBoost applied on two datasets: MNIST images dataset and password strength dataset. The experimental results showed that the hand gesture and password strength classification processes accurately performed at 99% in AUC, Accuracy, F1-measures, Precision, and Recall. As a result, the extracted features of hand gestures will directly impact the complexity of generated passwords, which are very strong, hard to guess, and memorable.
With the tremendous growth of information in the web, the classic query processing approaches are unable to respond to queries in real time. The aim of this paper is to develop an innovative tool using swarm intelligence to address information retrieval in the context of response time and solution quality through cope with the complexity induced by that huge volume of information. In this paper, we will show that our proposed approach that use of Artificial Bee Colony (ABC) algorithm called MABC can be another alternative to palliate the complexity issue in terms of response time while it produces a solution quality is relatively convergent or even better. Experimental tests have been conducted on two well-known CACM and NPL collections. Both are different in size, CACM is small while NPL is relatively large. Numerical results exhibit the superiority and the benefit gained from using the MABC approach instead of the classic approaches.
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