Browsing by Author "Sebbak, Faouzi"

Now showing 1 - 8 of 8

An Alternative Combination Rule for Evidential Reasoning
(2014) Sebbak, Faouzi; Benhammadi, Farid; Mataoui, Mhamed; Bouznad, Sofiane; Amirat, Yacine
With an aim of having a normal behavior in combination of bodies of evidence, this paper proposes a new combination rule which includes the cardinality of focal set elements in conjunctive operation and the conflict redistribution to all meaningful sets steps. This distribution is based on factors, which are computed by weighting the masses assigned to each set by the sources of information and those obtained by the conjunctive operation with its cardinality. This strategy forces the conflict redistribution in favor of the more committed hypothesis. Our method is evaluated and compared with some numerical examples reported in the literature. As result, this rule redistributes the conflict in favor of the more committed hypothesis and gives intuitive interpretation for combining multiple information sources with coherent results
CSP Formulation for scheduling independent jobs in Cloud Computing
(2015) Mataoui, Mhamed; Sebbak, Faouzi; Beghdad Bey, Kada; Benhammadi, Farid
This paper investigates the use of Constraint Satisfaction Problem formulation to schedule independent jobs in heterogeneous cloud environment. Our formulation provides a basis for computing an optimal Makespan using job and machine reordering heuristics based on Min-min algorithm result. The combination of these heuristics with the weighted constraints allows improving the efficiency of the tree search algorithm to schedule jobs with considerable space search reduction. The proposed CSP model is validated through simulation experiments against clusters of 10 virtual machines. The results demonstrate that our model is able to efficiently allocate resources for jobs with significant performance gains between 18%-40% compared to the Min-Min heuristic results to optimize the Makespan
Evidence-Fuzzy System for Human Concurrent Activities Recognition
(IEEE, 2015) Sebbak, Faouzi; Benhammadi, Farid; Mataoui, Mhamed
Activity recognition is an important task which can be applied to many real- life problems in pervasive computing. In this work, we propose concurrent activities recognition system based on two layers of inference. The first layer develops a framework for dealing with fusion system through merging different sources of information using evidence theory. The second layer proposes a decision framework under the fuzzy logic formalism. Our experimental results suggest that the fuzzy logic method for the plausibility combinations at the decision level is the best for activities in progress simultaneously but not necessarily involving the user's interaction at the same time steps. It yields high accuracy of 79.7%, regarding experimental results
Evidential-Link-based Approach for Re-ranking XML Retrieval Results
(2014) Mataoui, Mhamed; Mezghiche, Mohamed; Sebbak, Faouzi; Benhammadi, Farid
In this paper, we propose a new evidential link-based approach for re-ranking XML retrieval results. The approach, based on Dempster-Shafer theory of evidence, combines, for each retrieved XML element, content relevance evidence, and computed link evidence (score and rank). The use of the Dempster–Shafer theory is motivated by the need to improve retrieval accuracy by incorporating the uncertain nature of both bodies of evidence (content and link relevance). The link score is computed according to a new link analysis algorithm based on weighted links, where relevance is propagated through the two types of links, i.e., hierarchical and navigational. The propagation, i.e. the amount of relevance score received by each retrieved XML element, depends on link weight which is defined according to two parameters: link type and link length. To evaluate our proposal we carried out a set of experiments based on INEX data collection
A Fuzzy Link-Based Approach for XML Information Retrieval
(IEEE, 2015) Mataoui, Mhamed; Sebbak, Faouzi; Benhammadi, Farid
The increasing amount of available XML documents collections has led to the emergence of new challenges in information retrieval field. Therefore, multiple sources of evidence were used to retrieve XML elements at different levels of granularity. XML information retrieval combines textual and structural information to perform different information retrieval tasks. In this paper, we propose a new approach exploiting link evidence to re-rank XML retrieval results. Our approach, based on fuzzy logic concepts, combines both content and link evidence for all retrieved XML elements. The combination process generates a new ranked list from the initial returned list. Experiment based on INEX 2007 Wikipedia collection showed improvement of the interpolated precision values
A New Combination Rule Based on a Total Conflict Redistribution
(2014) Sebbak, Faouzi; Mataoui, Mhamed; Benhammadi, Farid
New tasks scheduling strategy for resources allocation in Cloud Computing Environment
(2015) Beghdad Bey, Kadda; Benhammadi, Farid; Sebbak, Faouzi; Mataoui, Mhamed
Scientific applications are very complex and need massive computing power and storage space. Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy for resource allocation for maximizing profit in cloud computing environment. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the resources exploitation. To show the interest of the proposed solution, experiments results are conducted on a simulation data set
Query Expansion in XML Information Retrieval : a new Approach for terms selection
(2015) Mataoui, Mhamed; Sebbak, Faouzi; Benhammadi, Farid; Bey Beghdad, Kadda
Query Expansion is an important component for information retrieval systems. It makes possible the reformulation of the initial user query by adding new terms. In this paper, we propose a new approach for term selection in the relevance feedback process. This approach, based on Rocchio formula, is an adaptation to the XML information retrieval context. It can resolve two major problems specific to the XML information retrieval: the overlapping problem in the list of retrieved elements; and the problem of inclusion of irrelevant elements in the selection of new query terms