inproceedings by Farid Benhammadi
articles by Farid Benhammadi
Papers by Farid Benhammadi
2018 21st International Conference on Information Fusion (FUSION), 2018
Building context-aware pervasive computing systems - such as ambient intelligent spaces or ubiqui... more Building context-aware pervasive computing systems - such as ambient intelligent spaces or ubiquitous robots - needs to take into account the quality of contextual information collected from sensors. Such information are often inaccurate, uncertain or subject to noise due to environment and user dynamics. Dempster-Shafer theory has been extensively adopted to handle uncertainty in situation and activity recognition. This theory is used to represent, manipulate and decide under uncertainty. However, combining information using Dempster's rule may produce counterintuitive decision in highly conflicting evidences due to sources failure. Recently, a variety of rules were proposed to overcome such drawback. Inspired by Murphy's rule, we propose in this paper a new rule called “Weighted Average Combination Rule” (WACR) to deal with context recognition in highly dynamic environment such as ambient intelligence spaces. The proposed WACR rule is based on evidence arithmetic average and cardinality. WACR rule was applied to some conflictual evidence examples and has been shown to reap more appropriate decisions than other alternative rules for decision-making in activity-aware systems. To demonstrate the applicability and performance of our approach, we have studied a scenario of context recognition in an ambient intelligent environment. In this scenario, we simulated a smart kitchen composed of status devices and RFID sensors that allow determining what is the artifact in use by the inhabitant and for which activity.
Distributed processing environment has eme rged as a new vision for future network based calculat... more Distributed processing environment has eme rged as a new vision for future network based calculatio n, allowing the federation of heterogeneous computing resources to incorporate the power. Cloud computing is a new computing paradigm composed of a combination of grid computing and utility computing concepts. In cloud computing, the prediction methods play a key role i n managing large scale of computation capacity. In th is paper, a modelling approach to predict the future CPU load value is presented. The proposed approach employs a computat ional intelligence technique to classify the CPU load time series into similarity component group. This technique is based on the Fuzzy Subtractive Clustering algorithm and a combination of local Adaptive Network-based Fuzzy Inference System. The results of an exhaustive set o f experiments are reported to validate the proposed p rediction model and to evaluate the accuracy of their predict ion. Experimental results demonstrate both feasibility ...
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
In the present paper, a method based on a new concept called power fuzzy soft set is proposed for... more In the present paper, a method based on a new concept called power fuzzy soft set is proposed for multi-observer decision making problems under uncertain information. The new method applies a weighted conjunctive operator to aggregate these sets into a reliable resultant power fuzzy soft set from the input data set. To decide among the alternatives, a new ranking algorithm is introduced. The effectiveness and feasibility of this method are demonstrated by comparing it to algorithms based on the maximum score in decision making.
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
In recent years, a plethora of different studies for design of traditional ensemble classifiers h... more In recent years, a plethora of different studies for design of traditional ensemble classifiers has been proposed in order to improve final recognition accuracy. However, among the ensemble classifiers, combination methods are focused on building independent classifiers of the same or different algorithms using majority voting methods. In this paper, we present a new fusion scheme for ensemble classifiers based on a new concept called Generalized Fuzzy Soft Set (GFSS), which we apply in activity classification. Essentially, we apply a weighted aggregate operator to the output of each classifier in order to fuse the GFSS into a more reliable classifier. The proposed fusion method is based on a new ranking algorithm to classify activities. We show that the proposed method produces more accurate results than the best single classifier and its effectiveness is demonstrated by comparing it with single classifier in terms of activity recognition accuracy.
Default logic is one of the best known and used formalisms for default reasoning. The interest of... more Default logic is one of the best known and used formalisms for default reasoning. The interest of this paper is to examine a new method for reasoning in default logic in presence of priorities which are represented by an arbitrary partial order on default rules. We propose here an alternative approach for computing extensions from the prioritized default theories initially proposed by Brewka [ Brewka,1994 ] . By this way, we obtain two results, one for extension calculus, and another for query answering. Given a prioritized default theory, in the first case we are able to build more efficiently all its extensions, and in the second case we are able to compute the prioritized default proof of a given formula. Keywords Knowledge Representation, Logic for artificial Intelligence, nonmonotonic Reasoning, Default logic, Priorities 1 Introduction Reiter's default logic is one of the best known and most-widely used formalisms for default reasoning. Default logic is very expressive and...
Annals of Telecommunications, 2016
International Conference on Machine and Web Intelligence, 2010
One of the main challenges of the Crypto-systems in practice is the maintenance of the confidenti... more One of the main challenges of the Crypto-systems in practice is the maintenance of the confidentiality of the cryptographic key. A technique of hybridization between the biometrics and the cryptography has been proposed for the authentication. This last rests on two methods: the Fuzzy commitment and the Fuzzy vault. However no work to our knowledge approached the problem of display
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015
2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 2015
ABSTRACT Scientific applications are very complex and need massive computing power and storage sp... more ABSTRACT 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.
Lecture Notes in Computer Science, 1998
This paper presents a novel view of default reasoning in presence of priorities which are represe... more This paper presents a novel view of default reasoning in presence of priorities which are represented by an arbitrary partial order on default rules. We propose here a new approach for computing extensions from the prioritized default theories initially proposed by Brewka [4]. By this way, we obtain two results, one for extension calculus, and another for query answering. Given
11th IEEE Symposium on Computers and Communications (ISCC'06), 2006
Flow shop problems have been extensively studied over the last decade, and numerous works have be... more Flow shop problems have been extensively studied over the last decade, and numerous works have been published in this area. This paper deals with limited intermediate storage in hybrid flow shop organisation, considering unrelated volumes depending on jobs and including support constraints. For example, in many textile production systems, there are limited buffer storage between workshops and fixed numbers of
2009 Eighth International Symposium on Parallel and Distributed Computing, 2009
Resources performance forecasting constitutes one of particularly significant research problems i... more Resources performance forecasting constitutes one of particularly significant research problems in distributed computing. To ensure an adequate use of the computing resources in a metacomputing environment, there is a need for effective and flexible forecasting method to determine the available performance on each resource. In this paper, we present a modeling approach to estimating the future value of CPU load. This modeling prediction approach uses the combination of adaptive network-based fuzzy inference systems (ANFIS) and the clustering process applied on the CPU Load time series. Experiments show the feasibility and effectiveness of this approach that achieves significant improvement and outperforms the existing CPU load prediction models reported in literature.
11th IEEE Symposium on Computers and Communications (ISCC'06), 2006
ABSTRACT This paper proposes a novel fingerprint matching technique, which matches the fingerprin... more ABSTRACT This paper proposes a novel fingerprint matching technique, which matches the fingerprint minutiae by using an effective representation to capture both the local and global minutiae features and their relative directions in fingerprint according to these minutiae orientations. Exploiting this representation, we characterize the local features of each minutia which describe a rotation and translation invariant features in its close sub-region. The matching algorithm used the minutiae covering zones (sub-regions) technique to compute the global matching. They are used to find the correspondence of two minutiae sets and the global fingerprint features at the same time. Thus, our approach doesnt embed fingerprint alignment into the rotation of the feature vector stage to overcome the critical reference point location and orientation problem as defined in the original approach [4]. The matching algorithm was tested on a subset of the fingerprint database DB2 used in FVC2002 and the results are promising.
Lecture Notes in Computer Science, 2005
In this paper, we propose an original fingerprint matching algorithm using a set of intrinsic coo... more In this paper, we propose an original fingerprint matching algorithm using a set of intrinsic coordinate systems which each one is attached to each minutia according to its orientation estimated from fingerprint image. Exploiting these coordinate systems, minutiae locations ...
Neurocomputing, 2011
Ensuring adequate use of the computing resources for highly fluctuating availability in multi-use... more Ensuring adequate use of the computing resources for highly fluctuating availability in multi-user computational environments requires effective prediction models, which play a key role in achieving application performance for large-scale distributed applications. Predicting the processor availability for scheduling a new process or task in a distributed environment is a basic problem that arises in many important contexts. The present paper aims at developing a model for single-step-ahead CPU load prediction that can be used to predict the future CPU load in a dynamic environment. Our prediction model is based on the control of multiple Local Adaptive Network-based Fuzzy Inference Systems Predictors (LAPs) via the Naïve Bayesian Network inference between clusters states of CPU load time points obtained by the C-means clustering process. Experimental results show that our model performs better and has less overhead than other approaches reported in the literature.
Future Generation Computer Systems, 2010
The metacomputing environments are becoming real distributed running platforms for computeintensi... more The metacomputing environments are becoming real distributed running platforms for computeintensive services. One of the most difficult problems to be solved by metacomputing systems is ensuring accurate and fast prediction of available performance on each resource. The main objective of the present study is to develop a new prediction model that can be used to predict the future CPU load in a distributed computing environment. This prediction model is based on a mixture of Adaptive Network based Fuzzy Inference Systems (ANFIS) via the naïve Bayes assumption. Experimental results for different load time series confirm that the new prediction model performs better than other CPU load prediction methods. In addition, a comparison with previous prediction methods to evaluate their accuracy is presented.
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inproceedings by Farid Benhammadi
articles by Farid Benhammadi
Papers by Farid Benhammadi