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2019, Saoussen Bel Haj Kacem,
The Compositional Rule of Inference Under the Composition Max-Product
In: Endres, D., Alam, M., Şotropa, D. (eds) Graph-Based Representation and Reasoning. ICCS 2019. Lecture Notes in Computer Science(), vol 11530. Springer, Cham., 2019
Abstract
Approximate reasoning is used in Fuzzy Inference Systems to handle imprecise knowledge. It aims to be close as possible to human reasoning. The main approach of approximate reasoning is the compositional rule of inference, which generates different methods by varying its parameters: a t-norm and an implication. In most cases, combinations of t-norms and implications do not fit human intuitions. Based on these methods, we suggest the use of the product t-norm in the compositional rule of inference. We combine this t-norm with different known implications. We then study these combinations and check if they give reasonable consequences.
, Saoussen Bel Haj KacemProjection of Fuzzy Knowledge Over X-Axis for a Unified Multi-valued Framework
Arab J Sci Eng 44, 3061–3082 (2019)., 2019
Abstract
Nowadays, knowledge-based system has to be able to model and treat imperfect knowledge. Among the knowledge imperfection, we cite imprecision. Imprecise information are generally represented in a quantitative way using fuzzy logic or in a qualitative way using symbolic multi-valued logic. As far as we knew, no work has considered both fuzzy and symbolic multi-valued knowledge simultaneously in the same knowledge-based system. However, the user is often in need of both data types to insure a relevant decision-making. In order to improve the decision-making process performance, we propose in this paper an approach, that is able to standardize input knowledge. In fact, we propose a fuzzy-to-symbolic conversion of inputs by projecting them over the abscissa axis. We apply the proposed conversion module in symbolic inference systems. Thus, a symbolic approximate reasoning can be executed. The conversion process involves the expert by asking him to express its tolerance threshold toward handled fuzzy knowledge. Thus, a minimum of fuzzy information loss will be insured according to the expert preferences and the reasoning context. Our proposal is also useful even when the rule conclusion is originally fuzzy. In that case, a symbolic-to fuzzy conversion of the inference result is required to make the inference result more intelligible for the user and to maintain the transparency of the fuzzy-to-symbolic conversion. A numerical study is provided to illustrate the potential applications of the proposed methodology.
Hamdi Ouechtati, Nadia Ben Azzouna, Lamjed Ben SaidA fuzzy logic based trust-ABAC model for the Internet of Things
In International Conference on Advanced Information Networking and Applications (pp. 1157-1168). Cham: Springer International Publishing., 2019
Abstract
The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. In order to cope with certain challenges posed by device capacity and the nature of IoT networks, a lightweight access control model is needed to resolve security and privacy issues. In this paper, we present Fuzzy logic based Trust-ABAC model, an access control model for the Internet of Things. Our model for the IoT is an improvement of our previous work Trust-ABAC by a new Fuzzy logic-based model in which we consider an evaluation of trust based on recommendations and social relationship that can deal effectively with certain types of malicious behavior that intend to mislead other nodes. Results prove the performance of the proposed model and its capabilities to detect the collision and singular attacks with high precision.
Houyem Ben Hassen, Jihene Tounsi,An Artificial Immune Algorithm for HHC Planning Based on multi-Agent System
Procedia Computer Science, 164, 251-256, 2019
Abstract
This paper presents the home health care routing and scheduling problem as the vehicle routing problem with time windows (VRPTW). we propose a dynamic approach for home care planning to ensure the continuity of care for patients. The proposed approach aims to optimize the care plan route of each caregiver according to their skills, availabilities and preferences. We aim also to minimize the violation of time windows in order to maximize patient and caregiver’s satisfaction. The optimal plan route is generated with a population-based algorithm which is the Artificial Immune Algorithm (AIS). A multi-agent approach is used to ensure communication and coordination between the different actors.
, , Lilia RejebA honeybee mating optimization algorithm for solving the static bike rebalancing problem
Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM. Presented at the GECCO ’19: Genetic and Evolutionary Computation Conference, ACM New York (pp. 77-78). Prague Czech Republic. doi:10.1145/3319619, 2019
Abstract
This paper proposes a new approach to solve the Bike Rebalancing Problem (BRP) based on the Honey-Bee Mating Optimization (HBMO) algorithm. The aim is to reduce the overall traveling cost of redistribution operations under various constraints. The performance of the proposed algorithm is evaluated using a set of benchmark instances for the BRP. Preliminary results are obtained and showed that the proposed approach is promising.
Hanen Lejmi, Mouna Belhaj, Lamjed Ben SaidStudying Emotions at Work Using Agent-Based Modeling and Simulation
Studying Emotions at Work Using Agent-Based Modeling and Simulation, 2019
Abstract
Emotions in workplace is a topic that has increasingly attracted attention of both organizational practitioners and academics. This is due to the fundamental role emotions play in shaping human resources behaviors, performance, productivity, interpersonal relationships and engagement at work. In the current research, a computational social simulation approach is adopted to replicate and study the emotional experiences of employees in organizations. More specifically, an emotional agent-based model of an employee at work is proposed. The developed model is used in a computer simulator WEMOS (Workers EMotions in Organizations Simulator) to conduct certain analyzes in relation to the most likely emotions-evoking stimuli as well as the emotional content of several work-related stimuli. Simulation results can be employed to gain deeper understanding about emotions in the work life.
Slim Bechikh, Maha Elarbi, , , Lamjed Ben SaidA Hybrid Evolutionary Algorithm with Heuristic Mutation for Multi-objective Bi-clustering
In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 2323-2330). IEEE, 2019
Abstract
Bi-clustering is one of the main tasks in data mining with several application domains. It consists in partitioning a data set based on both rows and columns simultaneously. One of the main difficulties in bi-clustering is the issue of finding the number of bi-clusters, which is usually a user-specified parameter. Recently, in 2017, a new multi-objective evolutionary clustering algorithm, called MOCK-II, has shown its effectiveness in data clustering while automatically determining the number of clusters. Motivated by the promising results of MOCK-II, we propose in this paper a hybrid extension of this algorithm for the case of bi-clustering. Our new algorithm, called MOBICK, uses an efficient solution encoding, an effective crossover operator, and a heuristic mutation strategy. Similarly to MOCK-II, MOBICK is able to find automatically the number of bi-clusters. The outperformance of our algorithm is shown on a set of real gene expression data sets against several existing state-of-the-art works. Moreover, to be able to compare MOBICK to MOCK-I and MOCK-II, we have designed two basic extensions of MOCK-I and MOCK-II for the case of bi-clustering that we named B-MOCK-I and B-MOCK-II. Again, the experimental results confirm the merits of our proposal.
Rahma Dhaouadi, ,Weighted utility based recommender for e-procurement in handicraft communities
iiWAS 2019: 448-452, 2019
Abstract
In this paper, we would like to assess the positive impact of the recommendation process during the professional activities of business actors. We are interested specifically in the improvement of the economic life of the handicraft women from emerging countries. To this end, we introduce a utility based recommender dealing with the procurement opportunities. Actually, we proposed a utility function which takes into account the weighted preferences and expectations of final users. The system is evaluated based on the gain to obtain if the proposed recommendations are adopted in addition to the satisfaction level of the final users.
Ons Maatouk, Wassim Ayadi, ,Evolutionary biclustering algorithms: an experimental study on microarray data
Soft Computing 23(17): 7671-7697, 2019
Abstract
The extraction of knowledge from large biological data is among the main challenges of bioinformatics. Several data mining techniques have been proposed to extract data; in this work, we focus on biclustering which has grown considerably in recent years. Biclustering aims to extract a set of genes with similar behavior under a condition set. In this paper, we propose an evolutionary biclustering algorithm and we analyze its performance by varying its genetic components. Hence, several versions of the evolutionary biclustering algorithm are introduced. Further, an experimental study is achieved on two real microarray datasets and the results are compared to other state-of-the-art biclustering algorithms. This thorough study allows to retain the best combination of operators among the various experienced choices.
, Thouraya Sakouhi,A trajectory ontology design pattern for semantic trajectory data warehouses: behavior analysis and animal tracking case studies
In Emerging perspectives in big data warehousing (pp. 83-104). IGI Global., 2019
Abstract
Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.
