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2017, Lilia Rejeb, Lamjed Ben Said, Maha Elarbi
Evidential learning classifier system
Authors: Chedi Abdelkarim, Lilia Rejeb, Lamjed Ben Said, Maha ElarbiAuthors Info & Claims GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion Pages 123 - 124 https://doi.org/10.1145/3067695.3075997, 2017
Abstract
During the last decades, Learning Classifier Systems have known many advancements that were highlighting their potential to resolve complex problems. Despite the advantages offered by these algorithms, it is important to tackle other aspects such as the uncertainty to improve their performance. In this paper, we present a new Learning Classifier System (LCS) that deals with uncertainty in the class selection in particular imprecision. Our idea is to integrate the Belief function theory in the sUpervised Classifier System (UCS) for classification purpose. The new approach proved to be efficient to resolve several classification problems.
Sami Rojbi,KeybNav: a new system for web navigation through a keyboard
2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), IEEE, 2017
Abstract
Accessibility guidelines emphasize the need to allow the user to interact with web pages not only through a pointing device, but through the keyboard as well. The specification of HTML4 came with the access keys but they have resulted in an unexpected failure. This paper proposes a new system which deters conflicts between web page access keys and keyboard shortcuts used by various OS applications.
, Wassim Ayadi,Mining Negative Correlation Biclusters from Gene Expression Data using Generic Association Rules
Procedia Computer Science Volume 112, Pages 278-287, 2017
Abstract
A majority of existing biclustering algorithms for microarrays data focus only on extracting biclusters with positive correlations of genes. Nevertheless, biological studies show that a group of biologically significant genes may exhibit negative correlations. In this paper, we propose a new biclustering algorithm, called NBic-ARM (Negative Biclusters using Association Rule Mining). Based on Generic Association Rules, our algorithm identifies negatively-correlated genes. To assess NBic-ARM’s performance, we carried out exhaustive experiments on three real-life datasets. Our results prove NBic-ARM’s ability to identify statistically and biologically significant biclusters.
Riadh Ghlala, Zahra Kodia, Lamjed Ben SaidMC-DMN: Meeting MCDM with DMN Involving Multi-criteria Decision-Making in Business Process
Conference: International Conference on Computational Science and Its Applications, 2017
Abstract
The modelling of business processes and in particular decision-making in these processes takes an important place in the quality and reliability of IT solutions. In order to define a modelling standards in this domain, the Open Management Group (OMG) has developed the Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN). Currently, these two standards are a pillar of several business architecture Frameworks to support Business-IT alignment and minimize the gap between the managers expectations and delivered technical solution. In this paper, we propose the Multi-Criteria DMN (MC-DMN) which is a DMN enrichment. It allows covering the preference to criteria in decision-making using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a Multi-Criteria Decision-Making (MCDM) method and therefore it gives more faithfulness to the real world and further agility face the business layer changes.
Riadh Ghlala, Zahra Kodia, Lamjed Ben SaidMulti-Agent BPMN Decision Footprint
Conference: KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, 2017
Abstract
Nowadays, we are confronted with increasingly complex information systems. Modelling these kinds of systems will only be controlled through appropriate tools, techniques and models. Work of the Open Management Group (OMG) in this area have resulted in the development of Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN). Currently, these two standards are a pillar of various business architecture Frameworks to support Business-IT alignment and minimize the gap between the managers’ expectations and delivered technical solutions. Several research focus on the extension of these models especially BPMNDF which aims to harmonize decision-making throughout a single business process. The current challenge is to extend the BPMNDF in order to cover business process in a distributed and cooperative environment. In this paper, we propose the Multi-Agent BPMN Decision Footprint (MABPMNDF) which is a novel model based on both BPMNDF and MAS to support decision-making in distributed business process.
, Ines Thabet, , Lamjed Ben SaidModelling and simulating a crisis management system: an organisational perspective
Enterprise Information Systems, Volume 11, 2017
Abstract
Crises are complex situations due to the dynamism of the environment, its unpredictability and the complexity of the interactions among several different and autonomous involved organisations. In such a context, establishing an organisational view as well as structuring organisations’ communications and their functioning is a crucial requirement. In this article, we propose a multi-agent organisational model (OM) to abstract, simulate and analyse a crisis management system (CMS). The objective is to evaluate the CMS from an organisational view, to assess its strength as well as its weakness and to provide deciders with some recommendations for a more flexible and reactive CMS. The proposed OM is illustrated through a real case study: a snowstorm in a Tunisian region. More precisely, we made the following contribution: firstly, we provide an environmental model that identifies the concepts involved in the crisis. Then, we define a role model that copes with the involved actors. In addition, we specify the organisational structure and the interaction model that rule communications and structure actors’ functioning. Those models, built following the GAIA methodology, abstract the CMS from an organisational perspective. Finally, we implemented a customisable multi-agent simulator based on the Janus platform to analyse, through several performed simulations, the organisational model
, Ines Thabet,A Framework to Support Tunisian Tweets Analysis for Crisis Management
ISCRAM-med 2017, 2017
Abstract
The increasing crisis frequency and the growing impact of their damages require efficient crisis management processes in order to manage crisis effectively and reduce losses. In such context, the need of accurate and updated information about crises is extremely important. In recent years, crisis information has frequently been provided by social media platforms such as Twitter, Facebook, Flickr, etc. In fact, considering the huge amount of shared information, their precision and their real time characteristic, organizations are moving towards the development of crisis management applications that include information provided by social media platforms. Following this view, the main purpose of our work is to propose a framework for Tunisian tweets extraction and analysis. More precisely, we provide an architecture that includes necessary components and tools for Tunisian dialect treatment. The proposed architecture is an extension on the existing AIDR platform. In addition, we specify the functioning of the proposed architecture to enable firstly terms transliteration from Arabic to Latin alphabet, secondly their normalization and finally their translation in order to be treated by existing social media analysis platform.
Abir Chaabani, Slim Bechikh, Lamjed Ben SaidA co-evolutionary decomposition-based chemical reaction algorithm for bi-level combinatorial optimization problems.
International conference on Knowledge Based and Intelligent information and Engineering Systems KES’17, France, 112, 780-789, 2017
Abstract
Bi-level optimization problems (BOPs) are a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem which has another optimization problem as a constraint. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level one. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Recently, a new research field, called EBO (Evolutionary Bi-Level Optimization) has appeared thanks to the promising results obtained by the use of EAs (Evolutionary Algorithms) to solve such kind of problems. However, most of these promising results are restricted to the continuous case. The number of existing EBO works for the discrete (combinatorial case) bi-level problems is relatively small when compared to the field of evolutionary continuous BOP. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-Based Algorithm (CODBA) to solve combinatorial bi-level problems. The recently proposed approach applies a Genetic Algorithm to handle BOPs. Besides, a new recently proposed meta-heuristic called CRO has been successfully applied to several practical NP-hard problems. To this end, we propose in this work a CODBA-CRO (CODBA with Chemical Reaction Optimization) to solve BOP. The experimental comparisons against other works within this research area on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of our proposal.
Thouraya Sakouhi, ,Computing Semantic Trajectories: Methods and Used Techniques
In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_39, 2017
Abstract
The widespread use of mobile devices generates huge amount of location data. The generated data is useful for many applications, including location-based services such as outdoor sports forums, routine prediction, location-based activity recognition and location-based social networking. Sharing individuals’ trajectories and annotating them with activities, for example a tourist transportation mode during his trip, helps bringing more semantics to the GPS data. Indeed, this provides a better understanding of the user trajectories, and then more interesting location-based services. To address this issue, diverse range of novel techniques in the literature are explored to enrich this data with semantic information, notably, machine learning and statistical algorithms. In this work, we focused, at a first level, on exploring and classifying the literature works related to semantic trajectory computation. Secondly, we capitalized and discussed the benefits and limitations of each approach.
, ,A Multi-Agent based Hyper-Heuristic Algorithm for the Winner Determination Problem
Procedia Computer Science 112:117-126, 2017
Abstract
In this paper we propose a Multi-Agent based Hyper-Heuristic algorithm for theWinner Determination Problem named MAH2- WDP. This algorithm explores a set of cooperating agents to select the appropriate operation using learning techniques. MAH2- WDP is specialized for local search methods and evolutionary methods where the following agents are seeking to improve the search within reinforcement learning: the mediator agent, two local search agents, the perturbation agent and two recombination agents. Our computational study shows that the proposed algorithm performs well on the tested benchmark instances in terms of solution quality. Keywords: Multi-agent; Winner Determination Problem; hyper-heuristic; intensification; diversification; metaheuristics.
