A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism

Tunisia is well placed in terms of medical tourism and has highly qualified and specialized medical and surgical teams. Integrating social networks in Tunisian medical tourism recommender systems can result in much more accurate recommendations. That is to say, information, interests, and recommendations retrieved from social networks can improve the prediction accuracy. This paper aims to improve traditional recommender systems by incorporating information in social network; including user preferences and influences from social friends. Accordingly, a user interest ontology is developed to make personalized recommendations out of such information. In this paper, we present a semantic social recommender system employing a user interest ontology and a Tunisian Medical Tourism ontology. Our system can improve the quality of recommendation for Tunisian tourism domain. Finally, our social recommendation algorithm is implemented in order to be used in a Tunisia tourism Website to assist users interested in visiting Tunisia for medical purposes

Graphical Models for Multi-dialect Arabic Isolated Words Recognition

AbstractThis paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN

S-DLCAM: A Self-Design and Learning Cooperative Agent Model for Adaptive Multi-Agent Systems

International audienceGiven the incomplete knowledge that an Adaptive Multi Agent System (AMAS) has on its dynamic environment, the detection and the correction of problems encountered called Non Cooperative Situations for the construction of the good behaviour of the AMAS agent can challenge even the most experienced designer. Our goal is to help the AMAS designer in his task by providing an agent behaviour able to self-design. In this paper, we propose a self-design and learning cooperative agent model

Building a Know-How and Knowing-That Cartography to Enhance KM Processes in a Healthcare Setting

While knowledge management (KM) is becoming an established discipline with many applications and techniques, its adoption in healthcare has been challenging. It facilitates the creation, identification, acquisition, development, preservation, dissemination, and finally utilization of various facets of a healthcare enterprise’s knowledge assets. Knowledge identification and preservation are two facets of knowledge capitalization’s operations. Knowledge cartography is used nowadays as a tool for knowledge identification, sharing, and decision support. In this paper, we propose a Know-How and Knowing-That cartography for Healthcare Information System (HIS) and clinical decision support in the context of the organization of protection of the motor disabled children of Sfax-Tunisia (ASHMS). In fact, this cartography enables decision makers with general and detailed visibility of Know-How and Knowing-That mobilized in the ASHMS. It also facilitates clinical decision support by proposing the most appropriate alternatives for the continued treatment (or cessation) of each motor disabled child receiving treatment

BigDimETL with NoSQL Database

In the last decade, we have witnessed an explosion of data volume available on the Web. This is due to the rapid technological advances with the availability of smart devices and social networks such as Twitter, Facebook, Instagram, etc. Hence, the concept of Big Data was created to face this constant increase. In this context, many domains should take in consideration this growth of data, especially, the Business Intelligence (BI) domain. Where, it is full of important knowledge that is crucial for effective decision making. However, new problems and challenges have appeared for the Decision Support System that must be addressed. Accordingly, the purpose of this paper is to adapt Extract-Transform-Load (ETL) processes with Big Data technologies, in order to support decision-making and knowledge discovery. In this paper, we propose a new approach called Big Dimensional ETL (BigDimETL) dealing with ETL development process and taking into account the Multidimensional structure. In addition, in order to accelerate data handling we used the MapReduce paradigm and Hbase as a distributed storage mechanism that provides data warehousing capabilities. Experimental results show that our ETL operation adaptation can perform well especially with Join operation

A Hybrid Recommender System for HCI Design Pattern Recommendations

[EN] User interface design patterns are acknowledged as a standard solution to recurring de¿ sign problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first con¿ tribution is the development of a recommender system for selecting the most relevant design pat¿ terns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text¿based and ontology¿based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this pur¿ pose, we conducted a user¿centric evaluation experiment wherein participants were invited to fill pre¿study and post¿test questionnaires. The findings of the evaluation study revealed that the per¿ ceived experience of the proposed system¿s quality and the accuracy of the recommended design patterns were assessed positively.Braham, A.; Khemaja, M.; Buendía García, F.; Gargouri, F. (2021). A Hybrid Recommender System for HCI Design Pattern Recommendations. Applied Sciences. 11(22):1-25. https://doi.org/10.3390/app112210776S125112

Simulation Based Design for Adaptive Multi-Agent Systems: Extensions for the ADELFE methodology

International audienceADELFE is a methodology proposed to help and guide the designer during the development of an Adaptive Multi-Agent System (AMAS). In this paper, we propose extensions to ADELFE in order to facilitate the task of the designer and help him to detect and correct the Non Cooperative Situations that the agent may encounter during its life

A Typology of Temporal Data Imperfection

International audienceTemporal data may be subject to several types of imperfection (e.g., uncertainty, imprecision..). In this context, several typologies of data imperfections have been already proposed. However, these typologies cannot be applied to temporal data because of the complexity of this type of data and the specificity that it contains. Besides, to the best of our knowledge, there is no typology of temporal data imperfections. In this paper, we propose a typology of temporal data imperfections. Our typology is divided into direct imperfections of both numeric temporal data and natural language based temporal data, indirect imperfections that can be deduced from the direct ones and granularity (i.e., context - dependent temporal data) which is related to several factors that can interfer in specifying the imperfection type such as person’s profile and multiculturalism. We finish by representing an example of imprecise temporal data in PersonLink ontology