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Journal Cover ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal
  [7 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 2255-2863
   Published by Universidad de Salamanca Homepage  [3 journals]
  • Multi-agent system for Knowledge-based recommendation of Learning Objects

    • Abstract: Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.
      PubDate: 2015-10-07
      Issue No: Vol. 4 (2015)
       
  • Index vol 4 n. 1

    • PubDate: 2015-10-07
      Issue No: Vol. 4 (2015)
       
  • Bridging the gap between human knowledge and machine learning

    • Abstract: Nowadays, great amount of data is being created by several sources from academic, scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning -which is holistic, flexible and parallel- may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided.
      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
  • Improving Podcast Distribution on Gwanda using PrivHab: a Multiagent
           Secure Georouting Protocol.

    • Abstract: We present PrivHab, a multiagent secure georouting protocol that improves podcast distribution on Gwanda, Zimbabwe. PrivHab learns the whereabouts of the nodes of the network to select an itinerary for each agent carrying a piece of data. PrivHab makes use of cryptographic techniques to make the decisions while preserving nodes' privacy. PrivHab uses a waypoint-based georouting that achieves a high performance and low overhead in rugged terrain areas that are plenty of physical obstacles. The store-carry-and-forward approach used is based on mobile agents and is designed to operate in areas that lack network infrastructure. The PrivHab protocol is compared with a set of well-known delay-tolerant routing algorithms and shown to outperform them.
      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
  • SmartHeart CABG Edu

    • Authors: Gabriele DI GIAMMARCO, Tania DI MASCIO, Michele DI MAURO, Antonietta TARQUINIO, Pierpaolo VITTORINI
      Abstract: The paper reports on the SmartHeart CABG Edu Android app. The app was conceived to be an innovative and up-to-date tool for patient education, the first of its kind in the Italian context. In particular, the app was developed to provide educational material for patients about to undergo Coronary Artery Bypass Graft (CABG) surgery, a set of self-assessment tools concerning health status (i.e., BMI calculator, LDL cholesterol calculator and anxiety assessment tool) and usability questionnaires (i.e., SEQ and SUS). The paper initially describes the app, then reports on its evaluation, concerning both the app usability and the pre-operative anxiety, and ends by showing the improvements -- derived from the usability evaluation -- put into practice.
      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
  • A Semantic Social Recommender System Using Ontologies Based Approach For
           Tunisian Tourism

    • Authors: Mohamed FRIKHA, Mohamed MHIRI, Faiez GARGOURI
      Abstract: 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.
      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
  • Dominance Weighted Social Choice Functions for Group Recommendations

    • Authors: Silvia ROSSI, Antonio CASO
      Abstract: In travel domains, decision support systems provide support to tourists in the planning of their vacation. In particular, when the number of possible Points of Interest (POI) to visit is large, the system should help tourists providing recommendations on the POI that could be more interesting for them. Since traveling is, usually, an activity that involves small groups of people, the system should take simultaneously into account the preferences of each group's member. At the same time, it also should model possible intra-group relationships, which can have an impact in the group decision-making process. In this paper, we model this problem as a multi-agent aggregation of preferences by using weighted social choice functions, whereas such weights are automatically evaluated by analyzing the interactions of the group's members on Online Social Networks.
      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
  • An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem

    • Abstract: The Set Covering Problem (SCP) is a NP-hard combinatorial optimization problem that is challenging for meta-heuristic algorithms. In the optimization literature, several approaches using meta-heuristics have been developed to tackle the SCP and the quality of the results provided by these approaches highly depends on customized operators that demands high effort from researchers and practitioners. In order to alleviate the complexity of designing metaheuristics, a methodology called hyper-heuristic has emerged as a possible solution. A hyper-heuristic is capable of dynamically selecting simple low-level heuristics accordingly to their performance, alleviating the design complexity of the problem solver and obtaining satisfactory results at the same time. In a previous study, we proposed a hyper-heuristic approach based on Ant Colony Optimization (ACO-HH) for solving the SCP. This paper extends our previous efforts, presenting better results and a deeper analysis of ACO-HH parameters and behavior, specially about the selection of low-level heuristics. The paper also presents a comparison with an ACO meta-heuristic customized for the SCP.

      PubDate: 2015-10-06
      Issue No: Vol. 4 (2015)
       
 
 
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