Accepted Papers

  • AHP UNDER UNCERTAINTY: A MODIFIED VERSION OF CLOUD DELPHI HIERARCHICAL ANALYSIS
    Alaa Abou Ahmad1, Ghaida Rebdawi1 and Obaida alsahli2, 1 Information Department, HIAST, Damascus, Syria, 2Quality Program, SVU, Damascus, Syria
    ABSTRACT

    Cloud Delphi Hierarchical Analysis (CDHA) is an Analytic Hierarchical Process (AHP) based method for group decision making under uncertain environments. CDHA adopts appropriate tools for such environments, namely Delphi method, and Cloud model. Adopting such tools make it a promising AHP variant in handling uncertainty. In spite of CDHA is a promising method, it is still suffering from two main defects. The first one lies in its definition of the consistency index, the second one lies in the technique used in building the pairwise comparisons Cloud models. This paper will discuss these defects, and propose a modified version. To overcome the defects mentioned above, the modified version will depend more on the context of the interval pairwise comparisons matrix while building the corresponding Cloud pairwise comparisons matrix. A simple case study that involves reproducing the relative area sizes of four provinces in Syria will be used to illustrate the modified version and to compare it with the original one.

  • PREDICTINGPREMAURITYAT BIRTHONTHE BASISOFINTELLIGENTMETHODS
    S. Mostafa Mazhari, Farzad Towhidkhah, Rasoul Khayati, Fatemeh Haji Ebrahim Tehrani, Ehsan Ghajari, Biomedical Engineering Faculty, Shahed University, Tehran, Iran
    ABSTRACT

    Preterm birth is defined childbirth happening after a gestation period of less than 37 weeks. On average, every year, 10% of the newborns, and approximately 13 million newborns are born prematurely. 75% of deaths in newborns are due to premature birth. In this research, using intelligent methods, premature birth is predicted. A questionnaire was prepared, and data were collected from 1101 mothers who have recently given birth, in 6 hospitals in Iran, in a period of 4 months. Hundreds of tests were performed using, RBF neural network, and the Mamdani and SugenoFuzzy Inference Systems with FCM clustering method, and differential cluster method. Independent tests were performed following selection of important and effective risk factors. For evaluation of the test results, the ROC curve and evaluation matrix were used.This study demonstrated that the predictive model showed a non-linear behavior.This study can be used as part of a CDSS.

  • Random Subspace Two-dimensional LDA for Face Recognition
    Garrett Bingham, Yale University ,USA
    ABSTRACT

    In this paper, a novel technique named random subspace two-dimensional LDA (RS-2DLDA) is developed for face recognition. This approach offers a number of improvements over the random subspace two-dimensional PCA (RS- 2DPCA) framework introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more discriminative power than those from 2DPCA, resulting in higher accuracy for the RS-2DLDA method over RS-2DPCA. Various distance metrics are evaluated, and a weighting scheme is developed to further boost accuracy. A series of experiments on the MORPH-II and ORL datasets are conducted to demonstrate the effectiveness of this approach.

  • An Autonomous Agent for the Robotic Arm Jaco Kinova
    Edi M. M. de Araujo and Augusto Loureiro da Costa, Robotics Laboratory, Federal University of Bahia,Salvador, Bahia, Brazil
    ABSTRACT

    This work presents the use of the Concurrent Autonomous Agent (AAC) in an Jaco Kinova robot arm, enabling it to perform complex tasks in a completely autonomous way. The communication between the AAC and the manipulator will be made through the ROS (Robot Operating System), as well as the performance of the behaviors present in the reactive level. The Concurrent Autonomous Agent is an implementation of a cognitive agent architecture based on the Generic Cognitive Model for Autonomous Agents. This model over the years proved to be very effective, initially being used for the implementation of a distributed control system for multi-robot systems, called Mecateam, obtaining significant results in RoboCup's Latin America and Brazilians. The AAC has already been implemented in several successful applications, such as the NAO humanoid robot and the AxeBot omnidirectional robot. These results point to AAC as a model of well-known cognition for the training of robots to perform tasks that require a certain degree of cognition.