Accepted Papers


  • SINE COSINE CROW SEARCH ALGORITHM: A POWERFUL HYBRID META HEURISTIC FOR GLOBAL OPTIMIZATION
    Seyed Hamid Reza Pasandideh and Soheyl Khalilpourazari,Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
    ABSTRACT

    This paper presents a novel hybrid algorithm named Since Cosine Crow Search Algorithm. To propose the SCCSA, two novel algorithms are considered including Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of the two algorithms are considered and utilize to design an efficient hybrid algorithm which can perform significantly better in various benchmark functions. The combination of concept and operators of the two algorithms enable the SCCSA to make an appropriate trade-off between exploration and exploitation abilities of the algorithm. To evaluate the performance of the proposed SCCSA, seven well-known benchmark functions are utilized. The results indicated that the proposed hybrid algorithm is able to provide very competitive solution comparing to other state-of-the-art meta heuristics.

  • Computer Aided Liver Tumor Detector - CALTD
    Thayalini Piragash,Department of Computing,Informatics Institute of Technology,Sri Lanka
    ABSTRACT

    Computer-aided liver tumor detection can be an assist to the radiologist to detect the liver tumors from the abdominal CT image. This research proposes a software solution to illustrate the automated liver segmentation and tumor detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumor detection, in cooperation with the radiologist by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples collected from ten patients and received 90% accuracy rate.

  • LEAN LEVEL OF AN ORGANIZATION ASSESSED BASED ON FUZZY LOGIC
    A. Abreu and J. M. F. Calado,Mechanical Engineering Department, ISEL - Instituto Superior de Engenharia de Lisboa, IPL – Polytechnic Institute of Lisbon Rua Conselheiro Emídio Navarro, 1, 1959-007 Lisboa, Portugal
    ABSTRACT

    To determine the lean level of an organization a methodology was developed. It was based on a qualitative assessment approach, including quantitative basis, whose development was supported using fuzzy logic. Recourse to the use of fuzzy logic is justified by its ability to cope with uncertainty and imprecision on the input data, as well as, could be applied to the analysis of qualitative variables of a system, turning them into quantitative values. A major advantage of the developed approach is that it can be adjusted to any organization regardless of their nature, size, strategy and market positioning. Furthermore, the proposed methodology allows the systematically identification of constraint factors existing in an organization and, thus, provide the necessary information to the manager to develop a holistic plan for continuous improvement. To assess the robustness of the proposed approach, the methodology was applied to a maintenance and manufacturing aeronautical organization.

  • Error Estimates for Multi-Penalty Regularization under General Source Condition
    Abhishake Rastogi,Department of Mathematics,Indian Institute of Technology Delhi,India
    ABSTRACT

    In learning theory, the convergence issues of the regression problem are investigated with the least square Tikhonov regularization schemes in both the RKHS-norm and the L2-norm. We consider the multi-penalized least square regularization scheme under the general source condition with the polynomial decay of the eigenvalues of the integral operator. One of the motivation for this work is to discuss the convergence issues for widely considered manifold regularization scheme. The optimal convergence rates of multi-penalty regularizer is achieved in the interpolation norm using the concept of e ective dimension. Further we also propose the penalty balancing principle based on augmented Tikhonov regularization for the choice of regularization parameters. The superiority of multi-penalty regularization over single-penalty regularization is shown using the academic example and moon data set.

  • AN INTERVAL TYPE-2 FUZZY LOGIC-BASED FRAMEWORK FOR ADMISSION CONTROL IN 4G MOBILE NETWORKS
    Uduak Umoh1, Daniel Asuquo2 and Imoh Eyoh3,1,2Department of Computer Science,3University of Uyo, Akwa Ibom State, Nigeria,3ASAP Research Group, University of Nottingham Nottingham, United Kingdom
    ABSTRACT

    This paper presents a technique to control based on interval type-2 fuzzy logic system (IT2FLS) of Mamdani fuzzy inference, employed to model connection admission control in Fourth Generation (4G) Networks to improve quality of service (QoS). The appropriate parameter selection to achieve connection admission control is also considered based on major system parameters like latency, packet loss, load, signal strength and user mobility. We explore the use of Karnik-Mendel (KM) and Wu-Mendel (WM) approaches in our proposed system. We also implement a T1FLS connection admission control for guaranteed QoS in 4G mobile networks for comparison purposes. The empirical comparison is made on the designed system using 4G network admission control synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in IT2FLS-WM controller tend to reduce the root mean square error (RMSE) of the model compared to IT2FLS-KM and fuzzy type-1 approaches.