Faculty of Technology

Permanent URI for this community

https://ir.oauife.edu.ng/handle/123456789/303
Faculty of Technology

Browse

Browsing Faculty of Technology by Author "Adagunodo, E. R."

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Open Access
    An e-business grid model architecture for small and medium scale enterprises
    (2010-05) Bello, Sururah Apinke; Oluwatope, Ayodeji; Abiona, Olatunde; Sanni, Mistura; Onime, Clement E.; Eludiora, Safiriyu Ijiyemi; Aderounmu, Ganiyu A; Adagunodo, E. R.; Kehinde, L. O.
    In grid computing a loosely coupled and virtual computing infrastructure is created out of a collection of connected heterogeneous processors sharing various combinations of resources. It is pertinent to note that African Small and Medium Enterprises (SMEs) cannot participate in the global market using the current e-business solution due to the lack of or nonexistent infrastructure to support such services. In this paper, we propose a grid service based solution that is indigenous to African SMEs and technologically tailored to meet their local needs. The solution is based on an Application Service Provider (ASP) grid framework. The framework is specified using Universal Modeling Language (UML)
  • No Thumbnail Available
    Item
    Open Access
    An Improved Data Clustering Algorithm for Mining Web Documents
    (2010-12) Odukoya, O. H.; Aderounmu, Ganiyu A.; Adagunodo, E. R.
    This paper formulates, simulates and assess an improved data clustering algorithm for mining web documents with a view to preserving their conceptual similarities and eliminating the problem of speed while increasing accuracy. The improved data clustering algorithm was formulated using the concept of K-means algorithm. Real and artificial datasets were used to test the proposed and existing algorithm. The proposed algorithm was simulated using the fuzzy logic and statistical toolbox in Matlab 7.0. The simulated results were compared with the existing data clustering algorithm using accuracy, response time, adjusted rand index and entropy as performance parameters. The results show an improved data clustering algorithm with a new initialization method based on finding a set of medians extracted from a dimension with maximum variances. The results of the simulation showed that the accuracy is at its peak when the number of clusters is 3 and reduces as the number of clusters increases. When compared with existing algorithm, the proposed clustering algorithm showed an accuracy of 89.3% while the existing had an accuracy of 88.9%. The entropy was stable for both algorithms with a value of 0.2485 at k = 3. This also decreases as the number of clusters increase until when the number of clusters reached eight where it increased slightly. The adjusted rand index values varied from 0 to 1 for both clustering algorithms. The existing method achieved a value of 53% as compared with the proposed method which achieved an adjusted rand index value of 63.7%, when the number of clusters was five. In addition, the response time decreased from 0.0451 seconds to 0.0439 seconds when the number of clusters was three. This showed that the proposed data clustering algorithm decreased by 2.7% in response time as compared to the K-means data clustering. This study has shown that the proposed data clustering algorithm could be adapted by web search engine developers for more efficient web sea- - rch result clustering.