• DocumentCode
    2549232
  • Title

    Model building methodologies utilizing Artificial Neural Network (ANN) for performance of project planning, implementation and controlling processes

  • Author

    Gandapur, Muhammad Salim Javed ; Mahmood, Ahmed Kamil Bin ; Sulaiman, Suziah B.

  • Author_Institution
    Comput. & Inf. Sci. Dept., Univ. Teknol. PETRONAS, Tronoh, Malaysia
  • fYear
    2010
  • fDate
    16-18 April 2010
  • Firstpage
    439
  • Lastpage
    443
  • Abstract
    Model building methodologies are playing an increasingly significant role in many aspects of software engineering activities. Today models are being applied right from requirement conceptualization to the final software installation and maintenance. Traditional methodologies however, fail to cope with increasing complexity and rapidly evolving nature of the software. The need for an efficient model building methodology is quite manifest today. The main objective of this study is to propose and implement a novel Model Building Methodology utilizing Artificial Neural Network (ANN). In order to achieve this objective, information related to regression analysis was reviewed.
  • Keywords
    neural nets; planning; project management; regression analysis; software maintenance; software management; ANN; artificial neural network; model building methodology; project planning; regression analysis; requirement conceptualization; software engineering activities; software installation; software maintenance; Artificial neural networks; Inventory management; Investments; Predictive models; Process control; Process planning; Productivity; Profitability; Project management; Resource management; Artificial Neural Network (ANN); Multiple Regression Method; Prognostic Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-5263-7
  • Electronic_ISBN
    978-1-4244-5265-1
  • Type

    conf

  • DOI
    10.1109/ICIME.2010.5477865
  • Filename
    5477865