IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v10y2015i2p34.html
   My bibliography  Save this article

Utilizing Modular Neural Network for Prediction of Possible Emergencies Locations within Point of Interest of Hajj Pilgrimage

Author

Listed:
  • Adamu Abubakar
  • Haruna Chiroma
  • Abdullah Khan
  • Mukhtar Fatihu Hamza
  • Ali Baba Dauda
  • Mahmood Nadeem
  • Shah Asadullah
  • Jaafar Zubairu Maitama
  • Tutut Herawan

Abstract

This paper utilize modular neural network for prediction of possible emergencies locations during hajj pilgrimage. Available location, localization and positioning determination systems become increasingly important for use in day-to-day activities. These systems dwells on various scientific tools which ensure that the systems will provide accurate response to the needed service at the right time. Unfortunately, some tools were faced with drawbacks, either their use was not appropriate or they do not give reliable results, or the results obtained in certain scenario might not be apply to other scenarios. For this reasons, we utilize modular neural network tool to examine the analysis of determining possible emergencies locations within point of Interest of Hajj Pilgrimage in Meccah Saudi Arabia. The prediction results are generated by the use of longitude, latitude and distances as the dataset. Modular neural network takes longitude and latitude as inputs and predict distances within pilgrim’s possible point of interest. The learning systems were trained on the collected data. Experimental investigation demonstrated that modular network produce higher prediction accuracy compaired to other tools. This finding would contribute to the design of add-on applications which will deem to provide location based services for possible emergencies locations.

Suggested Citation

  • Adamu Abubakar & Haruna Chiroma & Abdullah Khan & Mukhtar Fatihu Hamza & Ali Baba Dauda & Mahmood Nadeem & Shah Asadullah & Jaafar Zubairu Maitama & Tutut Herawan, 2016. "Utilizing Modular Neural Network for Prediction of Possible Emergencies Locations within Point of Interest of Hajj Pilgrimage," Modern Applied Science, Canadian Center of Science and Education, vol. 10(2), pages 1-34, February.
  • Handle: RePEc:ibn:masjnl:v:10:y:2015:i:2:p:34
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/55908/29977
    Download Restriction: no

    File URL: https://ccsenet.org/journal/index.php/mas/article/view/55908
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Siddhivinayak Kulkarni & Imad Haidar, 2009. "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices," Papers 0906.4838, arXiv.org.
    2. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    2. Yongxin Yang & Yu Zheng & Timothy M. Hospedales, 2016. "Gated Neural Networks for Option Pricing: Rationality by Design," Papers 1609.07472, arXiv.org, revised Mar 2020.
    3. Anindya Goswami & Nimit Rana, 2024. "A market resilient data-driven approach to option pricing," Papers 2409.08205, arXiv.org.
    4. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    5. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    6. Yan Liu & Xiong Zhang, 2023. "Option Pricing Using LSTM: A Perspective of Realized Skewness," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    7. Gradojevic Nikola, 2016. "Multi-criteria classification for pricing European options," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 123-139, April.
    8. Manuel Monge & Ana Lazcano, 2022. "Commodity Prices after COVID-19: Persistence and Time Trends," Risks, MDPI, vol. 10(6), pages 1-20, June.
    9. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    10. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    11. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    12. Mona Shazly & Alice Lou, 2016. "Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 22(4), pages 361-376, November.
    13. Hammad Siddiqi & Sajid Anwar, 2020. "The Pricing Kernel Puzzle: A Real Phenomenon or a Statistical Artifact?," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 485-491, June.
    14. Dragan Kukolj & Nikola Gradojevic & Camillo Lento, 2012. "Improving Non-Parametric Option Pricing during the Financial Crisis," Working Paper series 35_12, Rimini Centre for Economic Analysis.
    15. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    16. Joseph L. Breeden & Eugenia Leonova, 2021. "Creating Unbiased Machine Learning Models by Design," JRFM, MDPI, vol. 14(11), pages 1-15, November.
    17. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
    18. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
    19. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    20. Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:masjnl:v:10:y:2015:i:2:p:34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.