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IJMLC 2012 Vol.2(6): 729-732 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.224

Convergence of LBS and AI

Sandeep Kumar, Mohammed Abdul Qadeer, and Sumit Kumar Singh

Abstract—Location Based Service (LBS) refers to class of applications that provides services based on the current or a known location. The location information can be obtained through the mobile communication network or the Global Navigation Satellite Systems (GNSS) [8]. But in all the cases after extracting information we have to make an exhaustive processing over this extracted geographic information. And this exhaustive processing requires a significant amount of time if it is performed by traditional techniques.
Artificial Intelligence, on the other hand, has been proven to be useful in the interpretation of spatial resource information. On of the artificial intelligence technique to support spatial processing is artificial neural networks (ANNs). The recent developments in ANN technology have made it more ofan applied mathematical technique that has some similarities to the human brain.
The integration of LBS with AIoffers a potential mechanismto lower the analysis-time of spatial information. One major advantage is that this integration allows the interpretive result from a small area to be transferred to a larger, geographically similar area.
The objective of this paper is to discuss about the relativity of LBS services and Artificial Intelligence technologies and to uncover the benefits of converging LBS and AI.

Index Terms—LBS, GIS, AI, ANNs.

S. Kumar is with the Tata Consultancy Services Limited, Pune India (email: sandeepkumar@zhcet.ac.in).
M. A. Qadeer is with the Aligarh Muslim University. Uttar Pradesh, Aligarh, India (e-mail: maqadeer@gmail.com).
S. K. Singh is with the Tata Consultancy Services Limited, Pune India (e-mail: sumitkumarsingh@zhcet.ac.in).


Cite:Sandeep Kumar, Mohammed Abdul Qadeer, and Sumit Kumar Singh, "Convergence of LBS and AI," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 729-732, 2012.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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