MULTI-LAYER ARTIFICIAL NEURAL NETWORK FOR ESTIMATING REAL-ESTATE PRICES
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Abstract
The estimation of land costs, are a helpful and sensible methodology for purchasers and for neighborhood and monetary specialists. It is of most extreme significance to assess the present status of the market and anticipate its presentation over the present moment so as to settle on suitable money related choices. We will utilize two propelled displaying approaches Multi-Level Models and Artificial Neural Networks to demonstrate house costs. This methodology is contrasted and the standard Hedonic Price Model as far as exactness in expectation, gathering the area data and their logical (understanding) power. This undertaking presents the advancement of a multi-layer fake neural system based models to help land financial specialists and home designers in this basic assignment. (Abstract)
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References
L. Huan and M. Hiroshi, Handwritten English Character Recognition Using Neural Networkâ€, International Journal of Computer Science & Communication.
Anita Pal & Dayashankar Singh, “ Handwritten English Character Recognition Using Neural Networkâ€, International Journal of Computer Science & Communication, Vol. 1, No.2, July-December 2010, pp. 141- 144.
Attaullah Khawaja, Shen Tingzhi, Noor Mohammad Memon, AltafRajpa, “Recognition of printed Chinese characters by using Neural Networkâ€, IEEE, 2006, pp 169-172
I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,†in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.
Keechul Jung, KwangIn Kim and Anil K. Jain, “Text information extraction in images and video: a surveyâ€, The journal of the Pattern Recognition society, 2004
R. Nicole, The ANN (CFBP) They can work fine in case of Requires high processing Cited By: 4 Network. CF incomplete time for la artificial information. Advantages / intelligence model
Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,†IEEE
Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].