16-Neighborhood Non Linear- CA for Bone Cancer Prediction
Abstract
Malignant growth forecast is a troublesome issue to foresee in reality. The explanation and reason behind uncommon spread of this illness is astoundingly difficult to get it. We have various structures and sorts of tumors, a novel model to anticipate harm is irksome. Though various papers are open to pursue dangerous development, there is still space for propelling another methodology for envisioning sickness. We propose a novel 16-Neighborhood Non Linear-CA based familiar memory which gains from various logical examinations separating the data and predicts the Bone Cancer. We have taken datasets from ICCR Datasets and took care of them using Hybrid Unsupervised learning computation. Crucial work was done and we have differentiated our work and some standard existing composition. The proposed classifier execution was found promising.
How to cite this article:
Sree PK, UN Devi. 16-Neighborhood Non Linear-
CA for Bone Cancer Prediction. J Adv Res Comp
Tech Soft Appl 2019; 3(1): 22-24.
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