By M. Khoshnevisan, S. Bhattacharya, F. Smarandache
The aim of this booklet is to use the synthetic Intelligence and keep an eye on structures to various actual types. it's been designed for graduate scholars and researchers who're energetic within the functions of man-made Intelligence and keep an eye on structures in modeling. In our destiny learn, we are going to deal with the original points of Neutrosophic common sense in modeling and information research.
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Et al. ”, Journal of American Medical Association, 1985, pp1420-1426 63 Rubinow, S. , “A maturity-time representation for cell populations”, Biophysics Journal, 1968, pp1055-1073 Tan, Clarence N. , 2001, pp25-42 Tucker, Susan L. , Brown, Barry W. , vol. 83, pp181-210 Webb, G. , “A model of proliferating cell populations with inherited cycle length”, Journal of Mathematical Biology, 1986, pp269-282 Zaguskin, S.
E. 60199. 636. 865). e. 33786. 068. 691). e. 23722. These defining ranges as evaluated above will however depend on the parameters of the utility function and will therefore be different for different investors according to the values assigned to his or her utility indices corresponding to the expected excess equity. 44 In general, if we have a parabolic utility function u (x) = a + bx – cx2, where c > 0 ensures concavity, then we have u’ (x) = b – 2cx and u’’ (x) = -2c. The Arrow-Pratt measure is given by λ (x) = 2c /(b–2cx).
The inputs and output of Phase I is proposed to be fed as input to Phase II along with additional clinical data on maximum tumor mass. The output and inputs of Phase 2 is finally to be fed into Phase III to generate the model output – a binary variable M|B that takes value of 1 if the tumor is predicted to metastasize or 0 otherwise. The recursive structure of the model is intended to pick up any underlying chaotic attractor that might be at work at the point where benign hyperplasia starts to degenerate into cancer.
Artificial Intelligence and Responsive Optimization by M. Khoshnevisan, S. Bhattacharya, F. Smarandache