Quantum Generators: Kernel Regression Model with Machine Learning from the Structured Compute Units

EasyChair Preprint 5070

Abstract

Quantum Generators is a means of achieving mass food production with short production cycles, and when and where required by means of machines rather than land based farming which has serious limitations. The process for agricultural practices for plant growth in different stages is simulated in a machine with a capacity to produce multiple seeds from one seed input using computational models of multiplication (generating multiple copies of kernel in repetition). In this paper, we present a Kernel Regression Model with Machine Learning for the structured Compute Units resulted by the computational models of multiplication and also train a Kernel Classifier to see if we get better results of reconstruction so that they can be linked to tissues of the kernel which mimic the real cell structure that grows into full-fledged natural tissue. We use simulation to show that we achieve good accuracy with respect to the size of the input space and it is an improvement compared to the logistic regression. The results suggest that it is possible to achieve suitable cell structure for quantum generation.

Keyphrases: 3D bioprinting, Compute Units, Machine learning/Artificial Intelligence, Quantum Generators

@booklet{EasyChair:5070,
  author    = {Poondru Prithvinath Reddy},
  title     = {Quantum Generators: Kernel Regression Model with Machine Learning from the Structured Compute Units},
  howpublished = {EasyChair Preprint 5070},
  year      = {EasyChair, 2021}}