Design control and management of intelligent and autonomous nanorobots with artificial intelligence for Prevention and monitoring of blood related diseases.

The effective management of human bloodstream remains to be the prime focus for the clinicians over years and it impose greater challenges when it comes to real-time solution. In particular managing hypoxemia and detection of blood clots is most pertinent. One major challenge faced is the existence of limited training data generated from real-world scenarios. On the other hand, creating an efficient model is often time consuming and expensive. This paper focusses on effective convergence of artificial intelligence and nanorobotics technologies to design and implement autonomous intelligent nanorobots to deal with blood related diseases. The major contribution of the research is two-fold, first we define an efficient architecture of the nanorobotics system with appropriate design parameter. Next, we develop a novel semi-supervised learning model using stochastic gradient descent method and kernel space that efficiently control and manage the nanorobots and helps in earlier prognosis and treatment of blood related diseases. The proposed model is novel and efficient as it enables working at nanoscale, providing resourceful understanding on physical and chemical properties associated with human body. The use of artificial intelligence techniques further makes the system to work more intelligently and independently. COSMOL with integrated MATLAB environment is used for experimental setup and simulation. MNIST dataset is compared with online RP tree method and other conventional batch related techniques. The performance analysis is compared based on performance, error rates and risk related factors. The proposed approach provides significant improvement in terms of performance with minimal error rate and improved accuracy measures. [ABSTRACT FROM AUTHOR]