Managing & Analyzing the Regression of Infected Cells By Means of Photonic Crystal Tweezers & Artificial Intelligence

Abstract

Abstract This paper is proposed for managing and analyzing the infected cells through photonic crystal tweezers along with the use of a regression models like support vector machine (SVM) and artificial intelligence (AI)-based K-nearest neighbor (KNN). Previously, photonic crystal tweezers were used to detect tumor cells and proved very effective in many types of tumor detection but it was not supposed to analyze these infected cells so with the help of regression model we can analyze them. Among the available AI techniques like K- nearest neighbor (KNN), Adoptive Neuro Fuzzy Inference System (ANFIS), Fuzzy KNN (FKNN), Support Vector Machine (SVM) and probabilistic neural network (PNN); SVM and KNN observed accuracy of 96% and 92% respectively while the sensitivity is importantly analyzed by these two techniques are 32,358 nm/RIU and 11,258 nm/RIU was observed to be 1.251 and 1.337 for tumor cells, respectively. Majorly the research is supposed to offer advantages for managing and for early detection of infected tumor cells by implication of tweezers with selected regression technique.