Optimized Performance Validation of Biosensors with High Fault Tolerance

The deployment of biosensors is increasing with advancement of bio-electronics. Owing to challenging state of working of biosensors, the present applications of biosensors are capable of capturing only certain types of signals till date. This detection also depends on the type of the bio transducers used for signal generation and therefore, the signals generated from biosensors cannot be considered to be error free. This paper has reviewed some of the existing research contributions towards biosensor validation to find that there are no computational framework that efficiently performs validation as majority of the technique uses either clinical approach or experimental approach, which limits the validation of bio signal performance. Therefore, this paper presents a novel computational framework that uses enhanced version of auto-associative neural network and significantly optimizes the validation performance of biosensors as compared to other conventional optimization techniques.