Biochemical Sensing Technology Based on Deep Neural Network Algorithm

Abstract

As a new type of technology, biochemical sensing technology has received great attention from scientific researchers. It is mainly used to develop artificial intelligence sensing equipment. The use of deep neural network algorithms to assist research and development can improve the speed and accuracy of data processing. By constructing a deep neural network model, it is possible to improve the data fitting ability in biochemical sensing equipment and reduce the cost of research and development. This article investigates the current situation of users using biochemical sensing equipment, and the results of the investigation are as follows: Users who know about biochemical sensing equipment account for 51.01% of the total number of people, and users who have never heard of biochemical sensing equipment are only 6.57%; doctors and scientific researchers use more biochemical sensing equipment, accounting for 36% and 34% of the total, respectively. The least users are workers, indicating that the educational level has certain limitations on the popularization of new technologies; among the common biochemical sensing equipment, there are 65 users who know the food composition analyzer and 78 users who know the enzyme electrode sensor, indicating that the two major concerns of users are medical health and food safety.