TY - EJOU AU - Merin, D. Devina AU - Jagatheeswari, P. TI - Detection of Microbial Activity in Silver Nanoparticles Using Modified Convolution Network T2 - Intelligent Automation \& Soft Computing PY - 2022 VL - 33 IS - 3 SN - 2326-005X AB - The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the networking system and the DL classification model Deep convolution neural network (DCNN) with modified activation function Leaky ReLy is designed particularly to classify the antibacterial activity of AgNP in various concentrations. DCNN analyses the absorption maxima AgNP and categorizes the activity of microbes. The experimental analysis of the proposed method shows that the AgNP shows the maximum absorption value of 0.56 at 450 nm. The overall technique yields an accuracy of 94% through the DCNN technique. The methodology used here to develop silver particles at the nanoscale is easy and economic. KW - Networking; green extract; silver nanoparticles; adaptive histogram equalization; deep convolution neural network; anti-bacterial activity DO - 10.32604/iasc.2022.024495