Record Details

Aspergillus niger Fungus Detection using Transfer Learning Technique and Modified Backpropagation Algorithm with Inertia and Legendre Polynomial

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Aspergillus niger Fungus Detection using Transfer Learning Technique and Modified Backpropagation Algorithm with Inertia and Legendre Polynomial
 
Creator Vanitha, V
Sornam, M
 
Subject Confusion matrix
Fungus classification
Orthogonal polynomial
Pre-trained deep learning models
 
Description 622-632
Looking at the loss due to health problems from fungal diseases in one hand and the benefits from its
industrial/agricultural use, rapid automated fungal species identification is the need of the hour. Hence, proposed a fast
identification of fungal species by a 15 minutes staining procedure followed by an artificial-intelligence-based image
classification technique. In this modern era, deep architectures have shown a significant performance on computer vision
problems. Instead of developing a new model from scratch, the pre-trained convolutional neural network models are
available to obtain the appropriate features from input samples using the transfer-learning technique. This work utilizes the
transfer-learning approach for feature extraction and classification performed using the proposed modified third-term
Backpropagation (BP) algorithm. This proposed algorithm contains Inertia as a third factor in the weight updation rule
expanded in the form of the Legendre polynomial to overcome the limitations of the traditional Backpropagation algorithm.
The effectiveness of the proposed classifier compared to the results of the existing cutting-edge algorithms namely,
Backpropagation algorithm, Backpropagation algorithm using Momentum, and softmax classifier. Compare to the existing
models, the proposed model scored a high testing accuracy of 97.27%.
 
Date 2022-06-06T06:15:48Z
2022-06-06T06:15:48Z
2022-06
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/59856
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(06) [June 2022]