Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/5323
Title: A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET (Article)
Authors: Divya, T
Gripsy J, Viji
Keywords: Risk analysis
Optimization
Lung cance
Prediction
Wavelet neural network
Issue Date: 2024
Publisher: Intelligent Network and Systems Society
Abstract: This study proposes three contributions focused on lung cancer detection and severity level identification. The absence of non-invasive technologies for predicting lung cancer necessitates faster, more efficient, and more accurate classification procedures due to the absence of non-invasive technologies for predicting lung cancer. Creating an automated and intelligent prediction system is crucial for identifying phases and predicting the possibility of a recurrence. The objective is to create an automated detection system for identifying lung cancer using an optimizationfocused deep learning model. We develop an adaptive multi-swarm PSO and combine it with the firefly algorithm to determine the ideal weight values for the Wavelet Neural Network (WNN) model. We use the HAPSO-FFA WNN method to explore problems with multiple optimal solutions. This study evaluated two lung cancer datasets, and the proposed HAPSO-FFA-WNN model achieved 97.58% accuracy for dataset 1 and 98.54% accuracy for dataset 2. Furthermore, the proposed model achieved better precision, recall, and MCC performance metrics.
URI: https://inass.org/wp-content/uploads/2024/02/2024063062-2.pdf
ISSN: 2185310X
Appears in Collections:a) 2024-Scopus Open Access (pdf)



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.