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Author: search.filters.author.Divya, T

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    A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET (Article)
    (Intelligent Network and Systems Society, 2024) Divya, T; Gripsy J, Viji
    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.
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    A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET
    (Intelligent Network and Systems Society, 2024) Divya, T; Gripsy J, Viji
    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.
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    AN INTEGRATED DEEP LEARNING BASED ENHANCED GREY WOLF OPTIMIZATION FOR LUNG CANCER PREDICTION
    (Little Lion Scientific, 2024-03-31) Divya, T; Viji Gripsy, J
    Lung cancer is an extremely harmful disease that represents the leading cause of death among both males and females within the nation. The survival spans for lung cancer patients within the 10%-20% range are limited to a duration of five years. Nevertheless, in the event that lung cancer is identified in its early stages and promptly treated, there is potential for a reduction in death rates. When lung cancer is identified at an early stage during the screening procedure, the clinical response to treatment may exhibit variability and provide very favourable outcomes. The implementation of a dependable and automated system might greatly facilitate the early identification of lung cancer, even in remote regions. This research presents a unique technique called Integrated Deep Learning-based Enhanced Grey Wolf Optimization for lung cancer prediction (IDL-EGWO). In order to address the issue of instability and convergence accuracy that occurs when using the Grey Wolf Optimizer (GWO) as a meta-heuristic algorithm with a robust capacity for optimum search, A weighted average GWO algorithm is suggested as a way to try to fix the problems with the GWO, such as the fact that it can get stuck in local optima and has a slow convergence rate in later stages. This technique incorporates an Artificial Neural Network (ANN) during the training phase. The research included a range of performance criteria, including precision, recall, f-measure, accuracy, execution time, and root mean squared error. According to the experiment, the IDL-EGWO algorithm demonstrated a higher accuracy rate of 97% compared to the previous methods. © Little Lion Scientific.
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    LUNG CANCER DISEASE PREDICTION AND CLASSIFICATION BASED ON FEATURE SELECTION METHOD USING BAYESIAN NETWORK, LOGISTIC REGRESSION, J48, RANDOM FOREST, AND NAÏVE BAYES ALGORITHMS
    (IEEE Xplore, 2023-05-26) Viji Cripsy, J; Divya, T
    People who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90 % ), followed by Naive Bayes (90.29 % ), Bayesian Network (88.34 % ), j48 (86.08 % ) and Random Forest (90.93 % ).