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Item 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, VijiThis 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.Item 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, VijiThis 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.Item NATURE-INSPIRED OPTIMIZED ARTIFICIAL BEE COLONY FOR DECISION MAKING IN ENERGY-EFFICIENT WIRELESS SENSOR NETWORKS (Book Chapter)(IGI Global, 2024-04-15) Gripsy J, Viji; Sasikala, MIn recent times, there has been a surge in the popularity of nature-inspired algorithms (NIAs) for addressing challenging and complex nonlinear difficult problems. Wireless sensor networks (WSNs) provide a wide range of concerns and challenges that must be addressed when formulating methodologies and algorithms aimed at conserving energy and enhancing the overall lifespan of the network. This chapter examines and analyzes optimization-based energy-efficient strategies for clustering and routing using optimization algorithms. The major challenges in WSNs are to provide low energy consumption, enhancing network lifetime, minimizing interference in communication, improving data rate, balancing network load and quality network functioning, etc. The chapter improves the quality of service of the network by lengthening network lifetime for more packet transmission. The proposed optimized artificial bee colony (OABC) outperformed in comparison to the existing algorithms in terms of less packet loss, higher network lifetime, minimum energy consumption, and lesser average execution time.