Energy-Efficient Cluster-Based Reliable Routing Using Hybrid Nutcracker and Improved Sand Cat Optimization Algorithm for Extending Network Lifetime in WSNs Joseph Martin Sahayaraj, Gopi Prabaharan, Loganathan Kartheesan, Natarajan Jayapandian International Journal of Communication Systems, 2025 In wireless sensor networks (WSNs), sensor nodes are deployed in a target region for sensing environmental physical parameters to attain the objective of reactive decision‐making. These sensor nodes necessitate energy for processing and forwarding the sensed data to the base station (BS) for better data delivery in WSNs. Balanced energy utilization in WSNs prevents the problem of hotspot, and dynamic cluster head (CH) selection with reliable route establishment is a vital decision‐making approach that helps in optimal path selection with maximized energy conservation. In this paper, a nutcracker and sand cat optimization algorithm (NCSCOA)–based multiobjective CH selection and sink node mobility scheme is propounded for enabling rapid and reliable data transmission with reduced energy consumption in heterogeneous WSNs. This NCSCOA handled the problem of hotspot as well as isolated nodes and facilitated loop‐free routing with the support of the improved nutcracker optimization algorithm (INCOA) that makes the decision of routing using local and global search optimization processes. It constructed an energy‐level matrix (ELM) by deriving the impactful factors of intercluster formation, distance between CH and BS, residual energy (RE), and node density for achieving optimal CH selection and route determination. In specific, improved sand cat optimization algorithm (ISCOA) is used during the intercluster formation phase by discovering the optimized path between source and destination during route establishment. Simulation‐based findings of the proposed NCSCOA confirmed its efficacy by improving the mean number of alive nodes by 23.18%, reducing energy consumption and delay by 21.86% and 20.98% compared to benchmarked protocols.
Lung disease classification in chest X-ray images using optimal cross stage partial bidirectional long short term memory T Babu, GV Sam Kumar, L Kartheesan, Surendran Rajendran Journal of X Ray Science and Technology, 2025 Background Lung disease is the crucial disease that affects the breathing conditions and even causes death. There are various approaches for the lung disease classification; still the inefficiency in accurate detection, computational complexity and over-fitting issues limits the performance of the model. To overcome the challenges, a deep learning model is proposed in this research. Initially, the input is acquired and is pre-processed using three various techniques like data augmentation, filtering and image re-sizing. Then, the threshold based segmentation is employed for obtaining the required region. Objective From the segmented image, various categories of lung diseases like COVID, lung Opacity, Pneumonia and normal are identified using the proposed Optimal Cross Stage Partial Bidirectional Long short term memory (OCBiNet). Methods The proposed OCBiNet is designed using Bidirectional Long short-term memory (BiNet) with Cross Stage Partial connection in its hidden state. Besides, the adjustable parameters are modified using the proposed Improved Mother Optimization (ImMO) algorithm. Results The ImMO algorithm is designed by integrating the Logistic Chaotic Mapping within the conventional Mother Optimization algorithm for enhancing the convergence rate in obtaining the global best solution. Conclusions The proposed OCBiNet is evaluated based on Accuracy, Recall, Precision, and F-Score and acquired the values of 99.11%, 98.98%, 99.18%, and 99.08% respectively.
Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals Muthukumar B, Kartheesan L, Vijayalakshmi Pasupathy, Surendran R 5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024 Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features.
Multi-Task Distillation Learning for Coffee Corticium Salmonicolor Pink Berry Disease for Real-Time Prediction Raveena S, Surendran R, Sangeetha M, Kartheesan L 5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024 Coffee Corticium Salmonicolor Pink Berry Disease (CCSPBD) is a substantial risk to coffee cultivation. Precise and prompt identification is essential for efficient disease treatment. This research introduces a multi-task distillation learning (MTDL) method for the real-time prediction of CCSPBD. A more extensive teacher model is trained on a comprehensive dataset of coffee plant diseases, and its knowledge is conveyed to a smaller student model tailored for CCSPBD prediction. Integrating supplementary activities about coffee plant health into the teacher model enhances the student model's comprehension of plant disease patterns. The resultant student model can precisely forecast CCSPBD in real-time, allowing farmers to implement prompt interventions to avert crop loss. Experimental findings indicate the efficacy of the suggested MTDL method in attaining high accuracy and efficiency for CCSPBD prediction.
