Conference Papers
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Item Cardiovascular Diseases Divination using Artificial Neural Network with Ensemble Models(Institute of Electrical and Electronics Engineers Inc., 2023) Pabitha, B.; Sanshi, S.; Karthik, N.Health is wealth, but nowadays, wealth is health, where humans keep running their day-to-day activities without caring about their health for various reasons. Every human being in this world suffers from one or other diseases. Recently, cardiovascular diseases like heart attacks are prevalent in all age groups. Addressing cardiovascular diseases is essential before the disease reaches a crucial stage. Nowadays, artificial intelligence algorithms have been used to detect diseases in their early stages. In this piece of writing, a model of an artificial neural network is utilised to analyze, detect and predict the likelihood of cardiovascular disease in the early stages. In this proposed work, feed forward propagation, forward the input data to learn and map the relationships between inputs and outputs, and backward propagation is used to reduce the errors in the data. Further, an ensemble learning stacked model is used to achieve high accuracy in the prediction of diseases. To verify the correctness of the model, ensemble learning to stack is executed with three different models, namely Model 1, Model 2, and Model 3, with varying sets of feature selections. The experiment results show an accuracy rate of 93% in their predictions. © 2023 IEEE.Item Performance Evaluation of Wireless Health and Remote Monitoring Network Throughput Under Varying Conditions Using NetSim(Institute of Electrical and Electronics Engineers Inc., 2024) Pabitha, B.; Vani, V.; Sanshi, S.The Wireless Body Area Network (WBAN), organized in/out of the human body region to form Wireless enabled Health and Remote monitoring Network (WHRN), is trending on the medical platform for efficient diagnosis by the physician without the patient's physical visit. This network is framed with different biological sensors in the regional area of the human body to sense unlike biological signals promptly. Wearable WHRN, like smart watches and mobile phones, can notify people about stress, heart rate, and other physiological nods. The technology developed enhances the treatment for the patient, but the security of the information transmitted over different mediums is vulnerable. WHRN is simulated using the NetSim standard tool. Network performance metrics and their plots are analyzed using various encryption standards to provide data transmission and diagnosis security. Security is the primary concern for physiological data sensed and transmitted over different mediums. © 2024 IEEE.Item Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques(Springer Science and Business Media Deutschland GmbH, 2024) Pabitha, B.; Vani, V.; Sanshi, S.; Karthik, N.In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable sensors, and on-body sensors. The doctor’s access the data recorded and monitored by the sensor embedded in the patient to treat critical situations immediately. Maintaining data integrity and guarding against threats is necessary to secure sensitive patient information. Several people have proposed schemes for authenticating data access through formal and informal verification. In this research work, we carry out multi-factor authentication extensively using zero-knowledge proofs. The anomaly detection of the sensors is detected using machine learning algorithms, which help tune the sensors to their correct working conditions. The work aims to concentrate on sensor working conditions promptly and to handle attacks like masquerade, forgery, and key escrow attacks. To assess whether performance metrics are superior in computing cost, storage overhead, and communication overhead, utilize the BAN logic tool. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.