Intruder Detection System using IoT with Adaptive Face Monitoring and Motion Sensing Algorithm

Jojo, Aswin; Sunil, Gokul K.; Quadir, Abdul; Vigneswaran, T.; Punitha, K; Sivaraman, Arun Kumar

doi:10.1109/icicict54557.2022.9917831

Cited by 5 publications

(1 citation statement)

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“…Effective analysis was performed by utilizing population size as 10 and maximum iteration count as 25. Different classifiers utilized for simulation were Neural Network (NN) [ 31 ], K-Nearest Neighbors (KNN) [ 32 ], Long Short-Term Memory (LSTM) [ 33 ], BiLSTM [ 34 ], and Tunicate Swarm-Sail Fish Optimization-Recurrent Neural Network (TS-SFO-RNN) [ 35 ] and also heuristic approaches like Grey Wolf Optimization (GWO) [ 36 ], Dragonfly Algorithm (DA) [ 37 ], DOX [ 38 ], Sail Fish Optimization-Tunicate Swarm Algorithm (SFO-TSA) [ 39 ] and TS-SFO [ 40 , 41 ].…”

Section: Results Calculationsmentioning

confidence: 99%

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Nithiyanantham¹,

Sivaraman²

2023

Biomedicines

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Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient’s health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

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