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Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems
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作者 Attiya Khan Muhammad Rizwan +3 位作者 ovidiu bagdasar Abdulatif Alabdulatif Sulaiman Alamro Abdullah Alnajim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第12期2121-2141,共21页
The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce tre... The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain. 展开更多
关键词 Anomaly detection deep learning Internet of Things(IoT) health care
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O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS
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作者 Anbalagan PRATAP Ramachandran RAJA +3 位作者 Jinde CAO Chuangxia HUANG Jehad ALZABUT ovidiu bagdasar 《Acta Mathematica Scientia》 SCIE CSCD 2022年第4期1273-1292,共20页
This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing ... This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results. 展开更多
关键词 O(t^(-β))-synchronization asymptotic synchronization BAM neural networks fractional order state feedback control law
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