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Artificial Intelligence-Based Classification of Trusted and Untrusted Sensor Nodes in WBAN Using Multi Layered Stacked Naive Bayes Method for Resilient Infrastructure
参考中译:基于人工智能的WBAN中可信和不可信传感器节点分类，使用多层堆叠朴素Bayes方法用于弹性基础设施

刊名：	Revue d'Intelligence Artificielle
作者：	Seba Aziz Sahy（Middle Technical university, Institute of Medical Technology Al-Mansour） Rajaa Salih Mohammed Hasan（Middle Technical university, Institute of Medical Technology Al-Mansour） Hala Shaker Mehdy（College of Education, Computer Science, Al-Mustansiriya University） Israa Ibraheem Al_Barazanchi（Department of Communication Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University） Jamal Fadhil Tawfeq（Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University） Ravi Sekhar（Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU)） Pritesh Shah（Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU)）
刊号：	737F0004
ISSN：	0992-499X
出版年：	2024
年卷期：	2024, vol.38, no.2
页码：	515-521
总页数：	7
分类号：	TP3
关键词：	WBANs；Trusted/Untrusted nodes；Multi_layered_Stacked_Nave_Bayes (MLSNB)；Independent component analytic (ICA)；Bi_Objective_Genetic algorithms (BGA)
参考中译：	WBAN;受信任/不受信任的节点; Multi_layered_Nave_Bayes（MLSNB）;独立分量分析（ICA）; Bi_Observer_Genetic算法（LGA）
语种：	eng
文摘：	WBAN Magnetic Sensor Nodes can be classified based on artificial intelligence using the Multi-Layered Stacked Naive Bayes Method for Resilient Infrastructure. As wireless body area networks (WBANs) hold considerable potential for monitoring, identifying, forecasting, and diagnosing disease in humans, this study is significant for the healthcare industry. WBAN data can be inaccurate and unreliable when collected by untrusted sensor nodes, leading to inaccurate diagnoses and treatments. WBAN networks can be improved by identifying untrusted sensor nodes in this study to address this issue. Sensor nodes are categorized using the MLSNB method based on their trust aspects. When compared to other methods currently in use, MLSNB performs better. It is possible, using the proposed methodology, to introduce high-quality, affordable, and easily accessible healthcare systems to the world's growing population, in particular to the elderly and persons living with old-age diseases.
参考中译：	WBAN磁传感器节点可以使用弹性基础设施的多层堆叠朴素Bayes方法基于人工智能进行分类。由于无线体域网（WBAN）在监测、识别、预测和诊断人类疾病方面具有相当大的潜力，因此这项研究对于医疗保健行业具有重要意义。WBAN数据由不受信任的传感器节点收集时可能不准确且不可靠，导致诊断和治疗不准确。在本研究中，可以通过识别不受信任的传感器节点来改进WBAN网络以解决这个问题。传感器节点根据其信任方面使用MLSNB方法进行分类。与目前使用的其他方法相比，MLSNB的表现更好。使用所提出的方法，有可能为世界上不断增长的人口，特别是老年人和老年病患者引入高质量、负担得起且易于获得的医疗保健系统。