Artificial Intelligence-Based Classification of Trusted and Untrusted Sensor Nodes in WBAN Using Multi Layered Stacked Naive Bayes Method for Resilient Infrastructure


          

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：
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.



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