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首页> 外文期刊>Journal of ambient intelligence and humanized computing >A distributed key authentication and OKM-ANFIS scheme based breast cancer prediction system in the IoT environment
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A distributed key authentication and OKM-ANFIS scheme based breast cancer prediction system in the IoT environment

机译:基于分布的关键身份验证和OKM-ANFIS方案的IOT环境中的乳腺癌预测系统

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The Internet of Things (IoT) has significantly upgraded in medical and health care. This technology aids the patients as well as doctors for envisaging an assortment of diseases precisely and diagnoses these diseases as per the outcomes. However, the prevailing research methodologies encompass the issue of poor diagnostic accuracy in addition to safe data transfer betwixt IoT and cloud storage. This paper proposed a distributed key authentication in addition to OKM-ANFIS cantered breast cancer (BC) prediction system on the IoT environment to trounce such disadvantages also, the research used GA for the prediction of multi models. Initially, the authentication is performed by means of the patient. Then, the sensed values are attained as of the ' sensors that are placed inside the bra. Later, the DK-AES algorithm uploads the attained data safely to the hospital public cloud server (CS). Subsequently, the hospital management (HM) system downloads the data securely. The HM-system envisages BC in '2' phases: (1) pre-processing and (2) prediction. Utilizing removal redundancy, replacement of missing attributes, along with normalization, the data is pre-processed. Subsequently, the OKM-ANFIS classification algorithm predicts the disease. If any critical concerns arise, an alert text is sent by the HM to the patient's mobile. In an experimental assessment, the proposed work renders better outcomes than the prevailing methods.
机译:物联网(物联网)在医疗和医疗保健中显着升级。这项技术辅助患者以及医生,准确地设想各种各样的疾病,并根据结果诊断这些疾病。然而,除了IOT和云存储之间的安全数据传输外,普遍的研究方法包括诊断准确性差的问题。本文提出了一种分布式关键认证,除OKM-ANFIS患者乳腺癌(BC)预测系统上还有关于IOT环境的预测系统,也是如此缺点,该研究使用GA用于预测多模型。最初,通过患者执行认证。然后,被感测值作为放置在胸罩内的传感器。稍后,DK-AES算法安全地将达到的数据安全到医院公共云服务器(CS)。随后,医院管理(HM)系统安全下载数据。 HM-System设想在“2”阶段中的BC:(1)预处理和(2)预测。利用删除冗余,替换丢失属性以及归一化,数据被预先处理。随后,OKM-ANFIS分类算法预测疾病。如果出现任何关键问题,HM将由HM发送警报文本到患者的移动。在实验评估中,拟议的工作呈现出比主要方法更好的结果。

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