Recently, a wide variety of real-world problems are solved via cloud computing, deep learning, machine learning, artificial intelligence, and the Internet of Things (IoT). These methodologies are concerned with different areas like smart cities, agriculture, transportation systems, and healthcare systems. The existing researchers focused on the health care monitoring application along with IoT and cloud computing. It met several shortcomings in case of computational complexity, cost, time, and improper health care data storage and so forth. To overcome these challenges, the novel IoT-enabled secure healthcare monitoring model is proposed in this study. At first, several sensors are deployed in the human body thereby collecting patients' data with respect to vital parameters like body temperature deviation. The patient's health record dataset consists of 10 attributes namely phone numbers, marital status, address, age, name, heartbeat rate, oxygen level, smoking, temperature, and blood pressure. The data size reduction and normalization are performed in the IoT medical sensor dataset which contains the redundant or irrelevant attributes that are eliminated during pre-processing. The artificial hummingbird (AHB) algorithm-based convolutional neural network (AHB-CNN) model performs both feature extraction and classification of cancer disease. The AHB-CNN model classifies whether the patient is prone to cancer or not based on the sensor input collected. The received results are then sent to the hospital management for analysis. The Rivest-Shamir-Adleman (RSA) encryption method is mostly used in this study due to the major benefits it gives in terms of asymmetric encryption, ease of use, simpler deployment, and high security associated with factoring large prime numbers. A modified RSA algorithm is used in this article which uses the double encryption-decryption process and “n” prime numbers to enhance the security of the conventional RSA algorithm. The data is always encrypted during transit before being stored in the cloud or for further processing. Depending upon the experimental consequences, the proposed method established superior performances compared to other state-of-art techniques.

中文翻译：

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一种基于安全物联网的医疗保健框架，使用基于人工蜂鸟的 CNN 修改后的 RSA 算法

最近，通过云计算、深度学习、机器学习、人工智能和物联网 (IoT) 解决了各种各样的现实问题。这些方法论涉及不同领域，如智慧城市、农业、交通系统和医疗保健系统。现有的研究人员专注于医疗保健监控应用以及物联网和云计算。它在计算复杂性、成本、时间和医疗保健数据存储不当等方面遇到了一些缺点。为了克服这些挑战，本研究提出了一种新型的支持物联网的安全医疗保健监测模型。首先，在人体中部署多个传感器，从而收集患者关于体温偏差等重要参数的数据。病人' 健康记录数据集包含 10 个属性，即电话号码、婚姻状况、地址、年龄、姓名、心跳率、氧气水平、吸烟情况、体温和血压。数据大小缩减和规范化在物联网医疗传感器数据集中执行，其中包含在预处理过程中消除的冗余或不相关属性。基于人工蜂鸟 (AHB) 算法的卷积神经网络 (AHB-CNN) 模型执行癌症疾病的特征提取和分类。AHB-CNN 模型根据收集到的传感器输入对患者是否易患癌症进行分类。然后将收到的结果发送给医院管理层进行分析。Rivest-Shamir-Adleman (RSA) 加密方法主要用于本研究，因为它在非对称加密、易用性、更简单的部署以及与分解大质数相关的高安全性方面具有主要优势。本文采用一种改进的RSA算法，采用双重加解密过程和“n”个素数，增强了传统RSA算法的安全性。在将数据存储在云中或进行进一步处理之前，数据在传输过程中始终被加密。根据实验结果，与其他最先进的技术相比，所提出的方法建立了卓越的性能。本文采用一种改进的RSA算法，采用双重加解密过程和“n”个素数，增强了传统RSA算法的安全性。在将数据存储在云中或进行进一步处理之前，数据在传输过程中始终被加密。根据实验结果，与其他最先进的技术相比，所提出的方法建立了卓越的性能。本文采用一种改进的RSA算法，采用双重加解密过程和“n”个素数，增强了传统RSA算法的安全性。在将数据存储在云中或进行进一步处理之前，数据在传输过程中始终被加密。根据实验结果，与其他最先进的技术相比，所提出的方法建立了卓越的性能。