Performance Analysis of Secure Patient Data in Cloud IOT using Data Mining Techniques
Implementation Plan:
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Step 1: Initially, We Collect and Load the IOT Healthcare Security Dataset for Data Collection Process.
Step 2: Next, We preprocess the Loaded data using Synthetic Minority Oversampling Technique (SMOTE)
Step 3: Next, We perform the Feature extraction process using Principal Component Analysis (PCA) Method.
Step 4: Next, We perform classification process using Generative Adversarial Networks(GAN) with Adaptive Moment Estimation optimization algorithm.
Step 5: Next, We Transmit the data to the Cloud environment to reduce storage issues and guarantee immediate access to vital information.
Step 5.1: This process starts with the encryption and decryption of data using Homomorphic encryption with the Laplacian technique.
Step 5.2: Next, We Perform the Routing process using Leach protocol to optimize energy consumption and communication efficiency.
Step 5.3: Next, we store the data in the Cloud Architecture server.
Step 6: Finally, The proposed work is verified using performance metrics such as
6.1:Number of Epochs vs. accuracy (%)
6.2:Number of Epochs vs. Precision (%)
6.3: Number of Users vs. Authentication Time(s)
6.4: Number of Users vs. Throughput (%)
6.5: Number of User vs. Packet Delivery Ratio (%)
[The process based on your requirement]
Software Requirement:
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1. Development Tool: Python – 3.11.4 or Above version
2. Operating System: Windows 10 (64-bit)
Dataset Link:
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https://www.kaggle.com/datasets/faisalmalik/iot-healthcare-security-dataset
Note:
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1) Please note that this implementation plan does not include any further steps after it is put into implementation.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) This project is only based on simulations. Not a real time project.
4) If the above plan satisfies your requirement please confirm with us.
We perform the EXISTING process based on the Reference 1: Title: IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning
