Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using Improved CNN
ABSTARCT :
The global healthcare industry and artificial intelligence has promoted the development of the diversified intelligent healthcare applications. IoT will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this paper, we first analyze the security performance, and derive the novel expressions. Then, to analyze the security performance in real time, an security performance intelligent prediction algorithm is proposed. An improved CNN model is designed, which combines four layer convolutions and a four-branch inception block. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.
EXISTING SYSTEM :
? The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data.
? In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings.
? Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data
? Main focus is determining the distance between a new unlabelled data point and the existing training datasets stored in the feature space.
? The study further compares existing literature, highlights their features and shortcomings, and discusses possible gaps in each approach in order to select appropriate algorithms for building an efficient prediction model.
DISADVANTAGE :
? The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks.
? However, the pooling layers may remove features that have an important impact on CNN prediction output.
? In order to avoid the loss of important information, an improved CNN without the pooling layer is designed in this paper.
? In this paper, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN).
PROPOSED SYSTEM :
• The proposed system was integrated with base station appliances to remotely regulate the pulse and temperature of patients as well as convey the patient’s data to the medical practitioner’s phone.
• Therefore, the proposed platform could monitor attributes of patients including temperature, glucose, respiratory, EEG (electroencephalogram), ECG (electrocardiogram), and BP (blood pressure), and relay them to a database via Wi-Fi or GPRS.
• The proposed review intends to offer an updated assessment of medical imaging ML algorithms and their application.
• The proposed review hopes to continue in this vein but add on an in-depth examination of the application of these ML algorithms in practical environments and the potential benefits.
ADVANTAGE :
? The OP performance can predict the interruption level, which is extremely important to ensure the IoT QoS.
? Based on IoT big data, artificial intelligence (AI) is an effective technology to predict the performance.
? The widely used methods for performance prediction include radial basis function (RBF), Elman, extreme learning machine (ELM), and generalized regression (GR) methods.
? A good CNN model can not only optimize the performance prediction, but also reduce the cost and time of model selection.
? Therefore, the mobile OP performance prediction is investigated in mobile IoT big data wireless transmission. The mobile OP performance is first analyzed by combining the decodeand-forward (DF) relaying and transmit antenna selection (TAS) schemes.
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