An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO

With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a novel technique based on Artificial Neural Network-and Particle Swarm Optimization Algorithm (ANNPSO) for enabling a highly secured framework. The ANN-PSO method was created to predict health status from a database and its functions were selected from these data sets. The particle swarm optimization algorithm matches the ANN for better results by reducing errors. The results show the potential of the ANNPSO-based methodology for satisfactory health prediction results. This proposed approach will be tested using large medical data in a Hadoop environment. The proposed work will be carried out in the JAVA work phase.

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Scaling up the hybrid Particle Swarm Optimization algorithm for nominal data-sets

Intelligent Data Analysis ◽

10.3233/ida-150747 ◽

2015 ◽

Vol 19 (4) ◽

pp. 825-844 ◽

Cited By ~ 3

Author(s):

Hedayatollah Dallaki ◽

Kimia Bazargan Lari ◽

Ali Hamzeh ◽

Sattar Hashemi ◽

Ashkan Sami

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Scaling Up ◽

Data Sets ◽

Nominal Data ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

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Feature Selection Based on BP Neural Network and Adaptive Particle Swarm Algorithm

Mobile Information Systems ◽

10.1155/2021/6715564 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Bingsheng Chen ◽

Huijie Chen ◽

Mengshan Li

Keyword(s):

Neural Network ◽

Feature Selection ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Data Sets ◽

Swarm Optimization

Feature selection can classify the data with irrelevant features and improve the accuracy of data classification in pattern classification. At present, back propagation (BP) neural network and particle swarm optimization algorithm can be well combined with feature selection. On this basis, this paper adds interference factors to BP neural network and particle swarm optimization algorithm to improve the accuracy and practicability of feature selection. This paper summarizes the basic methods and requirements for feature selection and combines the benefits of global optimization with the feedback mechanism of BP neural networks to feature based on backpropagation and particle swarm optimization (BP-PSO). Firstly, a chaotic model is introduced to increase the diversity of particles in the initial process of particle swarm optimization, and an adaptive factor is introduced to enhance the global search ability of the algorithm. Then, the number of features is optimized to reduce the number of features on the basis of ensuring the accuracy of feature selection. Finally, different data sets are introduced to test the accuracy of feature selection, and the evaluation mechanisms of encapsulation mode and filtering mode are used to verify the practicability of the model. The results show that the average accuracy of BP-PSO is 8.65% higher than the suboptimal NDFs model in different data sets, and the performance of BP-PSO is 2.31% to 18.62% higher than the benchmark method in all data sets. It shows that BP-PSO can select more distinguishing feature subsets, which verifies the accuracy and practicability of this model.

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