Open AccessTrust Evaluation Model Based on PSO and LSTM for Huge Information Environments
Author(s) -
Lin Zhang,
Yanwen Huang,
Jie Xuan,
Xiong Fu,
Qiaomin Lin,
Ruchuan Wang
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.12.005
Subject(s) - initialization , computer science , big data , particle swarm optimization , artificial intelligence , machine learning , artificial neural network , volume (thermodynamics) , recurrent neural network , data mining , physics , quantum mechanics , programming language
Due to the challenge of increasing data volume, the traditional trust model is unable to manage data with high efficiency and effectively extract useful information hidden in big data. To fully utilize big data and combine machine learning with trust evaluation, a trust evaluation model based on Long short‐term memory (LSTM) is presented. The powerful learning ability, expressive ability and dynamic timing of LSTM can be applied to study data while avoiding the vanishing and exploding gradient phenomena of traditional Recurrent neural networks (RNNs) to ensure that the model can learn sequences of random length and provide accurate trust evaluation. Targeting the performance instability caused by the LSTM model's random initialization of weights and thresholds, Particle swarm optimization (PSO), one of the intelligent algorithms, is introduced to find global optimal initial weights and thresholds. Experiments proved that the trust model proposed in this paper has high accuracy and contributes a new idea for trust evaluation in big data environments.