Implementation and Experimental Results of Neural Network and Genetic Algorithm based Spam Filtering Technique

Based on discussing the advantages of improving genetic algorithm and analyzing the defects of back propagation neural network, we presented the genetic neural model. The simulating data proved that the genetic neural model was able to realize parallel search and could get faster searching speed during random searching optimizaiton. The model was applied to predicting distribution of guava fruit fly. The experimental results show that the model can predict distribution of the fly which is consistent with the practical distribution. The model is very useful in practice. It is worthwhile to refer the model to predicting similar insects.

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SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology

The Scientific World JOURNAL ◽

10.1155/2014/414583 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Seongwook Youn

Keyword(s):

Experimental System ◽

Experimental Results ◽

The Internet ◽

Spam Filtering ◽

Spam Filter ◽

Spam Filters ◽

Filtering Technique ◽

Communication Methods ◽

Email Classification

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user’s background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

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Optimizing Ontology Alignments by Using Neural NSGA-II

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2018010103 ◽

2018 ◽

Vol 16 (1) ◽

pp. 29-42 ◽

Cited By ~ 5

Author(s):

Mohamed Biniz ◽

Rachid El Ayachi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Hybrid Approach ◽

Experimental Results ◽

Hybrid Technique ◽

Ontology Alignment ◽

Nsga Ii ◽

Ontology Alignment Evaluation Initiative ◽

Matching Techniques ◽

Three Phases

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

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Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method

Shock and Vibration ◽

10.1155/2018/2830686 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 29

Author(s):

Huiliang Cao ◽

Yingjie Zhang ◽

Chong Shen ◽

Yu Liu ◽

Xinwang Wang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Kalman Filter ◽

Random Walk ◽

Radial Basis Function ◽

Basis Function ◽

Experimental Results ◽

Noise Characteristic ◽

Output Data ◽

Bias Instability

This paper proposed three methods to compensate the temperature energy influence drift of the MEMS vibration gyroscope, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), and RBF NN based on GA with Kalman filter (KF). Three-axis MEMS vibration gyroscope (Gyro X, Gyro Y, and Gyro Z) output data are compensated and analyzed in this paper. The experimental results proved the correctness of these three methods, and MEMS vibration gyroscope temperature energy influence drift is compensated effectively. The results indicate that, after RBF NN-GA-KF method compensation, the bias instability of Gyros X, Y, and Z improves from 139°/h, 154°/h, and 178°/h to 2.9°/h, 3.9°/h, and 1.6°/h, respectively. And the angle random walk of Gyros X, Y, and Z was improved from 3.03°/h1/2, 4.55°/h1/2, and 5.89°/h1/2to 1.58°/h1/2, 2.58°/h1/2, and 0.71°/h1/2, respectively, and the drift trend and noise characteristic are optimized obviously.

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The preparation process of nanocomposite materials was optimized based on neural network and genetic algorithm

Functional materials ◽

10.15407/fm26.03.615 ◽

2019 ◽

Vol 26 (3) ◽

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Nanocomposite Materials ◽

Preparation Process

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Based on genetic algorithm to optimize learn vector quantization neural network is a new algorithm of distribution network fault line selection

Information Science and Management Engineering II (Set) ◽

10.2495/isme20140241 ◽

2014 ◽

Author(s):

Yusheng Zhou ◽

Pai Peng ◽

Yunlong Gao ◽

Zhengzhou An ◽

Rangjiao Liu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Vector Quantization ◽

Distribution Network ◽

Fault Line ◽

Fault Line Selection ◽

Line Selection ◽

Network Fault

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Shunt Control on Smart Structures Using Genetic Algorithm and Neural Network Method.

10.26678/abcm.diname2019.din2019-0137 ◽

2019 ◽

Author(s):

Venicio Silva Araujo ◽

Guilherme Silva Prado ◽

Heinsten Frederich Leal dos Santos

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Smart Structures ◽

Neural Network Method ◽

Network Method ◽

Shunt Control

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Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797613999200730211514 ◽

2020 ◽

Vol 13 ◽

Author(s):

Renqiang Wang ◽

Qinrong Li ◽

Shengze Miao ◽

Keyin Miao ◽

Hua Deng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Sliding Mode Control ◽

Intelligent Control ◽

Control Algorithm ◽

Sliding Mode ◽

Rbf Neural Network ◽

Input Saturation ◽

Ship Motion ◽

Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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