A novel advanced 3D‐IPS based on mmWaves and SOM‐MLP neural network

2021 ◽  
Author(s):  
Lotfi Tamazirt ◽  
Farid Alilat ◽  
Nazim Agoulmine
Keyword(s):  
Neural Network ◽  
Mlp Neural Network

scholarly journals Neural Network-based System for Automatic Passport Stamp Classification

2020 ◽  
Vol 49 (4) ◽  
pp. 583-607
Author(s):  
Wala Zaaboub ◽  
Lotfi Tlig ◽  
Mounir Sayadi ◽  
Basel Solaiman
Keyword(s):  
Neural Network ◽  
Automatic System ◽  
Texture Feature ◽  
Illegal Immigrants ◽  
International Tourism ◽  
Multi Layer Perceptron ◽  
Texture Feature Extraction ◽  
Mlp Neural Network ◽  
Artificial Network ◽  
Systematic Control

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

Fuzzy Systems ◽  
2017 ◽  
pp. 682-714 ◽  
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

2017 ◽  
pp. 293-325
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

Precise volume fraction measurement for three-phase flow meter using 137Cs gamma source and one detector

2020 ◽  
Vol 108 (2) ◽  
pp. 159-164
Author(s):  
S. Z. Islami rad ◽  
R. Gholipour Peyvandi
Keyword(s):  
Neural Network ◽  
Gamma Ray ◽  
Volume Fraction ◽  
Mlp Neural Network ◽  
Three Phase ◽  
Three Phase Flow ◽  
Gamma Ray Attenuation ◽  
Set Up ◽  
Fraction Measurement ◽  
Error Percentage

AbstractThe ability to precisely predict the volume fraction percentage of the different phases flowing in a pipe plays an important role in the oil, petroleum and other industries. In this research, the volume fraction percentage was measured precisely in water-gasoil-air three-phase flows by using a single pencil beam gamma ray attenuation technique and multilayer perceptron (MLP) neural network. The volume fraction percentage determination in three-phase flows requires least two gamma radioactive sources with different energies while in this study, we used just a 137Cs source (with the single energy of 662 keV) and a NaI detector. Also, in this work, the MLP neural network in MATLAB software was implemented to predict the volume fraction percentage. The experimental setup provides the required data for training and testing the network. Using this proposed method, the volume fraction was predicted in water-gasoil-air three-phase flows with mean relative error percentage less than 6.95 %. Also, the root mean square error was calculated 2.60. The set-up used is simpler than other proposed methods and cost, radiation safety and shielding requirements are minimized.

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