scholarly journals OPTIMIZATION OF ARTIFICIAL NEURAL NETWORKS USING ANT COLONY OPTIMIZATION TO IDENTIFY SIGNATURE IMAGES

Transmisi ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 128-134
Author(s):  
Junia Kurniati
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Extraction Process ◽  
Ant Colony ◽  
Gabor Wavelet ◽  
The Neural Network ◽  
Network Method ◽  
Method Optimization ◽  
Recognition Systems ◽  
Physical Features

Biometrics can be used in identification or recognition systems because it is a method for recognizing humans based on one or more physical features or unique behaviors, one of which is the Signature. In its application, signatures need to be examined (identification) because signatures are often imitated or falsified for various purposes. The signature that will be identified will be taken first by its characteristics by performing the feature extraction process using the Gabor Wavelet Transform (TGW) method. After extracting, the signature identification process is carried out using the Neural Network. In the process of applying the Neural Network method, optimization will be performed using Ant Colony Optimization. The results showed that the identification of signatures made using the Neural Network alone produced an accuracy of 77%, but after optimization using Ant Colony Optimization increased to 83%.

ANN Optimization Experiments for Classification

2014 ◽  
pp. 200-212
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
The Other ◽  
The Neural Network

This chapter shows the application of PSO and GA algorithms for training the neural network using two datasets: XOR and Iris. Then the performance of both algorithms are compared and presented by figures. In addition, some of the other optimization algorithms such as Gravitation Search Algorithm (GSA) and Ant Colony Optimization (ACO) are explained.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

scholarly journals Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

2019 ◽  
Vol 8 (3S3) ◽  
pp. 280-290
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Histogram Equalization ◽  
Extraction Process ◽  
Input Image ◽  
Suggested Approach ◽  
Biometric Systems ◽  
Recognition Systems ◽  
The Right ◽  
Facial Identification

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

scholarly journals Comparative analysis of the geometrized histogram method and the neural network method for road markings recognition

2021 ◽  
pp. 1-22
Author(s):  
Aleksei Valerievich Podoprosvetov ◽  
Dmitry Anatolevich Anokhin ◽  
Konstantin Ivanovich Kiy ◽  
Igor Aleksandrovich Orlov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Video Sequences ◽  
Histogram Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Accuracy Speed

This paper compares two approaches to determining road markings from video sequences, namely, the method of finding the markings using geometrized histograms and the method based on neural networks. An independent open dataset TuSimple is used to conduct a comparative analysis of the algorithms. Since the investigated methods have different architectures, their work is evaluated according to the following metrics: Accuracy, speed (relative FPS), general computational complexity of the algorithm (TFlops).

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