An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Ajay Indian ◽  
Karamjit Bhatia
Keyword(s):  
Genetic Algorithm ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Zernike Moments ◽  
Classification Model ◽  
Computational Time ◽  
The Moment ◽  
Moment Features ◽  
Two Parameters

A technique to recognize off-line handwritten Hindi character is suggested by employing Zernike complex moments like a tool to describe the characteristics of a character. Further, an algorithm for selecting the features is employed to identify the substantial image moments from the extracted moments, as the extracted moments may have some insignificant ones. Insignificant moments can increase the computational time and can also degrade the classification accuracy. Thus, the objectives of the study are twofold: (1) to find the important Zernike moments by employing the Genetic algorithm (GA) and (2) the classification of each character is performed using neural network. This way, the performance of the proposed technique is evaluated on two parameters (i.e., speed and recognition accuracy). Further, the efficacy of GA for selecting the moment features is assessed, and the efficacy of selected Zernike complex moments using GA is analyzed for handwritten Hindi characters. Here, the authors used a resilient backpropagation learning algorithm (RPROP) as a classification model.

Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2051004
Author(s):  
Jian Liu ◽  
Yuchen Zheng ◽  
Ke Dong ◽  
Haitong Yu ◽  
Jianjun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Recommendation System ◽  
Computation Time ◽  
Training Data ◽  
Computational Time ◽  
Time Problem

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

scholarly journals Orthogonal Moment Extraction and Classification of Melanoma Images

2018 ◽  
Author(s):  
Sudhakar Singh ◽  
Shabana Urooj
Keyword(s):  
Support Vector Machine ◽  
Zernike Moments ◽  
Support Vector ◽  
Moment Invariants ◽  
Swarm Optimization ◽  
Orthogonal Moments ◽  
Equal Interval ◽  
The Moment ◽  
Phase Angles

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

scholarly journals A novel sentiment classification model based on online learning

2019 ◽  
Vol 13 ◽  
pp. 174830261984576
Author(s):  
Ningjia Qiu ◽  
Zhuorui Shen ◽  
Xiaojuan Hu ◽  
Peng Wang
Keyword(s):  
Experimental Data ◽  
Online Learning ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Sentiment Classification ◽  
Classification Model ◽  
Model Based ◽  
Adaptive Adjustment ◽  
Online Learning Algorithm

Memory limitation and slow training speed are two important problems in sentiment analysis. In this paper, we propose a sentiment classification model based on online learning to improve the training speed of the sentiment classification. First, combining the adaptive adjustment of learning rate of the Adadelta algorithm and the characteristics of avoid frequent jitter of Adam algorithm in the later stage of training, we present a novel Adamdelta algorithm. It solves the problem that learning rate of traditional follow the regularized leader (FTRL)-Proximal online learning algorithm will disappear with the increase of training times. Moreover, we gain an optimized logistic regression (LR) model and use it to the sentiment classification of online learning. Finally, we compare the proposed algorithm with five similar models with the experimental data of the IMDb movie review dataset. Experimental results show that the improved algorithm has better classification effect and can effectively improve the precision and recall of the classifier.

scholarly journals Research of the Classification Model Based on Dominance Rough Set Approach for China Emergency Communication

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fan Zifu ◽  
Sun Hong ◽  
Wang Lihua
Keyword(s):  
Emergency Response ◽  
Rough Set ◽  
Communication System ◽  
Recognition Accuracy ◽  
Decision Rules ◽  
Classification Model ◽  
Emergency Communication ◽  
Expert Interview ◽  
Classification Index

Ensuring smooth communication and recovering damaged communication system quickly and efficiently are the key to the entire emergency response, command, control, and rescue during the whole accident. The classification of emergency communication level is the premise of emergency communication guarantee. So, we use dominance rough set approach (DRSA) to construct the classification model for the judgment of emergency communication in this paper. In this model, we propose a classification index system of emergency communication using the method of expert interview firstly and then use DRSA to complete data sample, reduct attribute, and extract the preference decision rules of the emergency communication classification. Finally, the recognition accuracy of this model is verified; the testing result proves the model proposed in this paper is valid.

A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy

2021 ◽  
Author(s):  
Mingyin Yao ◽  
Gangrong Fu ◽  
Tianbing Chen ◽  
Muhua Liu ◽  
Jiang Xu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Recognition Accuracy ◽  
Adaptive Mutation ◽  
Tea Leaves ◽  
Breakdown Spectroscopy ◽  
Modified Genetic Algorithm ◽  
Sample Classification ◽  
Svm Model ◽  
Laser Induced Breakdown

This work provides a modified adaptive mutation probability selection genetic algorithm to optimize the SVM model, which improved the accuracy of tea sample classification by LIBS and its recognition accuracy was higher than CV-SVM and PSO-SVM.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Improving management as a way to increase labour productivity

Upravlenie ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 24-30
Author(s):  
A. O. Ivanov
Keyword(s):  
Productivity Growth ◽  
Labour Productivity ◽  
Production Costs ◽  
Managerial Practices ◽  
Industrial Enterprises ◽  
Labour Productivity Growth ◽  
Period 2 ◽  
Main Factors ◽  
The Moment

The article gives an overview, performs analysis and classification of successful managerial practices applied at Russian industrial enterprises in the framework of the national project “Labour productivity and employment support”. The paper emphasizes the main factors of labour productivity growth as follows: investment policy, growth of human capital, and efficient use of managerial capital of enterprise. In order to determine the need of enterprises to increase labour productivity, the author proposes four universal criteria that signal the existing inefficiency even before the loss of competitiveness: 1) the dynamics of labour productivity in the company is not positive during a given period; 2) the company is behind competitors by labour productivity indicator; 3) the company is behind competitors by labour productivity growth rates indicator for a certain period; 4) unit production costs rise. These criteria allow you to take into account the situation both within the enterprise and in comparison with other enterprises. Each criteria can be considered separately or in combination with the others, applied to enterprises of different industries, specialization, and scale. Criteria indicate the direction of development in which the company is experiencing difficulties at the moment, or may experience them in the future.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

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