scholarly journals Font Recognition of English Letters Based on Distance Profile Features

2020 ◽  
Vol 8 (2) ◽  
pp. 66-71
Author(s):  
Aveen J. Mohammed ◽  
Hasan S.M. Al-Khaffaf
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Final Phase ◽  
Rbf Kernel ◽  
Font Recognition

This paper presents a system for recognizing English fonts from character images. The distance profile is the feature of choice used in this paper. The system extracts a vector of 106 features and feeds it into a support vector machine (SVM) classifier with a radial basis function (RBF) kernel. The experiment is divided into three phases. In the first phase, the system trains the SVM with different Gamma and C parameters. In the second phase, the validation phase, we validate and select the pair of Gamma and C values that yield the best recognition rates. In the final phase, the testing phase, the images are tested and the recognition rate is reported. Experimental results based on 27,620 characters glyph images from three English fonts show a 94.82% overall recognition rate.

scholarly journals Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

2021 ◽  
Vol 7 (2) ◽  
pp. 119
Author(s):  
Belindha Ayu Ardhani ◽  
Nur Chamidah ◽  
Toha Saifudin
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Radial Basis Function ◽  
Sentiment Analysis ◽  
Basis Function ◽  
Descriptive Analysis ◽  
Support Vector ◽  
The Future ◽  
Radial Basis ◽  
Rbf Kernel

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function 

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

Interpreting Brain Waves

2017 ◽  
pp. 695-714
Author(s):  
Noran Magdy El-Kafrawy ◽  
Doaa Hegazy ◽  
Mohamed F. Tolba
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Basis Function ◽  
Brain Computer Interface ◽  
Support Vector ◽  
Left Hand ◽  
Brain Waves ◽  
Mode Decomposition ◽  
Rbf Kernel ◽  
The Brain

BCI (Brain-Computer Interface) gives you the power to manipulate things around you just by thinking of what you want to do. It allows your thoughts to be interpreted by the computer and hence act upon it. This could be utilized in helping disabled people, remote controlling of robots or even getting personalized systems depending upon your mood. The most important part of any BCI application is interpreting the brain signalsasthere are many mental tasks to be considered. In this chapter, the authors focus on interpreting motor imagery tasks and more specifically, imagining left hand, right hand, foot and tongue. Interpreting the signal consists of two main steps: feature extraction and classification. For the feature extraction,Empirical Mode Decomposition (EMD) was used and for the classification,the Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel was used. The authors evaluated this system using the BCI competition IV dataset and reached a very promising accuracy.

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