scholarly journals Classification of Historical Documents Based on LBP and LPQ Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1534-1539
Keyword(s):  
Support Vector ◽  
Historical Documents ◽  
Automatic Identification ◽  
Document Images ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Modern Language ◽  
Handwritten Document ◽  
Feature Values

Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.

Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

2016 ◽  
Vol 15 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Hedieh Sajedi ◽  
Mehran Bahador
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Nearest Neighbors ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Number Recognition ◽  
Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification

2013 ◽  
pp. 786-797
Author(s):  
Ruofei Wang ◽  
Xieping Gao
Keyword(s):  
Query Protein ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Protein Folds ◽  
Testing Dataset ◽  
Ensemble Strategy ◽  
Component Classifier ◽  
Independent Testing Dataset

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

2019 ◽  
Vol 16 (10) ◽  
pp. 4170-4178
Author(s):  
Sheifali Gupta ◽  
Gurleen Kaur ◽  
Deepali Gupta ◽  
Udit Jindal
Keyword(s):  
Region Of Interest ◽  
Initial Step ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Lighting Conditions ◽  
Roi Extraction ◽  
Feature Values ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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