BASIC HANDWRITTEN CHARACTER RECOGNITION FROM MULTI-LINGUAL IMAGE DATASET USING MULTI-RESOLUTION AND MULTI-DIRECTIONAL TRANSFORM

2012 ◽  
Vol 10 (05) ◽  
pp. 1250046 ◽  
Author(s):  
SHITALA PRASAD ◽  
GYANENDRA K. VERMA ◽  
BHUPESH KUMAR SINGH ◽  
PIYUSH KUMAR
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Morphological Alteration ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Novel Approach ◽  
Pros And Cons ◽  
Resolution Algorithm ◽  
Level Fusion

This paper, proposes a novel approach for feature extraction based on the segmentation and morphological alteration of handwritten multi-lingual characters. We explored multi-resolution and multi-directional transforms such as wavelet, curvelet and ridgelet transform to extract classifying features of handwritten multi-lingual images. Evaluating the pros and cons of each multi-resolution algorithm has been discussed and resolved that Curvelet-based features extraction is most promising for multi-lingual character recognition. We have also applied some morphological operation such as thinning and thickening then feature level fusion is performed in order to create robust feature vector for classification. The classification is performed with K-nearest neighbor (K-NN) and support vector machine (SVM) classifier with their relative performance. We experiment with our in-house dataset, compiled in our lab by more than 50 personnel.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

scholarly journals Improving Accuracy of Quasars' Photometric Redshift Estimation by Integration of KNN and SVM

2015 ◽  
Vol 11 (A29A) ◽  
pp. 209-209
Author(s):  
Bo Han ◽  
Hongpeng Ding ◽  
Yanxia Zhang ◽  
Yongheng Zhao
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Unsolved Problem ◽  
Support Vector ◽  
Catastrophic Failure ◽  
K Nearest Neighbor ◽  
Photometric Redshift ◽  
Novel Approach ◽  
Improving Accuracy ◽  
Fusion Approach

AbstractCatastrophic failure is an unsolved problem existing in the most photometric redshift estimation approaches for a long history. In this study, we propose a novel approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. Experiments based on the quasar sample from SDSS show that the fusion approach can significantly mitigate catastrophic failure and improve the accuracy of photometric redshift estimation.

Supervised Learning in Game Data Science

2021 ◽  
pp. 179-218
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Regression Methods ◽  
Vector Machines ◽  
Pros And Cons ◽  
Classification And Regression

This chapter discusses several classification and regression methods that can be used with game data. Specifically, we will discuss regression methods, including Linear Regression, and classification methods, including K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Support Vector Machines, Decisions Trees, and Random Forests. We will discuss how you can setup the data to apply these algorithms, as well as how you can interpret the results and the pros and cons for each of the methods discussed. We will conclude the chapter with some remarks on the process of application of these methods to games and the expected outcomes. The chapter also includes practical labs to walk you through the process of applying these methods to real game data.

The Novel Indices of Short-Time Heart Rate Variability for Prediction of Cardiovascular and Cerebrovascular Events

2020 ◽  
Vol 10 (3) ◽  
pp. 769-774
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Chunhe Song ◽  
Yun Su ◽  
Xingchi Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Singular Value ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Cerebrovascular Events ◽  
The Hilbert Transform ◽  
Short Time

In this paper, eight novel instantaneous indices of short-time heart rate variability (HRV) signals are proposed for prediction of cardiovascular and cerebrovascular events. The indices are based on Bubble Entropy (BE) and Singular Value Decompose (SVD). The process of indices calculation is as follows, firstly, the instantaneous amplitude (IA), instantaneous frequency (IF) and instantaneous phase (IP) of HRV signals are estimated by the Hilbert transform. Secondly, according to the HRV, IA, IP and IF, the BE and singular value (SV) is calculated, then eight novel indices are obtained, they are BEHRV, BEIA, BEIF, BEIP, SVHRV, SVIA, SVIF and SVIP. Last but not least, in order to evaluate the performance of the eight novel indices for prediction of cardiovascular and cerebrovascular events, the difference analysis of eight indices is carried out by t-test. According to the p value, seven of the eight indices BEHRV, BEIA, BEIF, BEIP, SVIA, SVIF and SVIP are thought to be the indices to discriminate the E group and N group. The K-nearest neighbor (KNN), support vector machine (SVM) and decision tree (DT) are applied on the seven novel indices. The results are that, seven novel indices are significantly different between the events and non-events groups, and the SVM classifier has the highest classification Acc and Spe for prediction of cardiovascular and cerebrovascular events, they are 88.31% and 90.19%, respectively.

Study on XML Retrieval Results Classification

2012 ◽  
Vol 263-266 ◽  
pp. 1773-1777
Author(s):  
Hong Yu ◽  
Xiao Lei Huang ◽  
Zhi Ling Wei ◽  
Chen Xia Yang
Keyword(s):  
Nearest Neighbor ◽  
Classification Performance ◽  
Feedback Mechanism ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Xml Retrieval ◽  
Text Documents ◽  
Plain Text ◽  
Xml Documents

Mining (classify or clustering) retrieval results to serve relevance feedback mechanism of search engine is an important solution to improve effectiveness of retrieval. Unlike plain text documents, since the XML documents are semi-structured data, for XML retrieval results classification, consider exploiting structure features of XML documents, such as tag paths and edges etc. We propose to use Support Vector Machine (SVM) classifier to classify XML retrieval results exploiting both their content and structure features. We implemented the classification method on XML retrieval results based on the IEEE SC corpus. Compared with k-nearest neighbor classification (KNN) on the same dataset in our application, SVM perform better. The experiment results have also shown that the use of structure features, especially tag paths and edges, can improve the classification performance significantly.

scholarly journals FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Pan ◽  
Li-Ping Li ◽  
Chang-Qing Yu ◽  
Zhu-Hong You ◽  
Zhong-Hao Ren ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Nearest Neighbor ◽  
Protein Sequences ◽  
Evolutionary Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
K Nearest Neighbor ◽  
Novel Approach ◽  
Knn Classifier ◽  
Scoring Matrix

Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach to predict PPIs in plants only using the information of protein sequences. Specifically, plants’ protein sequences are first converted as position-specific scoring matrix (PSSM); then, the fast Walsh–Hadamard transform (FWHT) algorithm is used to extract feature vectors from PSSM to obtain evolutionary information of plant proteins. Lastly, the rotation forest (RF) classifier is trained for prediction and produced a series of evaluation results. In this work, we named this approach FWHT-RF because FWHT and RF are used for feature extraction and classification, respectively. When applying FWHT-RF on three plants’ PPI datasets Maize, Rice, and Arabidopsis thaliana (Arabidopsis), the average accuracies of FWHT-RF using 5-fold cross validation were achieved as high as 95.20%, 94.42%, and 83.85%, respectively. To further evaluate the predictive power of FWHT-RF, we compared it with the state-of-art support vector machine (SVM) and K-nearest neighbor (KNN) classifier in different aspects. The experimental results demonstrated that FWHT-RF can be a useful supplementary method to predict potential PPIs in plants.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

scholarly journals Detecting Types of Sleep Apneas Through Nonlinear Features of Electrocardiogram and Electroencephalogram Signals

2021 ◽  
Author(s):  
Shaghayegh Saghafi ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Sleep Apnea ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Majority Voting ◽  
Support Vector ◽  
Svm Classifier ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nonlinear Features

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important. Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron. Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively. Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

scholarly journals Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

2018 ◽  
Vol 6 (4) ◽  
pp. 129-134 ◽  
Author(s):  
Jumoke Falilat Ajao ◽  
David Olufemi Olawuyi ◽  
Odetunji Ode Odejobi
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Chain Code ◽  
Nearest Neighbor Classifier ◽  
Digitized Images ◽  
Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

scholarly journals Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection

2020 ◽  
Vol 26 (3) ◽  
pp. 155-160
Author(s):  
Aicha Mokdad ◽  
Sidi Mohammed El Amine Debbal ◽  
Fadia Meziani
Keyword(s):  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Neuromuscular Disorders ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Emg Signal ◽  
Frequency Features

AbstractElectromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.

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