scholarly journals DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

2021 ◽  
Vol 7 (7) ◽  
pp. 114
Author(s):  
Fitri Arnia ◽  
Khairun Saddami ◽  
Khairul Munadi
Keyword(s):  
Speckle Noise ◽  
Support Vector ◽  
Learning Support ◽  
Document Images ◽  
Additional Noise ◽  
Conventional Machine ◽  
Ancient Documents ◽  
Noise Robust ◽  
Better Than

Analysis of degraded ancient documents is challenging due to the severity and combination of degradation present in a single image. Ancient documents also suffer from additional noise during the digitalization process, particularly when digitalization is done using low-specification devices and/or under poor illumination conditions. The noises over the degraded ancient documents certainly cause a troublesome document analysis. In this paper, we propose a new noise-robust convolutional neural network (CNN) architecture for degradation classification of noisy ancient documents, which is called a degradation classification network (DCNet). DCNet was constructed based on the ResNet101, MobileNetV2, and ShuffleNet architectures. Furthermore, we propose a new self-transition layer following DCNet. We trained the DCNet using (1) noise-free document images and (2) heavy-noise (zero mean Gaussian noise (ZMGN) and speckle) document images. Then, we tested the resulted models with document images containing different levels of ZMGN and speckle noise. We compared our results to three CNN benchmarking architectures, namely MobileNet, ShuffleNet, and ResNet101. In general, the proposed architecture performed better than MobileNet, ShuffleNet, ResNet101, and conventional machine learning (support vector machine and random forest), particularly for documents with heavy noise.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

DIALOG ACT CLASSIFICATION USING ACOUSTIC AND DISCOURSE INFORMATION OF MAPTASK DATA

2010 ◽  
Vol 09 (04) ◽  
pp. 289-311 ◽  
Author(s):  
FATEMA N. JULIA ◽  
KHAN M. IFTEKHARUDDIN ◽  
ATIQ U. ISLAM
Keyword(s):  
Classifier Fusion ◽  
Support Vector ◽  
Acoustic Features ◽  
Average Precision ◽  
Data Set ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Accuracy Rates ◽  
Better Than

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set.

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.

scholarly journals Supervised Learning Based Classification of Cardiovascular Diseases

2021 ◽  
Vol 20 ◽  
pp. 24-34
Author(s):  
Arif Hussain ◽  
Hassaan Malik ◽  
Muhammad Umar Chaudhry
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Matthews Correlation Coefficient ◽  
Early Stage ◽  
Support Vector ◽  
Logistics Regression ◽  
Efficiency And Effectiveness ◽  
Sensitivity Specificity ◽  
Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

scholarly journals A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 7080
Author(s):  
Christopher Flores ◽  
Carla Taramasco ◽  
Maria Elena Lagos ◽  
Carla Rimassa ◽  
Rosa Figueroa
Keyword(s):  
Short Term Memory ◽  
National Level ◽  
Point Of View ◽  
Support Vector ◽  
Mechanical Ventilators ◽  
Feature Based ◽  
Risk Categories ◽  
Better Than

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

Study on Detection Method for Crack in Eggs Based on Computer Vision and Support Vector Machine Neural Network

2014 ◽  
Vol 472 ◽  
pp. 176-179 ◽  
Author(s):  
Jian Yang ◽  
Ying Shi ◽  
Wei Zhou ◽  
Yong Shun Che
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Traditional Method ◽  
Detection Method ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Sample Data ◽  
Better Than

To improve the accuracy of detection and classification of egg with cracks, this paper is to add Support Vector Machine to neural network to automatically identify and classify the eggs with cracks. Firstly process the egg images with light-transmitting were obtained by the computer vision device including denoising, threshold segmentation. Five characteristic parameters of crack areas and noise areas were acquired. Secondly train SVM Neural Network and identify the eggs with cracks by five parameters data as the sample data. The correct discerning rate of grading table eggs is 98.07%. It proves better than traditional method in terms of prediction accuracy and robustness. The generalization ability of SVM Neural Network is strengthened.

SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS

2003 ◽  
Vol 14 (05) ◽  
pp. 575-585 ◽  
Author(s):  
CONG-ZHONG CAI ◽  
WAN-LU WANG ◽  
YU-ZONG CHEN
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Protein Interactions ◽  
Binding Proteins ◽  
Nearest Neighbor ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Testing Accuracy ◽  
Better Than

The support vector machine (SVM) is used in the classification of sonar signals and DNA-binding proteins. Our study on the classification of sonar signals shows that SVM produces a result better than that obtained from other classification methods, which is consistent from the findings of other studies. The testing accuracy of classification is 95.19% as compared with that of 90.4% from multilayered neural network and that of 82.7% from nearest neighbor classifier. From our results on the classification of DNA-binding proteins, one finds that SVM gives a testing accuracy of 82.32%, which is slightly better than that obtained from an earlier study of SVM classification of protein–protein interactions. Hence, our study indicates the usefulness of SVM in the identification of DNA-binding proteins. Further improvements in SVM algorithm and parameters are suggested.

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