scholarly journals Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints

2019 ◽  
Author(s):  
Camilo Lélis A. Gonçalves ◽  
Ronaldo F. Zampolo ◽  
Fabrício José B. Barros ◽  
Ana Claudia S. Gomes ◽  
Eduardo C. de Carvalho ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Service Providers ◽  
Geometric Constraints ◽  
Support Vector ◽  
Automatic Inspection ◽  
Detection And Tracking ◽  
High Classification Accuracy ◽  
Standard Concept ◽  
The Ideal

The inspection of train and railway components that can cause derailment plays a key role in rail maintenance. To improve productivity and safety, service providers look for automatic and reliable inspection solutions. Although automatic inspection based on computer vision is a standard concept, such an application challenges development community due to the environmental and logistic factors to be considered. Previous publications presented automatic classifiers to evaluate integrity and placement of wagon components. Although the high classification accuracy reported, ineffective object detection affected the general performance. Our object detector/tracker consists of a descriptor based on the histogram of oriented gradients, a support vector machine classifier, and a set of geometric constraints, which takes in account the ideal trajectory path of the wagon’s components of interest and the distances between them. We detail training and validation procedures, together with the metrics used to assess the performance of the system. Presented results compare two other techniques with our approach, which exhibits a fair trade-off between reliability and computational complexity for the application of wagon component detection.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

scholarly journals An emotion classification method from electroencephalogram based on 1/f fluctuation theory

2020 ◽  
Vol 53 (5-6) ◽  
pp. 824-832
Author(s):  
Hao Li ◽  
Xia Mao ◽  
Lijiang Chen
Keyword(s):  
Real Time ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Fluctuation Theory ◽  
Classification Method ◽  
Support Vector ◽  
Signal Acquisition ◽  
Emotion Classification ◽  
The Real ◽  
Time Performance

Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

EXPOSING DIGITAL VIDEO LOGO-REMOVAL FORGERY BY INCONSISTENCY OF BLUR

2010 ◽  
Vol 24 (07) ◽  
pp. 1027-1046 ◽  
Author(s):  
YUTING SU ◽  
JING ZHANG ◽  
YU HAN ◽  
JING CHEN ◽  
QINGZHONG LIU
Keyword(s):  
Classification Accuracy ◽  
Digital Video ◽  
Support Vector ◽  
Forgery Detection ◽  
Computation Cost ◽  
Statistical Property ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Fine Classification ◽  
Reference Areas

A novel approach for detecting video logo-removal forgery is proposed by measuring inconsistency of blur. Our approach is based on the assumption that if a digital video undergoes logo-removal forgery; the blurriness of the forged region is expected to be different as compared to the nontampered parts of the video. Blurriness is first estimated by analyzing the spatial and temporal statistical property of logo areas, and suspicious areas are roughly located; then features are extracted and a fine classification is implemented by applying support vector machine (SVM) to extract features. If the suspicious areas and the reference areas are classified into different classes, the video is judged as a forged video. Experimental results show that our method is robust to video lossy compression for logo-removal forgery detection with the advantages of high classification accuracy and low computation cost.

scholarly journals Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Na Liu ◽  
Jiang Shen ◽  
Man Xu ◽  
Dan Gan ◽  
Er-Shi Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Treatment Plan ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Classification Models ◽  
Misclassification Costs

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

Analysis of Textual Data Based on Inductive Learning Techniques

2013 ◽  
Vol 3 (2) ◽  
pp. 40-57
Author(s):  
Shigeaki Sakurai
Keyword(s):  
Classification Accuracy ◽  
Inductive Learning ◽  
Classification Model ◽  
Support Vector ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Named Entity ◽  
High Classification Accuracy ◽  
Learning Techniques ◽  
Textual Data

This paper introduces knowledge discovery methods based on inductive learning techniques from textual data. The author argues three methods extracting features of the textual data. First one activates a key concept dictionary, second one does a key phrase pattern dictionary, and third one does a named entity extractor. These features are used in order to generate rules representing relationships between the features and text classes. The rules are described in the format of a fuzzy decision tree. Also, these features are used in order to acquire a classification model based on SVM (Support Vector Machine). The model can classify new textual data into the text classes with high classification accuracy. Lastly, this paper introduces two application tasks based on these methods and verifies the effect of the methods.

scholarly journals The Improvement of a Brain Computer Interface Based on EEG Signals

2021 ◽  
Author(s):  
Maryam Khakpour
Keyword(s):  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Brain Computer Interface ◽  
High Accuracy ◽  
Computer Interface ◽  
Support Vector ◽  
Signal Acquisition ◽  
Common Spatial Pattern ◽  
Reliability And Robustness ◽  
Selection Phase

Purpose: Brain Computer Interface (BCI) has provided a novel way of communication that can significantly revolutionize life of people suffering from disabilities. Motor Imagery (MI) EEG BCI is one of the most promising solutions to address. The main phases of such systems include signal acquisition, pre-processing, feature extraction, classification and the intended interface. The challenging obstacles in such systems are to detect and extract efficient features that present reliability and robustness alongside promising classification accuracy. In this paper it is endeavored to present a robust method for a two-class MI BCI that results in high accuracy. Materials and Methods: For this purpose, the dataset 2b from BCI competition 2008, consisting of three channels (C3, C and Cz), was utilized. Firstly, the signals were bandpass filtered. Secondly, Common Spatial Pattern (CSP) was employed and then a number of features, including non-linear chaotic features were extracted from channels C3 and C4. After feature selection phase the number of features were reduced to 38 and 47. Finally, these features were fed into two classifiers, namely Support Vector Machine classifier (SVM) and Bagging to evaluate the performance of the system. Results: Classification accuracy and Cohen’s Kappa coefficient of the proposed method for two MI EEG channels are 96.40% and 0.92, respectively. Conclusion: These results indicate the high accuracy and stability of our method in comparison with similar studies. Therefore, it can be a promising approach in two-class MI BCI systems.

scholarly journals Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

2022 ◽  
Vol 13 (2) ◽  
pp. 1-11
Author(s):  
Malathy Jawahar ◽  
L. Jani Anbarasi ◽  
Prassanna Jayachandran ◽  
Manikandan Ramachandran ◽  
Fadi Al-Turjman
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Hybrid Algorithm ◽  
Vital Role ◽  
Support Vector ◽  
Learning Approach ◽  
X Ray ◽  
High Classification Accuracy ◽  
Chest X Ray ◽  
Different Parts

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

Utilization of Transfer Learning Model in Detecting COVID-19 cases from Chest X-Ray Images

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Hybrid Algorithm ◽  
Vital Role ◽  
Support Vector ◽  
Learning Approach ◽  
X Ray ◽  
High Classification Accuracy ◽  
Chest X Ray ◽  
Different Parts

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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