Combining Block DCV and Support Vector Machine for Ear Recognition

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

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A Computational Method for the Identification of Endolysins and Autolysins

Protein and Peptide Letters ◽

10.2174/0929866526666191002104735 ◽

2020 ◽

Vol 27 (4) ◽

pp. 329-336 ◽

Cited By ~ 1

Author(s):

Lei Xu ◽

Guangmin Liang ◽

Baowen Chen ◽

Xu Tan ◽

Huaikun Xiang ◽

...

Keyword(s):

Support Vector Machine ◽

Cell Wall ◽

Experimental Results ◽

Computational Method ◽

Lytic Enzyme ◽

Support Vector ◽

Lytic Enzymes ◽

Data Set ◽

Optimal Feature ◽

Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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Relevance Feedback berdasarkan Support Vector Machine pada Content Based Image Retrieval

Jurnal Teknik Informatika ◽

10.35793/jti.1.1.2012.543 ◽

2012 ◽

Vol 1 (1) ◽

Author(s):

Virginia Tulenan

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Image Retrieval ◽

Relevance Feedback ◽

Content Based Image Retrieval ◽

Support Vector ◽

Relevant Feedback

Content based image retrieval adalah bidang penelitianyang sangat penting saat ini dalam bidang multimedia database.Banyak penelitian yang telah dilakukan dalam dekade terakhiruntuk merancang teknik image retrieval yang efisien dari imagedatabase. Meskipun banyak teknik pengindeksan dan retrievaltelah dikembangkan, namun masih belum terdapat teknikpemisahan ciri (feature extraction), indexing dan retrieval yangbisa diterima secara universal oleh semua orang. Dalam tulisanini, digunakanlah metode relevant feedback berdasarkan supportvector machine (SVM) dan muhalobis distance untuk pengukurankemiripan pada image retrieval.

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A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

Journal of Sensors ◽

10.1155/2016/6506249 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Tsun-Kuo Lin

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Experimental Results ◽

Support Vector ◽

Manufacturing Processes ◽

Feature Description ◽

Blur Detection ◽

Industrial Inspection ◽

Blurred Images ◽

Edge Feature

A novel inspection sensor by using an edge feature description (EFD) algorithm based on a support vector machine (SVM) is proposed for industrial inspection of images. This method detects and adaptively segments blurred images by using the proposed algorithm, which uses EFD to effectively classify blurred samples and improve the conventional methods of inspecting blurred objects; the algorithm selects and optimally tunes suitable features. The proposed sensor applies a suitable feature-extraction strategy on the basis of the sensing results. Experimental results demonstrate that the proposed method outperforms the existing methods.

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An Improvement Of Least Square - Twin Support Vector Machine

Research and Development on Information and Communication Technology ◽

10.32913/mic-ict-research.v2021.n1.956 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Thanh Vi Nguyen ◽

Thế Cường Nguyễn

Keyword(s):

Support Vector Machine ◽

Binary Classification ◽

Least Square ◽

Experimental Results ◽

Twin Support Vector Machine ◽

Support Vector ◽

Classification Problems ◽

Training Time ◽

Data Points ◽

Better Than

n binary classification problems, two classes of data seem tobe different from each other. It is expected to be more complicated dueto the number of data points of clusters in each class also be different.Traditional algorithms as Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support VectorMachine (LSTSVM) cannot sufficiently exploit information about thenumber of data points in each cluster of the data. Which may be effectto the accuracy of classification problems. In this paper, we proposes anew Improved Least Square - Support Vector Machine (called ILS-SVM)for binary classification problems with a class-vs-clusters strategy. Experimental results show that the ILS-SVM training time is faster thanthat of TSVM, and the ILS-SVM accuracy is better than LSTSVM andTSVM in most cases.

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Urdu Sentiment Analysis Using Supervised Machine Learning Approach

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418510011 ◽

2017 ◽

Vol 32 (02) ◽

pp. 1851001 ◽

Cited By ~ 17

Author(s):

Neelam Mukhtar ◽

Mohammad Abid Khan

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Decision Tree ◽

Sentiment Analysis ◽

Nearest Neighbor ◽

Supervised Machine Learning ◽

Support Vector ◽

Ensemble Of Classifiers ◽

Machine Learning Approach ◽

Better Than

From the last decade, Sentiment Analysis of languages such as English and Chinese are particularly the focus of attention but resource poor languages such as Urdu are mostly ignored by the research community, which is focused in this research. After acquiring data from various blogs of about 14 different genres, the data is being annotated with the help of human annotators. Three well-known classifiers, that is, Support Vector Machine, Decision tree and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) are tested, their outputs are compared and their results are ultimately improved in several iterations after taking a number of steps that include stop words removal, feature extraction, identification and extraction of important features. extraction. Initially, the performance of the classifiers is not satisfactory as the accuracy achieved by all the three is below 50%. Ensemble of classifiers is also tried but the results are not fruitful (in terms of high accuracy). The results are analyzed carefully and improvements are made including feature extraction that raised the performance of these classifiers to a satisfactory level. It is further concluded that [Formula: see text]-NN is performing better than Support Vector Machine and Decision tree in terms of accuracy, precision, recall and [Formula: see text]-measure.

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A Behavior-Driven Forum Spammer Recognition Method with Its Application in Automobile Forums

Mathematical Problems in Engineering ◽

10.1155/2021/7682579 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Han Su ◽

Minglun Ren ◽

Anning Wang ◽

Xiaoan Tang ◽

Xin Ni ◽

...

Keyword(s):

Support Vector Machine ◽

Real World ◽

Experimental Results ◽

Support Vector ◽

Recognition Method ◽

Public Preferences ◽

Malicious Attack ◽

Behavioral Features ◽

Market Trends ◽

Effective Use

Forum comments are valuable information for enterprises to discover public preferences and market trends. However, extensive marketing and malicious attack behaviors in forums are always an obstacle for enterprises to make effective use of this information. And these forum spammers are constantly updating technology to prevent detection. Therefore, how to accurately recognize forum spammers has become an important issue. Aiming to accurately recognize forum spammers, this paper changes the research target from understanding abnormal reviews and the suspicious relationship among forum spammers to discover how they must behave (follow or be followed) to achieve their monetary goals. First, we classify forum spammers into automated forum spammers and marketing forum spammers based on different behavioral features. Then, we propose a support vector machine-based automated spammer recognition (ASR) model and a k-means clustering-based marketing spammer recognition (MSR) model. The experimental results on the real-world labelled dataset illustrate the effectiveness of our methods on classification spammer from common users. To the best of our knowledge, this work is among the first to construct behavior-driven recognition models according to the different behavioral patterns of forum spammers.

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Hybrid Tile based Feature Extraction and Support Vector Machine Base Content-Based Image Retrieval System for Medical Application

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5433.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 3305-3310

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Image Retrieval ◽

Color Image ◽

Medical Application ◽

Recall Rate ◽

Image Features ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method

Through the landing of therapeutic endoscopes, earth perception satellites and individual telephones, content-based picture recovery (CBIR) has concerned critical consideration, activated by its broad applications, e.g., medicinal picture investigation, removed detecting, and individual re-distinguishing proof. Be that as it may, developing successful component extraction is as yet reported as an invigorating issue.In this paper, to overcome the feature extraction problems a hybrid Tile Based Feature Extraction (TBFE) is introduced. The TBFE algorithm is hybrid with the local binary pattern (LBP) and Local derivative pattern (LDP). These hybrid TBFE feature extraction method helps to extract the color image features in automatic manner. Support vector machine (SVM) is used as a classifier in this image retrieval approach to retrieve the images from the database. The hybrid TBFE along with the SVM classifier image retrieval is named as IR-TBFE-SVM. Experiments show that IR-TBFE-SVMdelivers a higher correctness and recall rate than single feature employed retrieval systems, and ownsdecentweight balancing and query efficiency performance.

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A Novel BCI Classifier Based on Autoregressive Model and Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.2522 ◽

2013 ◽

Vol 694-697 ◽

pp. 2522-2525

Author(s):

Lu Huang ◽

Jun Gu ◽

Ran Li ◽

Xiang Jun Li ◽

Hong Yu

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Autoregressive Model ◽

Recognition Accuracy ◽

Experimental Results ◽

Ar Model ◽

Support Vector ◽

Eeg Signals ◽

Bci System

The efficient featureextraction and classification are very crucial for brain computerinterface(BCI) system. In this paper, feature extraction and classification forP300, a kind of EEG characteristic potential, was conducted. Afterpreprocessing EEG signals, we used autoregressive(AR) model for featureextraction, segmenting the selected EEG channel data and building AR model foreach segment respectively. AR model coefficients were estimated by using leastsquare method, and the estimated coefficient sequence constituted the featurevector. We applied support vector machine(SVM) for classification andexperimented on real EEG dataset. The experimental results showed the proposedmethod had a good recognition accuracy, being worth researching in the field of BCI.

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

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.8947 ◽

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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