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Supervised Facial Recognition Based on Multi-Resolution Analysis and Feature Alignment 57

Supervised Facial Recognition Based on Multi-Resolution Anal...

引用

57th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Aldhahab, Ahmed Atia, George Mikhael, Wasfy B. Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781479941322

A new supervised algorithm for face recognition based on the integration of Two-Dimensional Discrete Multiwavelet Transform (2-D DMWT), 2-D Radon Transform, and 2-D Discrete Wavelet Transform (2-D DWT) is proposed1. In the feature extraction step, Multiwavelet filter banks are used to extract useful information from the face images. The extracted information is then aligned using the Radon Transform, and localized into a single band using 2-D DWT for efficient sparse data representation. This information is fed into a Neural Network based classifier for training and testing. The proposed method is tested on three different databases, namely, ORL, YALE and subset fc of FERET, which comprise different poses and lighting conditions. It is shown that this approach can significantly improve the classification performance and the storage requirements of the overall recognition system.

关键词： Classification algorithms Databases Discrete wavelet transforms Feature extraction Multiresolution analysis Training Multiresolution analysis discrete wavelet transform sorting algorithm Feature extraction Database Radon transforms Facial Recognition Functional training Storage requirements recognition system Training

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Integrate Classifier Diversity Evaluation to Feature Selection Based Classifier Ensemble Reduction 14

Integrate Classifier Diversity Evaluation to Feature Selecti...

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14th UK Annual Workshop on Computational Intelligence (UKCI)

作者： Yao, Gang Chao, Fei Zeng, Hualin Shi, Minghui Jiang, Min Zhou, Changle Xiamen Univ Sch Informat Sci & Engn Dept Cognit Sci Fujian Prov Key Lab Brain Intelligent Syst Xiamen 361005 Peoples R China

ISBN: (纸本)9781479955381

Classifier ensembles improve the performance of single classifier system. However, a classifier ensemble with too many classifiers may occupy a large number of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a classifier ensemble reduction framework. The approach is implemented by using three conventional diversity algorithms and one new developed diversity measure method to calculate the diversity's merits within the classifier ensemble reduction framework. The subset evaluation method is demonstrated by the experimental data: the method not only can meet the requirements of high accuracy rate and fewer size, but also its running time is greatly shortened. When the accuracy requirements are not very strict, but the the running time requirements is more stringent, the proposed method is a good choice.

关键词： Accuracy Classification algorithms Diversity methods Extraterrestrial measurements Machine learning algorithms Q measurement Training Classifier Ensembles Q measurement Extraterrestrial measurements Diversity methods Machine learning algorithms sorting algorithm Functional training required precision Classifiers Runtime accuracy rate

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A Power Wheelchair Controlled using Hand Gestures, a Single sEMG Sensor, and Guided Under-determined Source Signal Separation

A Power Wheelchair Controlled using Hand Gestures, a Single ...

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4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) / Symposium on Surgical Robotics

作者： Rivera, Luis A. DeSouza, Guilherme N. Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

ISBN: (纸本)9781457712005

Surface Electromyographic signals (sEMG) find applications in many areas such as rehabilitation, prosthesis and human-machine interaction. Systems reliant on these muscle-generated electrical signals require some form of machine learning algorithm for recognition of specific patterns of muscle activity. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used, however, in all those cases, the use of multiple sensors is a constant requirement. In this paper, we present a power wheelchair control system that relies on a single sEMG sensor and a new technique for signature recognition called Guided Under-determined Source Signal Separation (GUSSS). Compared to other approaches in the literature, the proposed technique achieves comparable results even when using a simple distance classifier and a very small number of features.

关键词： Electric wheelchair SEMA5B gene Source separation sorting algorithm Machine learning algorithms Hand Sensors Gestures Feature extraction

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Using Classification for Cost Reduction of Applying Mutation Testing

Using Classification for Cost Reduction of Applying Mutation...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Strug, Joanna Strug, Barbara Cracow Univ Technol Dept Elect & Comp Engn Ul Warszawska 24 PL-30059 Krakow Poland Jagiellonian Univ Dept Phys Astron & Appl Comp Sci Ul Lojasiewicza 11 PL-30348 Krakow Poland

ISBN: (纸本)9788394625375

The paper uses machine learning methods to deal with the problem of reducing the cost of applying mutation testing. A method of classifying mutants of a program using structural similarity is proposed. To calculate such a similarity each mutant is firstly converted into a hierarchical graph, which represents the mutant's control flow, variables and conditions. Then using such a graph form graph kernels are introduced to calculate similarity among mutants. The classification algorithm is then applied for prediction. This approach helps to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented.

关键词： problem reducing cost COST REDUCTION Mutants Line graph sorting algorithm

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Classification of Patients with Heart Failure

Classification of Patients with Heart Failure

引用

18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Bayrak, Tuncay Ogul, Hasan Baskent Univ Bilgisayar Muhendisligi Bolumu Etimesgut Ankara Turkey

ISBN: (纸本)9781479975723

Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.

关键词： Bayes methods biomedical transducers echocardiography feature extraction learning (artificial intelligence) medical signal processing signal classification ultrasonic transducers dataset echocardiogram signals feature classification heart anatomy heart failure heart physiology high-frequency sound waves machine learning repository database naive Bayes database patient classification Abstracts Classification algorithms Echocardiography Educational institutions Heart Indexes Echocardiography ultrasonic transducers cardiac functions biomedical transducers Heart Failure sorting algorithm medical signal processing signal classification Educational institutions Bayes methods Heart Dataset Feature extraction Heart—Anatomy

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A new Classifier for Remote Sensing Data Classification : Partial Least-Squares

A new Classifier for Remote Sensing Data Classification : Pa...

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International Workshop on Earth Observation and Remote Sensing Applications

作者： Du, H. Q. Ge, H. L. Liu, E. B. Xu, W. B. Jin, W. Fan, W. Y. NE Forestry Univ Forest Coll Harbin 150040 Heilongjiang Pr Peoples R China Zhejiang Forestry Coll Sch Environm Sci & Technol Hangzhou Linan 311300 Zhejiang Peoples R China

ISBN: (纸本)9781424423934

This study has presented a new classifier-the Partial Least Squares (PLS) classifier including linear and nonlinear based on the Partial Least-Squares Regression theory, then explained the classification algorithm and process of this new classifier, and finally, them have been applied to classify Landsat TM remote sensing data. Results of Partial Least-Squares linear classifier showed that there exist many classify mistake among six kinds of land use types. On the contrary, the nonlinear classifier based on Gaussian kernel function got better classification result, the overall classification accuracy is 79.297% and overall Kappa statistics is 0.74213. So, to remote sensing classification, the nonlinear PLS classifier is basic feasible, however, it is necessary for us to improve its algorithms or learning process further.

关键词： Classifiers kappa statistics data classification sorting algorithm remote-sensing data nonlinearity Learning processes Taxonomy Partial

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Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Feature Selection for Classification Incorporating Less Mean...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Wosiak, Agnieszka Zakrzewska, Danuta Lodz Univ Technol Inst Informat Technol PL-90924 Lodz Poland

ISBN: (纸本)9788360810583

In medical diagnostics there is a constant need of searching for new methods of attribute acquiring, but it is difficult to asses if these new features can support the existing ones and can be useful in medical inference. In the paper the methodology of discovering features which are less informative while considering independently, however meaningful for diagnosis making, is investigated. The proposed methodology can contribute to better use of attributes, which have not been considered in the diagnostics process so far. The experimental study, which concerns arterial hypertension as one of the civilization diseases demanding early detection and improved treatment is presented. The experiments confirmed that additional attributes enable obtaining the diagnostic results comparable to the ones received by using the most obvious features.

关键词： algorithm design and analysis Classification algorithms Clustering algorithms Hypertension Machine learning algorithms Medical diagnostic imaging Strain algorithm design and analysis Machine learning algorithms sorting algorithm Clustering algorithms Hypertension Feature extraction diagnostic imaging Diagnosis Medical

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Multitime frequency classifiers for neighboring modulations in a multiplivative noise environment 25

Multitime frequency classifiers for neighboring modulations ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Gelle, G Colas, M Delaunay, G URCA LAM F-51687 Reims 02 France

ISBN: (纸本)0780362934

This communication presents two new classification algorithms based on Time Varying Higher Order Statistic (TVHOS). These two algorithms benefit from the advantageous localization properties provided by TVHOS4 for instantaneous frequency laws disrupted by a multiplicative noise. The classification scheme used is a bank of several normalized TVHOS4 correlators. Simulations illustrate successful performance of the classification algorithms for different situations and especially if the SNR is higher than -7 dB.

关键词： multiplicative noise noise environment sorting algorithm classification scheme Classifiers Higher order statistics instantaneous frequency

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Three-Phase Modular Multilevel Converter (MMC) for Low-voltage Applications: Improved Modulation Technique Toward Less Capacitance Requirement

Three-Phase Modular Multilevel Converter (MMC) for Low-volta...

引用

IEEE Conference on Power Electronics and Renewable Energy (IEEE CPERE)

作者： Younis, Tarek Mattavelli, Paolo Magnone, Paolo Toigo, Igino Corradin, Michele Univ Padua Dept Management & Engn Vicenza Italy Socomec Vicenza Italy

ISBN: (纸本)9781728109107

Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages over other multilevel topologies when considering high number of levels. This paper presents an alternative modulation technique for a modified modular multilevel converter (M-MMC) topology for low-voltage applications. By using level-shifted carrier (LSC) modulation and the proposed sorting algorithm, the capacitance requirement for this topology can be reduced. Finally, the proposed modulation scheme and the reduction of capacitance are verified by simulation results using PLECS.

关键词： Modular multilevel converters (MMCs) renewable energy multilevel converters level-shifted carrier (LSC) low-voltage systems modulation sorting algorithm

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Experimental Study on the Multiple Use of Training Patterns in Confidence-Weighted On-Line Learning for Fuzzy Classifiers

Experimental Study on the Multiple Use of Training Patterns ...

引用

IEEE International Conference on Granular Computing (GrC)

作者： Nakashima, Tomoharu Piera, Julien Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479954643

On-line learning for fuzzy classifiers are investigated in this paper. In the on-learning problems, it is assumed that there are only a single training patterns available at a time. This paper extends the assumption by allowing multiple training patterns available for incrementally update the fuzzy classifiers. Confidence-weighted learning is used for the on-line learning of the fuzzy classifiers. Computational experiments are conducted to show the high-performance of the fuzzy classifiers with the confidence-weighted learning.

关键词： Classification algorithms Fuzzy sets Gaussian distribution Mathematical model Optimization Support vector machine classification Training Support vector machine classification sorting algorithm Functional training Fuzzy Set Theory Gaussian distribution Classifiers Fuzzy Mathematical Model Training confidence IN-LINE

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