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检索条件"主题词=Classification Algorithms"

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Finger Movement classification Based on Statistical and Frequency Features Extracted from Surface EMG Signals

Finger Movement Classification Based on Statistical and Freq...

引用

International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2)

作者： Chyon Krishno Bhattachargee Niloy Sikder Md. Tariq Hasan Abdullah-Al Nahid Electronics & Communication Engineering Discipline Khulna University Khulna Bangladesh Computer Science & Engineering Discipline Khulna University Khulna Bangladesh

ISBN: (数字)9781728130606

ISBN: (纸本)9781728130613

Anatomization of EMG signals is one of the building blocks of modern prostheses. As the goal is to build robotic arms whose functions are identical to the natural ones, EMG signals produced from various hand gestures and finger movements have received much attention in recent times. Surface EMG signals collected from the upper hand muscles show specific patterns for a particular finger movement, which is also true for combined (more than one) finger movements. Utilizing Digital Signal Processing (DSP), and Machine Learning (ML) techniques this paper proposes a novel method to distinguish among various EMG signals generated from ten different hand gestures. To reduce complexity and make the signals more understandable to the algorithm statistical and frequency features were extracted from the raw EMG signals and used for classification. In order to prove the effectiveness of the method, it was tested on a practical EMG dataset and the results of the experiments are presented.

关键词： Electromyography Feature extraction Muscles classification algorithms Prediction algorithms Task analysis Boosting

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Automatic classification Algorithm for Poincare Plots Based On Modified Hausdorff Distance - Support Vector Machine

Automatic Classification Algorithm for Poincare Plots Based ...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Jiahao HUANG Xiang CHEN Hua JIN Jin LI The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi P. R. China Lepu medical electronic instruments co. Ltd

The Poincare plot is a method to reflect heart rate variability, which has its unique advantages in the diagnosis of arrhythmias. At present, the research on automatic classification of Poincare plots is still in its early stage, which greatly affects the clinical application of Poincare plots. Therefore, a new algorithm is proposed in this paper, which makes comprehensive use of the Modified Hausdorff Distance and support vector machine method to do multiple classification of Poincare plots. Then the arrhythmia data in MIT-BIH ECG database were used to test the algorithm. The results showed that the classification accuracy was 90.2%, indicating that the algorithm was basically reliable and could play a certain auxiliary role in clinical Poincare plots based diagnosis classification.

关键词： Electrocardiography Magnetohydrodynamics Support vector machines classification algorithms Heart rate variability Reliability Interference

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Multi-label classification Based on Domain Analysis in Fixed Point Method

Multi-label Classification Based on Domain Analysis in Fixed...

引用

Open Innovations Association (FRUCT), Conference of

作者： Anna Berger Sergey Guda Southern Federal University Rostov-on-Don Russia

ISBN: (数字)9789526924441

ISBN: (纸本)9781728191072

Nowadays a multi-label classification problem arises in different areas for which the significant amount of data has been gained. This problem can be viewed as the one comprising two steps: training some ranking function sorting instances in each class and defining the optimal number of predictions for it. This paper is devoted to the second step of the optimal threshold selection while maximizing the F-macro measure. To do so, we reduce the multi-dimensional problem to the two-dimensional problem of finding a fixed point of a specifically introduced transformation defined on a unit square. We suggest the algorithm of finding the vector of optimal thresholds based on the domain analysis of the introduced transformation. Moreover, we provide the complexity estimations of the proposed algorithm. We evaluate the algorithm on the extreme classification benchmark WikiLSHTC-325K comparing its performance with some baseline results.

关键词： Training Buildings Benchmark testing Approximation algorithms classification algorithms Complexity theory Optimization

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Streaming data classification

Streaming data classification

引用

International Conference on Recent Trends in Information Technology (ICRTIT)

作者： P. V Srilakshmi Annapoorna T. T. Mirnalinee Department of CSE SSN College of Engineering Chennai India

ISBN: (纸本)9781509004331

In the evolving technology of big data, high velocity data streams play a vital role since pattern of data is being changed over time. The temporal pattern change in data stream leads to a concept evolution called concept drift where statistical properties of data differs from time to time and the drift is taken into account in order to update old and outdated classifier and make it adaptable to new data arrival and pattern change over. In order to classify the stream data, a scalable efficient classification algorithm is to be designed which perfectly classifies the data with minimizing misclassification rate in presence of concept drift due to high velocity data. Training time of the classifier must be reduced in order to reduce computational complexity. In this work, a novel algorithm has been implemented using Random Forest with stratified random sampling and Bloom filtering in order to reduce the training time and to handle high velocity data. Experimental results are shown by performing classification with sampling, classification with filtering and classification with sampling and filtering. This enhances the performance of the algorithm by decreasing the training time and testing time of the classifier with negligible compromise in accuracy of classification.

关键词： Data models classification algorithms Filtering Training Decision trees Data mining Predictive models

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Improving the performance of non-rigid 3D shape recovery by points classification

Improving the performance of non-rigid 3D shape recovery by ...

引用

IAPR International Conference on Machine Vision Applications (MVA)

作者： Junjie Hu Terumasa Aoki Graduate School of Information Sciences (GSIS) Tohoku University Sendai Japan New Industry Creation Hatchery Center (NICHe) Tohoku University Sendai Japan

ISBN: (纸本)9781538604953

The goal of Non-Rigid Structure from Motion (NRSfM) is to recover 3D shapes of a deformable object from a monocular video sequence. Procrustean Normal Distribution (PND) is one of the best algorithms for NRSfM. It uses Generalized Procrustes Analysis (GPA) model to accomplish this task. But the biggest problem of this method is that just a few non-rigid points in 2D observations can largely affect the reconstruction performance. We believe that PND can achieve better reconstruction performance by eliminating the affection of these points. In this paper, we present a novel reconstruction method to solve this problem. We present two solutions to simply classify the points into non-rigid and nearly rigid points. After that, we use EM algorithm of PND to recover 3D structure again for nearly rigid points. Experimental results show that the proposed method outperforms the existing state-of-the-art algorithms.

关键词： Shape Three-dimensional displays Two dimensional displays Trajectory Gaussian distribution classification algorithms Face

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A Block-Level RNN Model for Resume Block classification

A Block-Level RNN Model for Resume Block Classification

引用

IEEE International Conference on Big Data

作者： Qiqiang Xu Ji Zhang Youwen Zhu Bohan Li Donghai Guan Xin Wang Nanjing University of Aeronautics and Astronautics China University of Southern Queensland Australia Southwest Petroleum University China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Resume block classification is the most significant step in resume information extraction. However, the existing algorithms applied to resume block classification are all the general text classification algorithms, which failed to consider the contextual order of each block within a resume. In order to improve the performance of resume block classification, we propose in this paper a block-level bidirectional recurrent neural network model that makes full use of the contextual order relationship among different resume blocks. The experimental results show that the average F1-score value of our model on three 1,400 real resume datasets is 6% to 9% higher than the existing methods.

关键词： Recurrent neural networks Text categorization Big Data Information retrieval Data models classification algorithms Context modeling

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Vehicle classification using compressive sensing

Vehicle classification using compressive sensing

引用

IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： M Uttarakumari Ashray V Achary Sujata D Badiger D S Avinash Anisha Mukherjee Nancy Kothari Dept. of Electronics and Communication Engg. RVCE Bangalore Dept. of Electronics and Communication Engg. RVCE Bangalore India

Compressive Sensing has garnered lot of attention in the fields of Information technology & Signal Processing as it offers an alternate, redundancy-free approach to signal compression and reconstruction. It exploits the `sparsity' of the signal to under-sample the sequence and reconstruct it without adding aliasing noise as established by Shannon-Nyquist sampling theorem. However, the methods of reconstruct are expensive due to the usage of non-linear reconstruction polynomials. This paper draws comparison between existing sparse signal recovery method, Compressed Sampling Matching Pursuit (CoSaMP), to Iterative Soft Thresholding algorithm (IST), and utilizes IST for classification of Vehicles with Acoustic signal source. The signals are sampled at half the Nyquist rate, followed by reconstruction using IST. Various features like mean, variance, skewness, and kurtosis of signal is extracted from multiple transform domains. The extracted features from the reconstructed signal is fed to K-NN classifier which classifies target signal as a bike, car, tractor, or a bus.

关键词： Feature extraction classification algorithms Matching pursuit algorithms Compressed sensing Approximation algorithms Computational complexity

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Remote Sensing Image in Mining Area classification Based on LVQ2 Neural Network

Remote Sensing Image in Mining Area Classification Based on ...

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International Conference on Electrical and Control Engineering (ICECE)

作者： Youjian Hu Hongxia Luo Faculty of Information Engineering China University of Geoscience Wuhan China

The remote sensing shows a widest perspective for land reclamation in mining areas. Based on how to improve the classification accuracy of mine image, we did some classification researchs with LVQ2 neural network. The proposed method had been applied to the aerial image of Heng country, Guangxi Province. The total classification accuracy was 72%, comparing with the minimum distance method increased by 9%.

关键词： Artificial neural networks Remote sensing classification algorithms Image classification Geology Signal processing algorithms Accuracy

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The Study of Fusion Image Block and Sparse Representation classification in Disguised Face Recognition

The Study of Fusion Image Block and Sparse Representation Cl...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Gan Junying Liu Dan Zeng Junying Tian fengxia School of Information Engineering Wuyi University Jiangmen Guangdong China

In this paper, a local SRC algorithm is presented and four fusion algorithms are obtained. This work is intended to get a better result in both recognition rate and speed. Experimental results based on AR Face Databas... 详细信息

关键词： Face recognition Testing Face Training Partitioning algorithms Databases classification algorithms

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Two-Stage classification of Typical Joint Scenarios in Power Systems Based on Improved Iterative Selforganizing Data Analysis Technique Algorithm and Feature Hierarchical Clustering

Two-Stage Classification of Typical Joint Scenarios in Power...

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New Energy and Electrical Technology (ISNEET), International Symposium on

作者： Shuliang Cheng Jiantao Liu Na Li Peng Zhang Kun Yuan Kaifeng Zhang School of Automation Southeast University Nanjing China China Electric Power Research Institute Co. Ltd. Nanjing China Shandong Electric Power Research Institute Jinan China State Grid Jibei Electric Power Co. Ltd. Shijiazhuang China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

With the increasing proportion of renewable energy in the power system, the load, photovoltaic and wind power characteristics show complex dynamic changes, and the traditional single-element scenario classification method is difficult to cope with this challenge. In order to solve this problem, this paper proposes a two-stage typical joint scene classification method for power systems based on improved iterative selforganizing data analysis technique algorithm (ISODATA) and elemental hierarchical clustering. In the first stage, for load, photovoltaic (PV) and wind power time-series data, the improved ISODATA algorithm is used to reconstruct the time-series data by variance and extreme variance thresholds to consider local differences, and combined with the composite Euclidean dynamic time warping (DTW) metrics to measure the overall differences, which optimizes the classification accuracy of the single-element time-series. In the second stage, on the basis of the elemental typical scenes obtained in the first stage, the interrelationships and dynamic change characteristics of load, PV and wind power are analyzed, the relevant feature quantities are set, and the elements are jointly analyzed by using the hierarchical clustering method, and a typical joint scene of multiple elements is finally obtained. The joint scene not only integrates the characteristics of multiple elements, but also is more complex and comprehensive than the single-element scene. The experimental results show that the method proposed in this paper can effectively improve the accuracy of typical scene classification, accurately capture the complex change characteristics of various elements in the power system, and provide strong data support for intelligent scheduling and optimization.

关键词： Photovoltaic systems Renewable energy sources Scene classification Data analysis Heuristic algorithms Power system dynamics Clustering algorithms Microgrids Wind power generation classification algorithms

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