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Evaluation of Supervised Classification algorithms for Human Activity Recognition with Inertial Sensors

Evaluation of Supervised Classification Algorithms for Human...

引用

IEEE Sensors Conference

作者： Tahmina Zebin Patricia J. Scully Krikor B. Ozanyan School of EEE University of Manchester UK School of CEAS University of Manchester UK

ISBN: (纸本)9781509010134

The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other known activity classification algorithms. A parallel coordinate plot based on visualization of features is used to identify useful features or predictors for separating classes. This enabled exclusion of features that contribute least to classification accuracy in a multi-sensor system (five in our case), made the classifier lightweight in terms of number of useful features, training time and computational load and lends itself to real-time implementation.

关键词： Human Activity Recognition (HAR) Classification Feature Selection Supervised Machine Learning MATLAB sorting algorithm Feature extraction MATLAB inertia sensor Sensor Data Activity recognition Human Activities Classifiers bagging PURSE Support Vector Network Recognition (Psychology)

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Automatic Error Detection from Pointing Device Input Data 13

Automatic Error Detection from Pointing Device Input Data

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Annual Meeting of The American Society for Information Science and Technology

作者： Afroza Sultana Karyn Moffatt McGill University

ISBN: (纸本)9780877155454

Although interaction techniques have been extensively studied under controlled laboratory conditions, little is known about their capabilities during unconstrained free tasks. Understanding real world use is particularly important for older adults, as some may find computer input more challenging due to age-related declines in motor skill. As a step towards addressing this gap, we studied the feasibility of using machine-learning techniques. In this study, we emphasized on identifying errors from submovement behavior, using pen-based data from younger and older adults. We tested four machine-learning algorithms: Decision Trees, Neural Networks, Naive Bayesian Networks, and Rule Induction. Each yielded an accuracy rate around 90%. Moreover, the experiments have identified some useful metrics to classify errors among older adult users.

关键词： Error modeling Classification algorithms Performance measurement Pen based pointing devices Sub-movement analysis sorting algorithm Performance metrics error modeling accuracy rate pointing devices aged person Rule induction Error detection Motor Skills Bayesian networks

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Comparison of Artificial Intelligence algorithms for ELINT Signals Classification

Comparison of Artificial Intelligence Algorithms for ELINT S...

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New Trends in Signal Processing (NTSP)

作者： Jozef Perdoch Stanislava Gazovova Zdenek Matousek Jan Ochodnicky Armed Forces Academy of gen. M. R. Stefanik Liptovsky Mikulas Slovakia

ISBN: (数字)9781728161556

ISBN: (纸本)9781728161563

Ability to automatically identify objects of interest and to automatically classify their signals belongs to essential functionalities of electronic intelligence systems. Objects identification results and signals classification results are conditioned by accurate measurement and technical analysis of objects signal parameters. Classification algorithm based on Feedforward Neural Network, and classification algorithms based on Support-Vector Machine, with three types of Kernel Function, are tested and compared in this paper as the first stage of objects identification in electronic intelligence systems.

关键词： classification algorithms signal analysis electronic intelligence feedforward neural networks support vector machine feedforward neural nets Support Vector Network Signal analysis sorting algorithm Taxonomy signals AUTOMATIC precise measurement

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Classification of Wheelchair Commands using Brain Computer Interface: Comparison between Able-Bodied Persons and Patients with Tetraplegia

Classification of Wheelchair Commands using Brain Computer I...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Rifai Chai Sai Ho Ling Gregory P. Hunter Yvonne Tran Hung T. Nguyen Key Centre for Health Technologies Faculty of Engineering and Information Technology University

ISBN: (纸本)9781457702150

This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward;these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.

关键词： Quadriplegia Particle swarm optimization Paralysis Brain-Computer Interfaces commands Time windows sorting algorithm Wheelchair Patients Religious Missions

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Intrinsic prior for Bayesian classification of texture images

Intrinsic prior for Bayesian classification of texture image...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Aurelien Schutz Lionel Bombrun Yannick Berthoumieu Universite de Bordeaux Institut Polytechnique de Bordeaux Laboratoire IMS Groupe Signal et Image

ISBN: (纸本)9781479928941

This paper introduces an intrinsic prior distribution for supervised classification of texture images. First, we introduce the intrinsic prior distribution as the normal law on a Riemannian manifold. Next, based on this definition, we derive the estimation and classification schemes. Finally, we propose an application for the classification of texture images. Experiments on the VisTex texture database are conducted and demonstrate the interest of the proposed intrinsic classification algorithm.

关键词： GRAIN PATTERN Prior distribution sorting algorithm Image classification Bayesian inference

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Fault Location Using Wavelet Transform and Independent Component Analysis

Fault Location Using Wavelet Transform and Independent Compo...

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Guilherme T. Alencar Ricardo C. Santos Aline O. Neves Universidade Federal do ABC Santo André São Paulo Brazil

ISBN: (纸本)9781728131863

This paper discusses Wavelet Transform and Independent Component Analysis methods for fault location in transmission lines. Traveling wave principle is used for measuring signals at the transmission line terminals. It is also shown a fault detection and classification algorithm for the pre-location step. The results are promising in all presented steps. In the fault location step the results considering two different methods are compared and the observed errors are within 2%.

关键词： fault diagnosis fault location independent component analysis power transmission faults power transmission lines wavelet transforms Fault location wavelet transforms Independent component analysis Power transmission lines Fault diagnosis Defect detection Transmission Lines sorting algorithm

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DETECTION OF MALICIOUS URLs IN BIG DATA USING RIPPER algorithm

DETECTION OF MALICIOUS URLs IN BIG DATA USING RIPPER ALGORIT...

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

作者： Sonika Thakur Er. Meenakshi Akansha Priya Centre for Computer Science & Technology Central University of Punjab

ISBN: (纸本)9781509037056

'Big Data' is the term that describes a large amount of datasets. Datasets like web logs, call records, medical records, military surveillance, photography archives, etc. are often so large and complex, and as the data is stored in Big Data in the form of both structured and unstructured therefore, big data cannot be processed using database queries like SQL queries. In big data, malicious URLs have become a station for internet criminal activities such as drive- bydownload,information warfare, spamming and phishing. Malicious URLs detection techniques can be classified into Non-Machine Learning (e.g. blacklisting) and Machine learning approach (e.g. data mining techniques). Data mining helps in the analysis of large and complex datasets in order to detect common patterns or learn new *** data is the collection of large and complex datasets and the processing of these datasets can be done either by using tool like Hadoop or data mining algorithms. Data mining techniques can generate classification models which is used to manage data, modelling of data that helps to make prediction about whether it is malicious or legitimate. In this paper analysis of RIPPER i.e. JRip data mining algorithm has been done using WEKA tool. A training dataset of 6000 URLs has been made to train the JRip algorithm which is an implementation of RIPPER algorithm in WEKA. Training dataset will generate a model which is used to predict the testing dataset of 1050 URLs. Accuracy are calculated after testing process. Result shows JRip has an accuracy of 82%.

关键词： Training Data mining Classification algorithms Testing Big Data algorithm design and analysis Feature extraction big data algorithm design and analysis data mining sorting algorithm scarifier Uniform Resource Locator Dataset Feature extraction Functional training

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SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING FOR HYPERSPECTRAL IMAGE

SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiangrong Zhang Peng Weng Jie Feng Erlei Zhang Biao Hou Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University

ISBN: (纸本)9781479911127

In this paper, a novel hyperspectral image classification method is proposed, based on group sparse coding. The method is based on this acknowledgement that larger spatial variation exists in high spatial resolution hyperspectral image, which degrades the separability of hyperspectral image. In order to obtain a smooth representation, each pixel and its spatial neighbors are coded together by group sparse coding. Although nothing about class information is included, the neighbor pixels in a small spatial window are inclined to belong to the same class. Thus, that will reduce the within-class scatter and be favorable to the classification task. Then, the obtained sparse representation vectors are used for hyperspectral image classification with SVM. Experimental results show that our method exceeds the classical classification algorithms in accuracy and regional consistency.

关键词： Hyperspectral image classification Spatial variance Group sparse Spatial-spectral hyperspectral imagery Image classification Taxonomy Sparse coding sorting algorithm images Classification Methods Spatial variations pixel

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Improved waveform design for non-Gaussian target classification in cognitive radar

Improved waveform design for non-Gaussian target classificat...

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IET International Radar Conference 2015

作者： Zhou Rui Zhou Bo Liu Lin Zhang Xiaofen Jiao Bin Aviation University of Air Force Changchun 130022 China Luoyang Electronic Equipment Testing Center Luoyang 471003 China

Because the performance of traditional Gaussian cognitive radar(CR) system for target response varying from Gaussian will be degraded, this paper extended the Gaussian target response to arbitrary non-Gaussian target distribution. In this paper, the CR multiple hypothesis classification algorithm was used for non-Gaussian targets, and the sparse spectrum of correlated narrowband target responses was utilized. In addition, we simulated the multiple nonGaussian distribution targets, and the simulation results demonstrate the non-Gaussian target classification algorithm is very effective.

关键词： Cognitive Radar Non-Gaussian Target Waveform Design Non-Gaussian Targets Gauss Simulation results sorting algorithm waveforms

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VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MARGIN CLUSTERING APPROACH

VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MAR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Phillip De Leon Salvador Sanchez New Mexico State University Klipsch School of Electrical and Computer Engineering Las Cruces New Mexico U.S.A.

ISBN: (纸本)9781479903573

Recently, an unsupervised, data clustering algorithm based on maximum margin, i.e. support vector machine (SVM) was reported. The maximum margin clustering (MMC) algorithm was later applied to the problem of voice activity detection, however, the application did not allow for real-time detection which is important in speech processing applications. In this paper, we propose a voice activity detector (VAD) based on a sliding window, MMC algorithm which allows for real-time detection. Our system requires a separate initialization stage which imposes an initial detection delay, however, once initialized the system can operate in real-time. Using TIMIT speech under several NOISEX-92 noise backgrounds at various SNRs, we show that our average speech and non-speech hit rates are better than state-of-the-art VADs.

关键词： Speech analysis Classification algorithms Speech analysis sorting algorithm real-time detection Support Vector Network Voice Speech processing Cluster Analysis algorithms activity METAL MATRIX COMPOSITES Mitocin-C

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