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An SMP Soft Classification algorithm for Remote Sensing 11

An SMP Soft Classification Algorithm for Remote Sensing

引用

Simulation Multiconference

作者： Rhonda D. Phillips Layne T. Watson Randolph H. Wynne MIT Lincoln Laboratory Departments of Computer Science and Math Virginia Polytechnic Institute and State University Forestry Virginia Polytechnic Institute and State University

ISBN: (纸本)9781617828409

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR), a semi-automated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10~8 pixels and 6 bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors.

关键词： Remote sensing Semisupervised clustering Classification IGSCR remote sensing Taxonomy sorting algorithm switched mode power supplies Supralinearity images

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Study of test classification algorithm based on domain knowledge

Study of test classification algorithm based on domain knowl...

引用

Third International Conference on Cyberspace Technology (CCT 2015)

作者： Yutong Gao Feifan Song Xiaqing Xie Qing Sun Xu Wu Department of Computer Science and Information Engineering National Taipei University of Technology China School of Economics andManagement Beijing University of Posts and Telecommunications China Beijing University of Posts and Telecommunications Library China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China

When facing various and massive data resources, how to effectively utilize the resources according to the division field is one of the core problem of the institutional repository research. In this paper, we improved Bayesian classification algorithm, then proposed a text classification algorithm based on domain knowledge. Furthermore, some key technologies such as text classification, feature selection, weight improvement and domain knowledge algorithm improvement are designed and implemented. We use widely applied IkAnalyzer method to classify Chinese words. For feature selection and weight improvement part, we focus on the processing of special vocabulary in the document. We introduce the field expand vocabulary assist the Bayesian formula in the field application part to obtain the final result. The experiment result shows that the improved algorithm enhanced the accuracy of the classification efficiently, and the system calculating time is acceptable.

关键词： Bayesian Classification Domain Knowledge Institutional Repository sorting algorithms Text Classification domain knowledge Text categorization sorting algorithm Feature extraction

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A Wireless Steady State Visually Evoked Potential-based BCI Eating Assistive System

A Wireless Steady State Visually Evoked Potential-based BCI ...

引用

International Joint Conference on Neural Networks

作者： Ching-Yu Chiu Avinash K. Singh Yu-Kai Wang Jung-Tai King Chin-Teng Lin Faculty of Engineering and Information Technology University of Technology National Chiao Tung University

ISBN: (纸本)9781509061839

Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.

关键词： Steady state visually evoked potential (SSVEP) BCI Robotic arm Eating assistive system robotic arm Consumers Physically Handicapped Food Intake Brain Waves Amyotrophic Lateral Sclerosis sorting algorithm Healthy Volunteers wireless

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Estimating the class prior and posterior from noisy positives and unlabeled data 16

Estimating the class prior and posterior from noisy positive...

引用

Annual Conference on Neural Information Processing Systems

作者： Shantanu Jain Martha White Predrag Radivojac Department of Computer Science Indiana University Bloomington Indiana USA

ISBN: (纸本)9781510838819

We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and parametric and nonparametric algorithms proposed here constitute an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.

关键词： sorting algorithm Noise Univariate Transform Parametric Posterior distribution

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Spammer Detection on Weibo Social Network

Spammer Detection on Weibo Social Network

引用

IEEE International Conference on Cloud Computing Technology and Science

作者： Zhipeng Zeng Xianghan Zheng Guolong Chen Yuanlong Yu Coll. of Math. & Comput. Sci. Fuzhou Univ. Fuzhou China

ISBN: (纸本)9781479940929

Social network has become a very popular way for internet users to communicate and interact online. Users spend a great deal of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting their messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behaviors (e.g. post messages containing commercial topics or URLs, following a larger amount of users, etc.), leading to great inconvenience on normal users' social activities. In this paper, a supervised machine learning based spammer filtering method is proposed. We first collected a dataset from Sina Weibo that includes 30,116 users and more than 16 million messages;then, construct a labeled dataset of users and manually classify users into spammers and non-spammers;after that, abstract a set of novel features from message content and users' social behavior, and apply into SVM based spammer classifier. Our experiments show that true positive rate of spammers and non-spammers could reach 99.1% and 99.9%.

关键词： Internet learning (artificial intelligence) pattern classification social networking (online) support vector machines unsolicited e-mail Internet users SVM based spammer classifier Weibo social network event discussion malicious behaviors message content message posting news reading normal user social activities spammer detection supervised machine learning based spammer filtering method user labeled dataset user social behavior Classification algorithms Crawlers Feature extraction Support vector machines Twitter Unsolicited electronic mail machine learning social network spam spammer support vector machine spammer Support Vector Network Unsolicited electronic mail Social Media Social Networks Crawlers sorting algorithm Consumers Pattern recognition Feature extraction Internets TWITTER

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A Classification Method of Current Impulse Waveforms for Non-instrusive Load Monitoring

A Classification Method of Current Impulse Waveforms for Non...

引用

International Conference on Electronics Technology (ICET)

作者： Qifeng Huang Hanmiao Cheng Kaijie Fang Yixuan Huang Shihai Yang Guofang Xia Laboratory of Power Demand Side Management Technology of State Grid State Grid Jiangsu Electric Power Company Marketing Service Center Nanjing China

ISBN: (数字)9781728162836

ISBN: (纸本)9781728162843

Focusing on the current impulse waveforms generated when the fixed or variable frequency air conditioners, vacuum cleaner, microwave oven and other electrical appliances are turned on, the characteristic parameters such as impulse amplitude, rising time, dropping amplitude, and falling time are defined, and a multi-feature Bayesian classification model is established. Considering that the classification algorithm works on the smart meter hardware, the computing and storage resources are limited, as a result that the model scale needs to be reduced as much as possible, a meant parameters Bayesian classification method is proposed. The current impulse waveform samples of different appliances are divided into several groups, and the mean value of the characteristic values of the samples is used as the parameters of the classification model. Under the same model capacity, the generalization error of the model is reduced. The simulation results show that the accuracy of Bayesian classifier constructed with meant value parameters is better than that of ordinary Bayesian classifier. In the laboratory, the impulse classification test is carried out on a single-phase smart meter, and the test results verify the feasibility of the proposed method.

关键词： Bayesian classifier load current impulse meant parameter smart meter non-intrusive load monitoring Smart meters Bayesian classifier Classification Methods load monitoring Pulses model scale sorting algorithm waveforms FALL TIME mean value

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ANDROID APPLICATIONS MALWARE DETECTION: A Comparative Analysis of some Classification algorithms

ANDROID APPLICATIONS MALWARE DETECTION: A Comparative Analys...

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International Conference on Electronics, Computer and Computation (ICECCO)

作者： Oluwaseyi Ezekiel Olorunshola Ayanfeoluwa Oluwasola Oluyomi Electrical Electronics Engineering Department Air Force Institute of Technology Kaduna Nigeria

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151618

The usage of the Android Operating System (OS) has surpassed all other operating systems and as a result, it has become the primary target of attackers. Many attacks can be geared towards Android phones mainly using application installation. These third-party applications first seek permission from the user before installation. Some of the permissions can be elusive evading the users' attention. With the type of harm that can be done which include illegal extraction and transfer of the users' data, spying on the users and so on there is a need to have a heuristic approach in the detection of malware. In this research work, some classification algorithms were tested to determine the best performing algorithm when it comes to the detection of android malware detection. An android application dataset was obtained from figshare and used in the Waikato Environment for Knowledge Analysis (WEKA) for training and testing, it was measured under accuracy, false-positive rate, precision, recall, f-measure, Receiver Operating Curve (ROC) and Root Mean Square Error (RMSE). It was discovered that multi-layer perceptron performs best with an accuracy of 99.4%.

关键词： Android (operating system) invasive software multilayer perceptrons pattern classification smart phones Invasive software Multilayer perceptrons Smartphone Pattern recognition Androids Methyltestosterone Operating systems Spyware permission sorting algorithm Androids Consumers

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Probabilistic Lung Nodule Classification with Belief Decision Trees

Probabilistic Lung Nodule Classification with Belief Decisio...

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

作者： Dmitriy Zinovev Jonathan Feigenbaum Jacob Furst Daniela Raicu DePaul University

ISBN: (纸本)9781424441211

In reading Computed Tomography (CT) scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. Computer-Aided Diagnostic Characterization (CADc) systems can assist radiologists by offering a "second opinion" predicting these semantic characteristics for lung nodules. In this work, we propose a way of predicting the distribution of radiologists' opinions using a multiple-label classification algorithm based on belief decision trees using the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four human radiologists for each one of the 914 nodules. Furthermore, we evaluate our multiple-label results using a novel distance-threshold curve technique - and, measuring the area under this curve, obtain 69% performance on the validation subset. We conclude that multiple-label classification algorithms are an appropriate method of representing the diagnoses of multiple radiologists on lung CT scans when ground truth is unavailable.

关键词： Lung nodules Radiologists sorting algorithm Decision Trees Area Under Curve ground truth Not Available Semantics Beliefs Second Opinion

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MUSIC GENRE CLASSIFICATION VIA SPARSE REPRESENTATIONS OF AUDITORY TEMPORAL MODULATIONS

MUSIC GENRE CLASSIFICATION VIA SPARSE REPRESENTATIONS OF AUD...

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European signal processing conference

作者： Yannis Panagakis Constantine Kotropoulos Gonzalo R. Arce Department of Informatics Aristotle University of Thessaloniki Box 451 Thessaloniki 54124 GREECE Department of Informatics Aristotle University of Thessaloniki Box 451 Thessaloniki 54124 GREECEDepartment of Electrical & Computer Engineering University of Delaware Newark DE 19716-3130 U.S.A Department of Electrical & Computer Engineering University of Delaware Newark DE 19716-3130 U.S.A

ISBN: (纸本)9781617388767

A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of slow temporal modulations of music recordings and the power of sparse representation-based classifiers. Linear subspace dimensionality reduction techniques are shown to play a crucial role within the framework under study. The proposed method yields a music genre classification accuracy of 91% and 93.56% on the GTZAN and the ISMIR2004 Genre dataset, respectively. Both accuracies outperform any reported accuracy ever obtained by state of the art music genre classification algorithms in the aforementioned datasets.

关键词： state of the art Music Frameworks representations Energy sources Dataset sorting algorithm

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Real-Time Human Detection for Robots using CNN with a Feature-Based Layered Pre-filter

Real-Time Human Detection for Robots using CNN with a Featur...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： E. Martinson V. Yalla Toyota InfoTechnology Center Mountain View CA 94043 USA

ISBN: (纸本)9781509039302

Convolutional neural networks (CNNs), in combination with big data, are increasingly being used to engineer robustness into visual classification systems including human detection. One significant challenge to using a CNN on a mobile robot, however, is the associated computational cost and detection rate of running the network. In this work, we demonstrate how fusion with a feature-based layered classifier can help. Not only does score-level fusion of a CNN with the layered classifier improve precision/recall for detecting people on a mobile robot, but using the layered system as a pre-filter can substantially reduce the computational cost of running a CNN - reducing the number of objects that need to be classified while still improving precision. The combined real-time system is implemented and evaluated on a two robots with very different GPU capabilities.

关键词： Image segmentation Classification algorithms Robots Neural networks Graphics processing units Cameras Mathematical model Robot sorting algorithm Cameras image segmentation pre-filter Neural network Graphics Processing Unit Mathematical Model Mobile robots layered systems big data

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