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Novel algorithm for RS Image classification

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2014年第2期42卷 435-437页

作者： Bharatkar, Pravada S. Patel, Rahila Durbude, Dilip G. RCERT Nagpur Univ Dept Comp Sci Chandrapur India Natl Inst Hydrol Environm Hydrol Div Roorkee Uttar Pradesh India

In the present era of modern technology, the efficacy and accuracy of output is demanding. Based on rigorous survey, Bharatkar and Patel (International Journal of Advanced Research in Computer Science 3(7):218-223, 2012) concluded that the incorporation of Block Truncation Coding (BTC) approach in the existing image classification algorithm can be used to improve the classification accuracy. Therefore, in the present study, an effort has been made to explore the Content Based Remote Sensing Image (CBRSI) classification algorithm to enhance classification accuracy with BTC approach. It is revealed from the study that BTC based maximum likelihood classifier gives better overall accuracy and kappa statistics.

关键词： Remote sensing classification algorithm Satellite image BTC etc.

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Implementation of a Power Quality Signal classification System using Wavelet based Energy Distribution and Neural Network

Implementation of a Power Quality Signal Classification Syst...

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Proceedings of the 2015 IEEE International Conference On Power and Advanced Control Engineering ICPACE 2015

作者： Sebastian, Praveen Dsa, Pramod Antony Manipal Inst Technol Dept Elect Engn Manipal 576104 Karnataka India

ISBN: (纸本)9781479983711

This paper presents a classification system based on Wavelet Transform for normal Power Quality disturbances. The parameter used for the classification algorithm was energy distribution of detailed coefficient of Wavelet Transform up to 10 leveles of decomposition. Wavelet Transform parameters are effective in classification of disturbance waveforms. Artificial Neural Network was used for the classification purpose which gave satisfactory results. And the algorithm developed in MATIAB was interfaced with Data Acquisition devices to check its accuracy for online classification purpose.

关键词： Power Quality Wavelet Transform Wavelet Energy classification Artificial Neural Network Online classification classification algorithm

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A New classification Method Based on Semi-supervised Support Vector Machine 1

A New Classification Method Based on Semi-supervised Support...

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1st International Conference on Human Centered Computing (HCC)

作者： Jiang, Weijin Yao Lina Jiang Xinjun Xu Yuhui Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China Hunan Univ Commerce Sch Comp & Informat Engn Changsha Hunan Peoples R China Hunan Radio & TV Univ Dept Comp Changsha 410005 Hunan Peoples R China

ISBN: (纸本)9783319155548;9783319155531

Semi-supervised learning using tag vector machine is a relatively new method of data classification and label-free. Semi-supervised support vector machines model the objective function is not smooth and fast optimization algorithm to solve the model cannot be applied. This paper presents a general three-moment method 3 times differentiable at the origin of construct quintic spline functions, construction of hinge can be used to approximate symmetry loss functions, the approximate accuracy estimation of and quintic spline functions. And on top of this, deduced five and a half times b-spline smoothing support vector machines for non-smooth a-smoothing model analyses the convergence. Broyden-Fletcher-Goldfarb-Shanno (storage) algorithm can be used in new models. Experimental results show that the new model has a better performance.

关键词： classification algorithm Smooth Spline unction Semi-supervised support vector machine

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Deriving the Relationship Between User Satisfaction on Engine Sounds and Affective Variable Sets Based on classification algorithms

Deriving the Relationship Between User Satisfaction on Engin...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Kim, W. J. Kim, G. W. Lee, Y. S. Yun, M. H. Seoul Natl Univ Dept Ind Engn Seoul South Korea

ISBN: (纸本)9781479964109

This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.

关键词： Emotion prediction product sounds classification algorithm user satisfaction engine sounds

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classification for safety-critical car-cyclist scenarios using machine learning 18

Classification for safety-critical car-cyclist scenarios usi...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Cara, Irene de Gelder, Erwin TNO POB 756 NL-5700 AT Helmond Netherlands

ISBN: (纸本)9781467365963

The number of fatal car-cyclist accidents is increasing. Advanced Driver Assistance Systems (ADAS) can improve the safety of cyclists, but they need to be tested with realistic safety-critical car-cyclist scenarios. In order to store only relevant scenarios, an online classification algorithm is needed. We demonstrate that machine learning techniques can be used to detect and classify those scenarios based on their trajectory data. A dataset consisting of 99 realistic car-cyclist scenarios is gathered using an instrumented vehicle. We achieved a classification accuracy of the gathered data of 87.9%. The execution time of only 45.8 mu s shows that the algorithm is suitable for online purposes.

关键词： automobiles bicycles feature extraction intelligent transportation systems learning (artificial intelligence) pattern classification road accidents ADAS advanced driver assistance system car-cyclist accident classification algorithm machine learning safety-critical car-cyclist scenario Accuracy classification algorithms Hidden Markov models Machine learning algorithms Support vector machines Trajectory Vehicles

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OCR for devanagari numerals using zonal histogram of angle

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2017年第4期20卷 519-534页

作者： Johnson, Kent Gourav, Kumar Gaurav Rudrapal, Dwijen Debnath, Sanjib Natl Inst Technol Agartala Dept Comp Sci & Engn Jirania 799046 Tripura India

Optical Character Recognition (OCR) system is used to generate the textual representation of handwritten or printed text. Many research works are going on in the field of OCR over the past few decades for most of the Indian scripts. Devanagari one of the most spoken languages in the world as well as India. Lack of a robust OCR system for Devanagari script is still there even after so much research. The aim of this paper is to make an OCR that could classify handwritten Devanagari numerals. This paper proposes an OCR based on Histogram of the angle made by a dark pixel with the zonal center of mass. This feature bags the angle made by each dark pixel in a zone about its center of mass. This newly extracted feature was used to train various classification algorithms like K-Nearest Neighbor, SVM, Linear SVM, Random Forest, Decision Tree, Gradient Boosting, Gaussian Naive Bayes. We reported an efficiency of each algorithm based on the new feature. Our experiment result shows that the Random Forest Model outperforms over the other algorithms and reports an efficiency of 92.57%.

关键词： Devanagari Numeral OCR Angular Histogram Machine learning algorithms Feature extraction classification algorithm

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Developing an approach to evaluate stocks by forecasting effective features with data mining methods

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1325-1339页

作者： Barak, Sasan Modarres, Mohammad Islamic Azad Univ Young Researcher Club Ardebil Branch Ardebil Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering;the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Stock market Data mining classification algorithm Feature selection Function-based clustering method

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An Extraction Method of Hyponymy based on Multiple Data Sources Fusion

An Extraction Method of Hyponymy based on Multiple Data Sour...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Shuqi Wang Xiao Feng Shuwu Zhang Chengcheng Yin Dearpartment of Signal and Information Systems Communication University of China Institute of Automation Chinese Academy of Sciences

Hyponymy is one of the most critical semantic relations,which contributes magnificently to semantic dictionary,information retrieval *** this paper,a method of extracting hyponymy is proposed based on multiple data sources fusion,which convert the extraction of hyponymy to the extraction of hypernyms for target ***,mining candidate hypernyms for the target words based on search engine,encyclopedia resources and core suffix ***,fusing the candidates from the above data *** last,the classification algorithm is used to filter the noise and extract the hypernyms,which is a quite mature machine learning *** is hyponymy between the target words and their correctly extracted *** experimental results show that the highest accuracy rate of hyponymy extraction reaches 0.832 using the proposed method.

关键词： hyponymy multiple data dources fusion mining candidate hypernyms classification algorithm hypernym extraction

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Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

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ECOLOGICAL INDICATORS 2015年 54卷 116-123页

作者： Jin, Pingbin Li, Pingheng Wang, Quan Pu, Zhi Zhejiang Univ Dept Geosci Hangzhou 310027 Zhejiang Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 830011 Peoples R China Shizuoka Univ Grad Sch Agr Shizuoka 4228529 Japan

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), insitu field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multipletype salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Arid land Soil salinity Salt type Hyperspectral Feature parameter classification algorithm

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Applied Biological Data Mining Based on Improved K-means Clustering algorithm And KNN Classifier in Protein Sub-cellular Localization

Applied Biological Data Mining Based on Improved K-means Clu...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Zhenfeng Lei Shunfang Wang School of Information Science and Engineering Yunnan University

With the completion of the human genome sequencing,a large number of data especially amino acid sequences floods into biological *** to analyze these data quickly and even predict the structure and function of protein correctly have become hot topics in recent *** this paper,we mainly study K-means clustering algorithm and KNN classifier in amino acid sequences of complicated data,which are applied in the prediction of protein sub-cellular *** many cases,fuzzy boundary and unbalance are frequently appeared among biological *** accuracy will be lower,if we make a prediction through traditional KNN and K-means clustering algorithm ***,in order to make clear the unbalance,we propose the within-class thought to make sure that training samples in each class around the testing sample are selected and we introduce membership to tell which class the testing sample belongs ***,we bring in rough sets and membership to solve the fuzzy ***,we apply correlation coefficient in the rough sets to better reflect the relationship among data *** experimental results based on protein sub-cellular localization prediction show that the methods proposed newly better work than the traditional methods.

关键词： data mining clustering algorithm classification algorithm protein sub-cellular localization

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