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international conference on pattern recognition

作者： Adriano Mancini Emanuele Frontoni Primo Zingaretti Università Politecnica delle Marche Ancona Italy

the paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary, automatic approaches, based on machine learning and image processing techniques, allow to update large areas in a short time with an accuracy and precision comparable to those obtained by human operators. this work is focused on the road-graph update starting from aerial, multi-spectral data. Georeferenced, ground data, acquired by a GPS and an inertial sensor, are integrated with aerial data to speed up the change detector. After roads extraction by means of a binary AdaBoost classifier, the old road-graph is updated exploiting a particle filter. In particular this filter results very useful to link (track) parts of roads not extracted by the classifier due to the presence of occlusions (e.g., shadows, trees).

关键词： Roads data mining Training Satellites Image resolution Remote sensing Global Positioning System

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Extracting Biomarker Information applying Natural Language Processing and machine learning

Extracting Biomarker Information applying Natural Language P...

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the 4th international conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Md Tawhidul Islam Mostafa Shaikh Abhaya Nayak Shoba Ranganathan Department of Chemistry and Biomolecular Science Biotechnology Research InstituteMacquarie Universi Dept.Of Information and Comm.Engineering University of Tokyo Tokyo Japan Department of Computing Macquarie University Sydney Australia Department of Chemistry and Biomolecular Science Biotechnology Research Institute Macquarie Univer

In this paper, we detail an approach to a very specific task of information extraction namely, extracting biomarker information in biomedical literature. Starting with the abstract of a given publication, we first identify the evaluative sentence(s) among other sentences by recognizing words and phrases in the text belonging to semantic categories of interest to bio-medical entities (i.e., semantic category recognition). For the entities like, protein, gene and disease, we determine whether the statement refers to biomarker relationship (i.e., assertion classification). Finally, we identify the biomarker relationship among the biomedical entities (i.e., semantic relationship classification). the system, Biomarker Information Extraction Tool (BIET) implements machine learning-based biomarker extraction using support vector machines (SVM). the system is trained and tested on a corpus of oncology related PubMed/MEDLINE literatures hand-annotated with biomarker information. We investigate the effectiveness of different features for this task and examine the amount of training data needed to learn the biomarker relationship with the entities. Our system achieved an average Fscore of 86% for the task of biomarker information extraction comparing to the human annotated dataset (i.e. gold standard) scores.

关键词： data mining Biomarkers Natural language processing machine learning Text recognition Support vector machines Support vector machine classification Proteins Diseases System testing

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Artificial immune system based machine learning for voltage stability prediction in power system

Artificial immune system based machine learning for voltage ...

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international Power Engineering and Optimization conference (PEOCO)

作者： S. I. Suliman T. K. Abdul Rahman Faculty of Electrical Engineering Universiti Teknologi Mara Malaysia

Voltage instability has recently become a challenging problem for many power system operators. this phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. this paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. the proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained.

关键词： Stability criteria Power system stability Artificial neural networks Indexes pattern recognition

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A methodology for integrating and exploiting data mining techniques in the design of data warehouses

A methodology for integrating and exploiting data mining tec...

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international conference on Advanced Information Management and Service

作者： Muhammad Usman Russel Pears School of Computing and Mathematical Sciences Auckland University of Technology Auckland New Zealand

data Warehousing and data mining are two mature disciplines in their own right. Yet, they have developed largely separate from each other, despite the fact that techniques developed for pattern recognition such as Clustering and Visualization in the data mining discipline have much to offer in the design of data Warehouses. this is somewhat surprising, given that the two disciplines have broadly the same set of objectives, although the techniques that they employ are admittedly quite different from each other. this may be due to the lack of a suitable methodology for integrating methods such as clustering and pattern visualization into data warehousing design. In this research, we propose such a methodology and report on its application to two case studies involving real world data taken from the UCI machine learning repository. We demonstrate how data clustering and visualization methods, working in conjunction with each other can be used to gain new insights and build more meaningful dimensions which may not be obvious to human data warehouse designers.

关键词： data visualization data mining data warehouses Warehousing Clustering algorithms Algorithm design and analysis Education

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Sunday school-work times of teachers in Tasmania, Australia

Sunday school-work times of teachers in Tasmania, Australia

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international conference on Distance learning and Education (ICDLE)

作者： Elkana Ngwenya Australian Maritime College University of Tasmania Australia

In most occupations there has been an increasing tendency to work unsociable hours, as a result of reforms. In the case of teaching, these unsociable hours have included after hours work and weekend work, and have created significant intensification of work. Little is known, however, about the Sunday work ethic that results from the intensification of teachers' work. In this study, data on teachers' workloads are examined for correlations between Sunday times and time allocated to other days of the week. An Ordinary Least Squares (OLS) regression equation that relates Sunday time-use to teacher and school characteristics is estimated. Results suggest that selected school and teacher characteristics affect the number of hours expended on Sunday school work by teachers in Tasmania. the absence of breaks during regular school time, and the extent to which teachers are overloaded, are particularly significant in increasing Sunday work time of teachers in Tasmania. these results have implications for teacher workloads, and future school reforms in Tasmania.

关键词： Educational institutions pattern recognition

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Mutual Information based recognition of Pulse Signal in TCM

Mutual Information based Recognition of Pulse Signal in TCM

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the 4th international conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Yong Shen Chiheng Zhou Chunming Xia Jianjun Yan Rui Guo Haixia Yan Fufeng Li Yiqin Wang Center for Mechatronics Engineering East China University of Science and Technology Shanghai 200237 Center for TCM information science and technology Shanghai University of TCM Shanghai 201203 P.R.Ch

this study aims at utilizing mutual information algorithm to make an objective and quantitative research for the pulses in Traditional Chinese Medicine (TCM) diagnosis. the normal pulse signals, slippery pulse signals and taut pulse signals were collected from the outpatients by Shanghai University of TCM. Some typical pulse signals from all these classes were selected as the templates to be matched with the new pulse signals by means of mutual information, and then which class the new pulse signal should be classified was determined through the maximum mutual information. Finally, the satisfactory results were obtained which proof that it was appropriate to use mutual information to recognize pulse signals. the biggest advantage of this way is simple, since the inputted pulse signals should not be extracted features. And the time-domain features were extracted from the pulse signals to be classified. the results from the two different ways show the classification method presented is efficient based on Mutual Information.

关键词： Mutual information Feature extraction Random variables data mining Support vector machines Support vector machine classification Neural networks Medical diagnostic imaging Time domain analysis pattern recognition

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A Svm-Based Algorithm for Automatic Species Classification of a Marine Diatom Genus Coscinodiscus Ehrenberg

A Svm-Based Algorithm for Automatic Species Classification o...

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international conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Jinfei Luo Qiaoqi Luo Yahui Gao Changping Chen Junrong Liang Chenhui Yang Sch. of Life Sci. Xiamen Univ. Xiamen China Sch. of Inf. Sci. & Technol. Xiamen Univ. Xiamen China

Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. these properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However, it proves to be complicated task because of the high spatial variability of ornamentation properties. Researchers have shown a Teach-program and a number of library functions operating on sample image lists (SIL's) and operating on classifiers (CF's) to solve the problem. In this paper, we present Coscinodiscus Ehrenberg ornamentation classifier algorithm called support vector machines (SVMs) to derive a new set of SIL's and CF's. the principal purpose of SVMs is Coscinodiscus Ehrenberg images pattern recognition approach. A pattern is in this context always the SIL's contained in a sub-rectangle of some given (possibly larger) image. For the same classifier this sub-rectangle must always have the same dimensions, while the query image to be searched may be arbitrarily large. the training is done by preparing SIL's for the pattern taxa in question and feeding them to CF's created. Our classifier generation with preprocessing code optimization achieves a AAAAA preprocessing code, a 0.981 learning success, a 100% computational complexity. Train with SIL's achieves 212 samples, 17 taxa, a 0.472% error rate and Test with Query image searching achieves 253 samples, 17 taxa, a 15.81% error rate. the experiments demonstrate that the proposed method is very robust to the threshold segmentation and ornamentation feature extraction of Coscinodiscus Ehrenberg images, and is effective and useful for species classification of Coscinodiscus Ehrenberg.

关键词： Feature extraction Error analysis Shape Libraries Support vector machines Support vector machine classification pattern recognition Computational complexity Testing Robustness

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MICAI 2010 Organization and conference Committee

Proceedings of Special Session - 9th Mexican International C...

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Proceedings of Special Session - 9th Mexican international conference on Artificial Intelligence: Advances in Artificial Intelligence and Applications, MICAI 2010 2010年 viii页

作者： Reyes-García, Carlos Alberto Sidorov, Grigori Hernández-Aguirre, Arturo Arroyo, Gustavo Murrieta, Rafael Gonzalez, Jesus A. Gonzalez, Miguel Herrera, Oscar Peña, Alejandro Espinoza, Félix A. Castro Cansino, Joel Suárez Galicia-Haro, Sofia N. Koeppen, Mario Reyes-García, Carlos A. Monroy, Raul Gelbukh, Alexander Mezura-Montes, Efrén Leguizamón, Guillermo Ramírez-Manzanares, Alonso Castillo, Oscar Fuentes, Olac Sánchez, Gildardo Natural Language Processing Mexico Machine Learning and Pattern Recognition Mexico Hybrid Intelligent Systems and Neural Networks Mexico Logic Reasoning Ontologies Knowledge Mgmt. Knowledge-Based Syst. Multi-agent Syst. Mexico Data Mining Mexico Intelligent Tutoring Systems Mexico Evolutionary Algorithms and Other Naturally Inspired Algorithms Mexico Computer Vision and Image Processing Mexico Fuzzy Logic Uncertainty and Probabilistic Reasoning Mexico Bioinformatics and Medical Applications Mexico Robotics Planning and Scheduling Mexico

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thalamic Bursts Mediate pattern recognition

Thalamic Bursts Mediate Pattern Recognition

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4th international IEEE/EMBS conference on Neural Engineering

作者： Jandel, Magnus Swedish Def Res Agcy Stockholm Sweden

ISBN: (纸本)9781424420728

A new functional model for burst firing in the dorsal thalamus is proposed where thalamocortical pattern recognition systems, based on kernel machine principles, are connected by burst signaling. the systems include input trapping in the dorsal thalamus, cortical learning state memory and processing in the thalamic reticular nucleus. Misclassified events are captured as training examples in the waking state and the pattern recognition systems are trained by extensive thalamic bursting in deep sleep.

关键词： thalamus thalamic reticular nucleus neocortex pattern recognition kernel machine support vector

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CLASSIFICATION OF FACIAL EXPRESSIONS USING data mining AND machine learning ALGORIthMS 4

CLASSIFICATION OF FACIAL EXPRESSIONS USING DATA MINING AND M...

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4th Iberian conference on Information Systems and Technologies

作者： Faria, Brigida Monica Lau, Nuno Reis, Luis Paulo Univ Aveiro Dept Elect Telecomunicacoes & Informat Aveiro Portugal IPP ESTSP Oporto Portugal IEETA Aveiro Portugal Univ Porto Fac Engn DEI Oporto Portugal Univ Porto LIACC Oporto Portugal

ISBN: (纸本)9789899624702

machine Facial recognition is a problem that consists in exploring still or video images of a scene to identify or verify one or more persons in the scene using stored databases of faces. Research in Facial recognition is multidisciplinary including disciplines like pattern recognition, image processing and analysis, computer graphics, machine learning and data mining. Sometimes applications include not only facial recognition but also facial expression characteristics identification and classification like blinking of the eyes, open/close mouth among others. this paper presents a comparative study of three algorithms for classification basic facial expressions.

关键词： data mining OneR Naive Bayes Support Vector machines Facial recognition

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