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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是481-490 订阅

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pattern recognition in bioinformatics - 9th iapr international conference, PRIB 2014, Proceedings

Pattern Recognition in Bioinformatics - 9th IAPR Internation...

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9th iapr international conference on pattern recognition in bioinformatics, PRIB 2014

ISBN: (纸本)9783319091914

the proceedings contain 11 papers. the topics discussed include: acquiring decision rules for predicting AMES-negative hepatocarcinogens using chemical-chemical interactions;using topology information for protein-protein interaction prediction;biases of drug-target interaction network data;logol: expressive pattern matching in sequences. application to ribosomal frameshift modeling;evolutionary algorithm based on new crossover for the biclustering of gene expression data;SFFS-SW: a feature selection algorithm exploring the small-world properties of GNs;CytomicsDB: a metadata-based storage and retrieval approach for high-throughput screening experiments;CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory Networks from expression data using random forest;supervised selective kernel fusion for membrane protein prediction;and analysis of miRNA expression profiles in breast cancer using biclustering.

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New methods of pattern analysis in the study of Iris Anderson-Fisher Data

New methods of pattern analysis in the study of Iris Anderso...

引用

international conference on Computers Communications and Control (ICCCC)

作者： Alexey Myachin National Research University Higher School of Economics NRU HSE Institute of Control Science of Russian Academy of Science ICS RAS Moscow Russian Federation

A new method of pattern analysis, based on paired index comparison is introduced. Key properties of the method are described. the effectiveness is demonstrated on the Iris Anderson-Fisher Data.

ISBN: (纸本)9781509017362

A new method of pattern analysis, based on paired index comparison is introduced. Key properties of the method are described. the effectiveness is demonstrated on the Iris Anderson-Fisher Data.

关键词： Iris recognition Iris pattern analysis Computers Clustering algorithms Complexity theory Image color analysis

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Biologically inspired pattern recognition for E-nose sensors 12th

Biologically inspired pattern recognition for E-nose sensors

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12th international conference on Advanced Data Mining and Applications, ADMA 2016

作者： Al-Maskari, Sanad Guo, Wenping Zhao, Xiaoming School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia College of Computer and Information Hohai University Nanjing210098 China Institute of Intelligent Information Processing Taizhou University Taizhou317000 China

ISBN: (纸本)9783319495859

the high sensitivity, stability, selectivity and adaptivity of mailman olfactory system is a result of a large number of olfactory receptors feeding into an extensive layers of neural processing units. Olfactory receptor cells (ORC) contribute significantly in the sense of smells. Bloodhounds have four billion ORC making them ideal for tracking while human have about 30 million ORC. E-nose stability, sensitivity and selectivity have been a challenging task. We hypothesize that appropriate signal processing with an increase number of sensory receptors can significantly improve odour recognition in e-nose. Adding physical receptors to e-nose is costly and can increase system complexity. therefore, we propose an Artificial Olfactory Receptor Cells Model (AORCM) inspired by neural circuits of the vertebrate olfactory system to improve e-nose performance. Secondly, we introduce and adaptation layer to cope with drift and unknown changes. the major layers in our model are the sensory transduction layer, sensory adaptation layer, artificial olfactory receptors layer (AORL) and artificial olfactory cortex layer (AOCL). Each layer in the proposed system is biologically inspired by the mammalian olfactory system. the experiments are executed using chemo-sensory arrays data generated over three-year period. the propose model resulted in a better performance and stability compared to other models. To our knowledge, e-nose stability, selectivity and sensitivity are still unsolved problem. Our paper provides a new approach in improving e-nose pattern recognition over long period of time. © Springer international Publishing AG 2016.

关键词： Stability

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Stabilizing linear prediction models using autoencoder 12th

Stabilizing linear prediction models using autoencoder

引用

12th international conference on Advanced Data Mining and Applications, ADMA 2016

作者： Gopakumar, Shivapratap Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Burwood Australia

ISBN: (纸本)9783319495859

To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is often overlooked in favour of performance. Yet, stability prevails as key when adopting models in critical areas as healthcare. Our study proposes a stabilization scheme by detecting higher order feature correlations. Using a linear model as basis for prediction, we achieve feature stability by regularizing latent correlation in features. Latent higher order correlation among features is modelled using an autoencoder network. Stability is enhanced by combining a recent technique that uses a feature graph, and augmenting external unlabelled data for training the autoencoder network. Our experiments are conducted on a heart failure cohort from an Australian hospital. Stability was measured using Consistency index for feature subsets and signal-to-noise ratio for model parameters. Our methods demonstrated significant improvement in feature stability and model estimation stability when compared to baselines. © Springer international Publishing AG 2016.

关键词： Stability

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Subquadratic-time algorithms for abelian stringology problems 6th

Subquadratic-time algorithms for abelian stringology problem...

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6th international conference on Mathematical Aspects of Computer and Information Sciences, MACIS 2015

作者： Kociumaka, Tomasz Radoszewski, Jakub Wiśniewski, Bartlomiej University of Warsaw Warsaw Poland

ISBN: (纸本)9783319328584

We propose the first subquadratic-time algorithms to a number of natural problems in abelian pattern matching (also called jumbled pattern matching) for strings over a constant-sized alphabet. Two strings are considered equivalent in this model if the numbers of occurrences of respective symbols in both of them, specified by their so-called Parikh vectors, are the same. We propose the following algorithms for a string of length n: Counting and finding longest/shortest abelian squares in O(n2/log2⁡n) time. Abelian squares were first considered by Erdös (1961);Cummings and Smyth (1997) proposed an O(n2)-time algorithm for computing them. Computing all shortest (general) abelian periods in O(n2/√logn) time. Abelian periods were introduced by Constantinescu and Ilie (2006) and the previous, quadratic-time algorithms for their computation were given by Fici et al. (2011) for a constant-sized alphabet and by Crochemore et al. (2012) for a general alphabet. Finding all abelian covers in O(n2/log⁡n) time. Abelian covers were defined by Matsuda et al. (2014). Computing abelian border array in O(n2/log2n) time. this work can be viewed as a continuation of a recent very active line of research on subquadratic space and time jumbled indexing for binary and constant-sized alphabets (e.g., Moosa and Rahman, 2012). All our algorithms work in linear space. © Springer international Publishing Switzerland 2016.

关键词： pattern matching

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Investigation of Adaptive Local threshold Segmentation in Context of 3D-Handwriting Forensics 6

Investigation of Adaptive Local Threshold Segmentation in Co...

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6th international conference on Image Processing theory, Tools and Applications (IPTA)

作者： Kalbitz, Michael Scheidat, Tobias Vielhauer, Claus Univ Appl Sci Brandenburg Dept Informat & Media Magdeburger Str 50 D-14770 Brandenburg Germany Otto Von Guericke Univ Fac Comp Sci Univ Pl 2 D-39106 Magdeburg Germany

ISBN: (纸本)9781467389105

Image segmentation plays an important role in digitized crime scene forensics. Particularly in context of modern high resolution contact-less and non-destructive acquisition and analysis of handwriting impression traces by means of 3D sensors, one main challenge is the separation of writing trace areas and non-traces by image segmentation. In earlier work authors have presented the general, yet qualitative feasibility to do so by an initial processing pipeline based on data acquisition, pre-processing and a global segmentation approach. However, quantitative measurements with regards to the segmentation quality have not been studied yet, as well as the discussion of alternative strategies for 3D image segmentation in this scenario. In this paper, we extent the earlier work by introducing a concept for benchmarking segmentation accuracy for 3D handwriting traces. Further we present results with regards to the initial approach as well as a new, adaptive local threshold segmentation. the benchmarking is based on ground truth data, determined using data of handwriting traces acquired by a high-quality flatbed scanner and segmentation information retrieved from those by means of an Otsu operator. this ground truth allows for calculation of true positive, true negative, false positive and false negative error rates as quality measurement. the practical impact of the suggested benchmarking is shown by comparison of experimental results based on initial segmentation approach and new adaptive approach. Experiments are based on ten handwriting traces each of eleven persons. the comparison of results indicates that the best parameter set of the adaptive thresholding leads to an quality increase of 1 2 : 1 % in terms of precision for writing trace and decrease of 1 : 4 % in terms of precission for background.

关键词： Image and 3D processing applications pattern recognition handwriting forensics questioned documents intended writing segmentation

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Combining deep learning with rough set analysis: A model of cyberspace situational awareness

Combining deep learning with rough set analysis: A model of ...

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international conference on Electronics Information and Emergency Communication

作者： Xueyu Li Xiaocheng Li Zhenhua Zhao College of Information Science and Engineering Shandong University of Science and Technology Qingdao Shandong Province China

A cyberspace situational awareness (CSA) model, DLRSA model, is proposed in this paper, with feature extraction based on deep learning (DL) and situation assessment based on rough set analysis (SARSA). We focus on network flow instead of server logs of IDS to extract features, transforming source data into information and establishing knowledge base. On account of Gaussian-Bernoulli deep belief network (GBDBN), accurate feature information is provided for assessment. While DLRSA model assesses situation in reference to pattern recognition, assessment strategy could be given by rough set analysis and pattern information abstracted deeply. Experiments indicate that DLRSA model has low extraction error and succinct assessment rule.

关键词： Feature extraction Cyberspace Rough sets Analytical models Machine learning pattern recognition Artificial neural networks

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Hybrid Indoor Navigation as sistant for visually impaired people based on fusion of proximity method and pattern recognition algorithm

Hybrid Indoor Navigation as sistant for visually impaired pe...

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IEEE international conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： W. C. S.S. Simões V. F. de Lucena Universidade Federal do Amazonas Manaus Amazonas Brazil

this paper presents an audio guided Indoor Navigation Systems built in a wearable device designed to work with a hybrid mapping. this mapping consist of radio frequency markers as well as visual markers located in special places inside known ambient. this system allows blind users a safe guided navigation with quick and low computational complexity. the chosen methodological approach divides the system in two stages: one offline and another online. In offline stage the indoor mapping is made through construction of markers to generate a contextual database that increases the quality of location indication. the online stage, where the indoor navigation is performed, is based on the proximity method, visual pattern recognition, odometry, and ultrasonic perception of barriers. Results showed rates of over 90.0% of the recognition of RF and visual markers with time of 0.4 seconds, as well as over 95.0% of positive ultrasonic perception of obstacles.

关键词： Visualization Classification algorithms Indoor navigation Acoustics Radio navigation Algorithm design and analysis

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Gene Selection by Sample Classification Using k Nearest Neighbor and Meta-heuristic Algorithms

Gene Selection by Sample Classification Using k Nearest Neig...

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IEEE international Advance Computing conference, IACC

作者： Souvik Saha Surama Biswas Sriyankar Acharyya Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Kolkata India

ISBN: (纸本)9781467382878

Advent in microarray technology enables the researchers of computational biology to apply various bioinformatics paradigms on gene expression data. But microarray gene expression data, obtained as a matrix has much larger number of genes as rows than number of samples (columns representing time points / disease states) which makes many bioinformatics jobs critical. In this direction a subset of genes are selected from the large noisy dataset so that their features are distinguishable between different types of samples (normal/diseased). Here, in this paper, the gene selection process is performed by sample classification using k Nearest Neighbor (k-NN) method. Comparing normal and preeclampsia affected microarray gene expression samples, collected from human placentas, we have selected a set of genes which may be termed as critical for a disease named Preeclampsia, a common complication during pregnancy causing hypertension and proteinuria. Both the normal and diseased dataset contain 25000 genes (rows) having 75 samples (columns) and we have selected 30 genes as disease-critical genes. We have applied two meta-heuristic algorithms, namely, Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Sample classification of normal and preeclampsia shows high fitness (number of samples properly classified). Here, out of 150 (75 normal + 75 diseased) samples, 80-90 samples are properly classified. the number of samples properly classified, denotes the fitness of a solution. So, achieved solution here is of good quality. In our experiments, PSO outperformed SA in respect of best fitness and SA defeated PSO in average fitness.

关键词： Gene expression Classification algorithms Diseases Proteins pattern recognition Pregnancy Simulated annealing

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Outlier detection on mixed-type data: An energy-based approach 12th

Outlier detection on mixed-type data: An energy-based approa...

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12th international conference on Advanced Data Mining and Applications, ADMA 2016

作者： Do, Kien Tran, Truyen Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319495859

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (***). the *** is a principled probabilistic method that models data density. We propose to use free-energy derived from *** as outlier score to detect outliers as those data points lying in low density regions. the method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of *** is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts. © Springer international Publishing AG 2016.

关键词： Free energy

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