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Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease

BMC Neuroscience 2012年第1期13卷 1-1页

作者： Jeroen Bergmann Newton Howard Medical Engineering Solutions in Osteoarthritis Centre of Excellence Imperial College London W68RF UK Massachusetts Institute of Technology Cambridge MA 02139 USA

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Inferring ethnicity from mitochondrial DNA sequence

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BMC proceedings 2011年第2期5 Suppl 2卷 S11页

作者： Chih Lee Ion I Măndoiu Craig E Nelson Computer Science and Engineering Department University of Connecticut Storrs CT USA. chihlee@engr.uconn.edu.

BACKGROUND:The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome. RESULTS:We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome. CONCLUSIONS:Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.

关键词： Support Vector Machine Linear Discriminant Analysis classification algorithm Hypervariable Region Published Dataset

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Detection of Bruise on Pear by Hyperspectral Imaging Sensor with Different classification algorithms

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SENSOR LETTERS 2010年第4期8卷 570-576页

作者： Zhao, Jiewen Ouyang, Qin Chen, Quansheng Wang, Jianhei Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China

A hyperspectral imaging sensor system was developed for the detection of bruises on pears, for these bruises were difficult to be detected by traditional computer vision technique. Hyperspectral imaging sensor technique is susceptible to the effects of uneven illumination due to a spherical object of pear. The data of hyperspectral image is a 3-dimension cube, which contains a huge amount of information. So it requires a suitable algorithm to extract some useful information from the 3-dimension data cube. In this work, Principal Component Analysis (PCA) was firstly used to extract some useful information, then several other classification algorithms were used comparatively to process the 3-dimension data cube. These classification algorithms were Maximum Likelihood classification (MLC), Euclidean Distance classification (EDC), Mahalanobis Distance classification (MDC) and Spectral Angle Mapper (SAM), respectively. Results show that MDC and SAM have well performance, with detection accuracy of 93.8% and 95.0% respectively. Compared with the other classification algorithms, MDC and SAM can overcome the effects of uneven illumination in detecting bruise of pear by hyperspectral imaging sensor technique. This work demonstrates that it is feasible to detect the bruised region on the surface of pear by hyperspectral imaging sensor technique combined with MDC and SAM.

关键词： Hyperspectral Imaging Detection classification algorithm Bruise Pear

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Research on Decision Tree algorithm Based on Information Entropy

Research on Decision Tree Algorithm Based on Information Ent...

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The 2011 International Conference on Materials Engineering for Advanced Technologies(ICMEAT 2011)

作者： DU Ming WANG SHU-mei GONG Gu College of Computer Science and Technology Xuzhou Normal University

Decision tree is an important learning method in machine learning and data mining,this paper discusses the method of choosing the best attribute based on information *** analyzes the process and the characters of classification and the discovery knowledge based on decision tree about the application of decision tree on data *** an instance, the paper shows the procedure of selecting the decision attribute in detail,finally it pointes out the developing trends of decision tree.

关键词： decision tree classification algorithm entropy information gain gain ratio

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Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees 11

Knowledge discovery through SysFor: a systematically develop...

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Proceedings of the Ninth Australasian Data Mining Conference - Volume 121

作者： Zahidul Islam Helen Giggins Charles Sturt University NSW Australia Newcastle University Callaghan Australia

ISBN: (纸本)9781921770029

Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set, and to predict the class attribute value of an unlabeled record. Sometimes a set of decision trees, rather than just a single tree, is also generated from a data set. A set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. Existing multiple tree techniques are catered for high dimensional data sets and therefore unable to build many trees from low dimensional data sets. In this paper we present a novel technique called Sys-For that can build many trees even from a low dimensional data set. Another strength of the technique is that instead of building multiple trees using any attribute (good or bad) it uses only those attributes that have high classification capabilities. We also present two novel voting techniques in order to predict the class value of an unlabeled record through the collective use of multiple trees. Experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery from both low dimensional and high dimensional data sets by building a number of good quality decision trees. Moreover, it also has prediction accuracy higher than the accuracy of several existing techniques that have previously been shown as having high performance.

关键词： classification algorithm data mining prediction accuracy multiple decision tree

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A Comparative Study of Two Matrix Factorization Methods Applied to the classification of Gene Expression Data

A Comparative Study of Two Matrix Factorization Methods Appl...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Nikulin, Vladimir Huang, Tian-Hsiang McLachlan, Geoffrey J. Univ Queensland Dept Math Brisbane Qld Australia Natl Cheng Kung Univ Inst Informat Management Tainan Taiwan Univ Queensland Inst Mol Biosci Brisbane Qld Australia

ISBN: (纸本)9781424483075

In microarray data analysis, dimension reduction is an important consideration in the construction of a successful classification algorithm. As an alternative to feature selection, we use a well-known matrix factorisation method. For example, we can employ the popular singular-value decomposition (SVD) or nonnegative matrix factorization. In this paper, we consider a novel algorithm for gradient-based matrix factorisation (GMF). We compare GMF and SVD in their application to five gene expression datasets. The experimental results show that our method is faster, more stable, and sensitive.

关键词： SVD bioinformatics biological techniques classification algorithm data reduction dimension reduction gene expression data classification gene expression datasets gradient based matrix factorisation gradient methods matrix algebra matrix factorization meth

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Short Range LIDAR for Bioagent Detection and classification

Short Range LIDAR for Bioagent Detection and Classification

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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI

作者： Ho, Nicolas Emond, Frederic Babin, Francois Healy, Dave Simard, Jean-Robert Buteau, Sylvie McFee, John E. INO 2740 Einstein St Quebec City PQ G1P 4S4 Canada MDA Richmond BC V6V 2J3 Canada Def Res & Dev Canada Valcartier Quebec City PQ G3J 1X5 Canada Def Res & Dev Canada Suffield Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9780819481290

We have developed a small, relatively lightweight and efficient short range (<100 m) LIDAR instrument for remotely detecting harmful bioagents. The system is based on a pulsed, eye-safe, 355 nm laser exciting aerosols which then fluoresce with a typical spectrum. The system makes use of a novel technology for continuously monitoring for the presence of unusual concentrations of bioaerosols at a precise remote location within the monitored area, with response within seconds. Fluorescence is spectrally resolved over 32 channels capable of photon counting. Results show a sensitivity level of 40 ACPLA of Bacillus Globigii, an anthrax simulant, at a distance of 100 m (assumed worst case where 1 ppl = 1 ACPLA) considering particle sizes between 0.5 and 10 mu m, with a geometric mean at 1 um. The apparatus has been tested in the field during three test and evaluation campaigns with multiple bioagents and public security products. Preliminary results show that the system is able to distinguish between harmful bioagents and naturally occurring ones. A classification algorithm was successfully tested with a single type of bioagent;experiments for daytime measurements are discussed.

关键词： Standoff detection biological warfare agents lidar classification algorithm spectroscopy laser induced fluorescence

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A Hardware/Software Co-design Architecture for Packet classification

A Hardware/Software Co-design Architecture for Packet Classi...

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22nd International Conference on Microelectronics (ICM 2010)

作者： Ahmed, O. Chattha, K. Areibi, S. Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781612841519

Packet classification involves matching information from a packet's header to a set of rules in a database in order to determine the manner in which the packet should be processed by network processors. The PCIU algorithm [1] is a novel classification algorithm which improves upon previously published techniques in the literature. The main features of the PCIU algorithm are the low pre-processing time and capability of incremental rule update. Using the network processor to implement packet classification would cause saturation even when the best performing packet classification algorithm is used. In this work, we propose a hardware implementation of the PCIU algorithm. Results obtained indicate that the hardware/software co-design approach achieves 4.3x speedup in terms of preprocessing over a pure software implementation and 5.3x speedup for classification.

关键词： PCIU algorithm classification algorithm hardware implementation hardware software codesign architecture hardware-software codesign matching information network processors packet classification pattern classification pattern matching

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Detection and classification of Marine Mammal Clicks

Detection and Classification of Marine Mammal Clicks

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Washington State Conference and Trade Center (WSCTC)

作者： McGaughey, Donald R. Marcotte, David Korenberg, Michael J. Theriault, James A. Royal Mil Coll Canada Box 17000 Stn Forces Kingston ON K7K 7B4 Canada ADAC Victoria BC Canada Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada DRDC Atlan Dartmouth NS B2Y 3Z7 Canada

ISBN: (纸本)9781424443338

Detection of marine mammals within an influence zone of episodal anthropogenic noise source is critical to insure the safety of the animals. Marine mammal clicks are closely modeled by AM/FM signals. The Teager-Kaiser energy operator followed by a threshold detector provides an effective means of detecting AM/FM signals. classification of the species generating the click is done by finding the maximum cross-covariance between the power spectral density (PSD) of the received click and the PSDs of clicks which have been identified. The classification algorithm will create a new library entry when the cross-covariance is below a predefined threshold. Five species of marine mammals were classified using data from the 3rd International Workshop on Detection, classification and Localization of Marine Mammals (DCL). This paper presented a computationally-inexpensive marine mammal detection and classification algorithm with high probabilities of detection and correct classification.

关键词： 3rd international workshop on detection AM-FM signals Teager-Kaiser energy operator acoustic noise acoustic signal detection bioacoustics classification algorithm episodal anthropogenic noise source marine mammal classification marine mammal clicks marine mammal detection marine mammal localization power spectral density threshold detector

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Experimental bifurcation diagram of a circuit-implemented neuron model

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PHYSICS LETTERS A 2010年第45期374卷 4589-4593页

作者： Linaro, D. Poggi, T. Storace, M. Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

An experimental bifurcation diagram of a circuit implementing an approximation of the Hindmarsh-Rose (HR) neuron model is presented. Measured asymptotic time series of circuit voltages are automatically classified through an ad hoc algorithm. The resulting two-dimensional experimental bifurcation diagram evidences a good match with respect to the numerical results available for both the approximated and original HR model. Moreover, the experimentally obtained current-frequency curve is very similar to that of the original model. The obtained results are both a proof of concept of a quite general method developed in the last few years for the approximation and implementation of nonlinear dynamical systems and a first step towards the realisation in silica of HR neuron networks with tunable parameters. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Experimental bifurcation diagram Hardware neuron model classification algorithm

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