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Feature shaving for spectroscopic data

Joint IAPR 10th international workshop on Structural and Syntactic Pattern Recognition, SSPR 2004 and 5th international workshop on Statistical Techniques in Pattern Recognition, SPR 2004

作者： Verzakov, Serguei Paclík, Pavel Duin, Robert P.W. Information and Communication Theory Group Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

ISBN: (纸本)9783540225706

High-resolution spectroscopy is a powerful industrial tool. the number of features (wavelengths) in these data sets varies from several hundreds up to a thousand. Relevant feature selection/extraction algorithms are necessary to handle data of such a large dimensionality. One of the possible solutions is the SVM shaving technique. It was developed for applications in microarray data, which also have a huge number of features. the fact that the neighboring features (wavelengths) are highly correlated allows one to propose the SVM band-shaving algorithm, which takes into account the prior knowledge on the wavelengths order. the SVM band-shaving has a lower computational demands than the standard SVM shaving and selects features organized into bands. this is preferable due to possible noise reduction and a more clear physical interpretation. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Spectroscopic analysis

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Dynamising interval scheduling: the monotonic case

Dynamising interval scheduling: The monotonic case

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24th international workshop on Combinatorial algorithms, IWOCA 2013

作者： Gavruskin, Alexander Khoussainov, Bakhadyr Kokho, Mikhail Liu, Jiamou Department of Computer Science University of Auckland New Zealand School of Computing and Mathematical Sciences Auckland University of Technology New Zealand

ISBN: (纸本)9783642452772

We investigate dynamic algorithms for the interval scheduling problem. We focus on the case when the set of intervals is monotonic. this is when no interval properly contains another interval. We provide two data structures for representing the intervals that allow efficient insertion, removal and various query operations. the first dynamic algorithm, based on the data structure called compatibility forest, runs in amortised time O(log2 n) for insertion and removal and O(logn) for query. the second dynamic algorithm, based on the data structure called linearised tree, runs in time O(logn) for insertion, removal and query. We discuss differences and similarities of these two data structures through theoretical and experimental results. © 2013 Springer-Verlag.

关键词： data structures

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Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization 10

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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10th IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Jin, Can Ye, Zhiwei Yan, Lingyu Cao, Ye Zhang, Aixin Ma, Lie Hu, Xiang Hu, Jiwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Jingzhi Technol Co Ltd Wuhan Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Peoples R China Fujian Prov Univ Fujian Prov Key Lab Data Intens Comp Fuzhou Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Peoples R China

ISBN: (纸本)9781728140698

Image segmentation is the indispensable part in the field of computer vision. there are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Image segmentation Fuzzy c-means spatial information Ant lion optimization

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Probabilistic k-median clustering in data streams

Probabilistic k-median clustering in data streams

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10th international workshop on Approximation and Online algorithms, WAOA 2012

作者： Lammersen, Christiane Schmidt, Melanie Sohler, Christian Simon Fraser University Burnaby BC Canada TU Dortmund Germany

ISBN: (纸本)9783642380150

We define the notion of coresets for probabilistic clustering problems and propose the first (k, Ε)-coreset constructions for the probabilistic k-median problem in the metric and Euclidean case. the coresets are of size poly(Ε-1, k, log(W/(wmin • pmin • δ))), where W is the expected total weight of the weighted probabilistic input points, wmin is the minimum weight of a probabilistic input point, pmin is the minimum realization probability, and δ is the error probability of the construction. We show how to maintain our coreset for Euclidean spaces in data streams. © Springer-Verlag 2013.

关键词： Computers

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the Sky Is Not the Limit

The Sky Is Not the Limit

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10th international workshop on Self-Organizing Maps (WSOM)

作者： Merenyi, Erzsebet Rice Univ Dept Stat 6100 Main St MS-138 Houston TX 77005 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

ISBN: (纸本)9783319076959;9783319076942

We live in the era of Big data, or at least our awareness of Big data's presence and impact has sharpened in the past ten years. Compared to data characteristics decades ago, Big data not only means a deluge of unfiltered bytes, but even more importantly it represents a dramatic increase in data dimensionality (the number of variables) and complexity (the relationships among the often interdependent variables, intricacy of cluster structure). Along with the opportunities for nuanced understanding of processes and for decision making, these data created new demands for information extraction methods in terms of the detail that is expected to be identified in analysis tasks such as clustering, classification, regression, and parameter inference. Many traditionally favored techniques do not meet these challenges if one's aim is to fully exploit the rich information captured by sophisticated sensors and other automated data collection techniques, to ensure discovery of surprising small anomalies, discriminate important, subtle differences, and more. A flurry of technique developments has been spawned, many augmenting existing algorithms with increasingly complex features. © Springer international Publishing Switzerland 2014.

关键词： Big data

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10th international workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging workshop and Tutorial, PPML 2021, held in conjunction with 24th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

10th International Workshop on Clinical Image-Based Procedur...

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10th international workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging workshop and Tutorial, PPML 2021, held in conjunction with 24th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030908737

the proceedings contain 17 papers. the special focus in this conference is on Clinical Image-Based Procedures. the topics include: DCL preface;LL-COVID-19 preface;PPML preface;intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction;multi-task Federated Learning for Heterogeneous Pancreas Segmentation;federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks;on the Fairness of Swarm Learning in Skin Lesion Classification;Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next;the Role of Pleura and Adipose in Lung Ultrasound AI;DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19;data Imputation and Reconstruction of Distributed Parkinson’s Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation algorithms;defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics;generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning;Bayesian Graph Neural Networks for EEG-Based Emotion Recognition;ViTBIS: Vision Transformer for Biomedical Image Segmentation;Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes;development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning;learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback.

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Exploring Homogeneity and Covariance Matrix Structure of Multistatic/Polarimetric Sea-Clutter data 10

Exploring Homogeneity and Covariance Matrix Structure of Mul...

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10th IEEE international workshop on Metrology for AeroSpace, MetroAeroSpace 2023

作者： Carotenuto, V. Aubry, A. De Maio, A. Fioranelli, F. University of Naples Federico II DIETI via Claudio 21 Naples Italy MS3 Group Department of Microelectronics TU Delft Mekelweg 4 Delft2628CD Netherlands

ISBN: (纸本)9781665456906

the design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. this is fundamental to develop an analytic model for the received signal samples, which is used to synthesize the radar detector. In this respect, the aim of this paper is the design of suitable learning tools to study some important statistical properties of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. the study is complemented by the use of radar returns measured with the Netted RADar (NetRAD), which collects simultaneously monostatic and bistatic measurements. Precisely, the homogeneity properties of the data in the slow-time domain are first assessed resorting to Generalized Inner Product (GIP) based statistics. then, the possible presence of structures in the clutter covariance matrices (both inter and intra channels) is investigated through ad-hoc statistical tools. the results show that the data, regardless the polarimetric/geometric configuration, can be modeled as drawn from a stationary process within the coherence time. Moreover, for both the monostatic and the bistatic returns the structure of the covariance matrix depends upon the polarimetric/geometric configuration of the sensing system. © 2023 IEEE.

关键词： Radar measurement

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Classification of defaced occlusion plates based on convolutional neural network 5

Classification of defaced occlusion plates based on convolut...

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5th international workshop on Pattern Recognition

作者： Sen, Zhang Zhang Jingle Jie, Li Shuai, Chen Minist Publ Secur Traff Management Res Inst 88 Qianrong Rd Wuxi 214151 Jiangsu Peoples R China Beijing Huizhi Data Technol Co Court 1 Upland 10th St Beijing 100085 Peoples R China

ISBN: (纸本)9781510638648

As one of the important components of intelligent transportation, license plate recognition plays an irreplaceable role in people's daily life. For example, illegal vehicles often escape from punishment because of the number plate defacement or intentional occlusion, which further increases the difficulty of law enforcement. therefore, it is significant for automatic recognition system to improve the identification efficiency of the contaminated or occluded license plate. this paper mainly focuses on the recognition of occlusion number plate. License plates can be divided into four categories: normal number plate, partial occlusion number plate, complete occlusion number plate and unsuspended number plate. the traditional OCR algorithm has a high accuracy in the recognition of Chinese characters, characters and numbers. Although the detection of normal and partial occlusion plates also shows a good recognition in the case of OCR, the recognition of complete occlusion and unsuspended license plates is still very poor. With the development of artificial intelligence, it is possible to identify all the sheltered and unsuspended plates better. Combining with the advantages of traditional algorithms, this paper uses traditional OCR and current deep learning algorithm to optimize the recognition effect of stained license plate.

关键词： Intelligent transportation defacement occlusion deep learning OCR

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Artificial neural networks for non destructive testing of concrete structures

Artificial neural networks for non destructive testing of co...

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10th international Conference on Engineering Applications of Neural Networks, EANN 2007

作者： Cannas, Barbara Carcangiu, Sara Cau, Francesca Fanni, Alessandra Montisci, Augusto Testoni, Pietro Department of Electrical and Electronic Engineering University of Cagliari Piazza d'Armi 09123 Cagliari Italy

In this paper, the determination of defects in concrete structures using an ultrasound technique is discussed. A diagnostic model for concrete pillars by means of Multi Layer Perceptron neural networks is developed to locate and classify the defects. Finite Elements numerical techniques have been used to model a concrete pillar of specified size (i.e., rectangular cross section and 2 meters in span) affected by defects of different position and size. the numerical analyses enable to obtain several received signals containing the fault information. these signals have been processed by a feature extractor system, whose purpose is to reduce the data dimensionality and to compute suitable features. Results showed good accuracy in the identification of the position and entity of the faults.

关键词： Concrete construction

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Practical Guidelines for Incorporating Knowledge-Based and data-Driven Strategies into the Inference of Gene Regulatory Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016年第1期13卷 64-75页

作者： Hsiao, Yu-Ting Lee, Wei-Po Yang, Wei Mueller, Stefan Flamm, Christoph Hofacker, Ivo Kuegler, Philipp Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung 80424 Taiwan Acad Sinica Genom Res Ctr Taipei 115 Taiwan Fudan Univ Shanghai Ctr Math Sci Shanghai 200433 Peoples R China Austrian Acad Sci Johann Radon Inst Computat & Appl Math Linz Austria Univ Vienna Inst Theoret Chem Vienna Austria Univ Vienna Fac Comp Sci Res Grp BCB Vienna Austria Univ Hohenheim Inst Appl Math & Stat Stuttgart Germany

Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed;this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors;and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. the results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. therefore, the outcomes are exploitable for future in vivo experimental design.

关键词： Prior knowledge-mapping S-system gene regulatory network modeling evolutionary algorithms

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