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Clustering algorithms in biomedical research: A review

IEEE Reviews in Biomedical engineering 2010年 3卷 120-154页

作者： Xu, Rui Wunsch, Donald C. Applied Computational Intelligence Laboratory GE Global Research Center Niskayuna NY 12309 United States Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 United States

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications. © 2010 IEEE.

关键词： Neural networks

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High performance computing with graphics cards to accelerate processing density functional theory

High performance computing with graphics cards to accelerate...

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2010 International Conference on High Performance Computing Systems, HPCS 2010

作者： Da Silva, Cleomar Pereira Pacheco, Marco Aurélio Cavalcanti Neto, Omar Paranaiba Vilela Department of Electrical Engineering - DEE Applied Computational Intelligence Laboratory - ICA Pontifical Catholic University of Rio de Janeiro - PUC Rio Brazil

ISBN: (纸本)9781617820663

The Graphics Processing Units (GPUs), or Graphics Cards, were originally designed to perform dedicated tasks to the computer graphics operations. Recently, however, NVIDIA has developed an extension of the C language for programming GPUs, called CUDA (Compute Unified Device Architecture). Density Functional Theory (DFT) is one of the most used iterative methods to find an approximate solution to the Schrödinger equation. However, the calculations in DFT are computationally intensive because of the exchange and correlation electronic integrals, integrals to calculate the Hartree and kinetic energies, which require more computational effort as the number of electrons in the simulation increases. The development of the GPU and tools focused on the concept of General-Purpose computation on Graphics Processing Units (GPGPU) is an alternative to accelerate the energy calculation in the DFT method. This research aimed to study the DFT calculations and identify parts of the algorithm that, if changed, could represent performance benefits to be executed on GPU. The SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms) is a DFT method that uses pseudopotencial for ionic core representation, numerical atomic orbitals and localized basis set. In this research, some functions of this method ware parallelized and used to calculate the physical properties of nanotubes in order to prove the efficiency of GPUs. It was found that the implementation of SIESTA GPU parallel version is able to achieve gains in performance, in individual functions, of one or even two orders of magnitude, making it promising employment of GPUs to speed up the processing of the Density Functional Theory.

关键词： Graphics processing unit

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Clustering with differential evolution particle swarm optimization

Clustering with differential evolution particle swarm optimi...

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2010 6th IEEE World Congress on computational intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

作者： Xu, Rui Xu, Jie Wunsch II, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 United States Department of Industrial Engineering and Management Sciences Northwestern University Evanston IL 60208 United States Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9781424469109

The applications of recently developed meta-heuristics in cluster analysis, such as particle swarm optimization (PSO) and differential evolution (DE), have increasingly attracted attention and popularity in a wide variety of communities owing to their effectiveness in solving complicated combinatorial optimization problems. Here, we propose to use a hybrid of PSO and DE, known as differential evolution particle swarm optimization (DEPSO), in order to further improve search capability and achieve higher flexibility in exploring the natural while hidden data structures of data of interest. Empirical results show that the DEPSO-based clustering algorithm achieves better performance in terms of the number of epochs required to reach a pre-specified cutoff value of the fitness function than either of the other approaches used. Further experimental studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method in finding meaningful clustering solutions. © 2010 IEEE.

关键词： Clustering algorithms

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Discrete-time recurrent neural networks for medical image segmentation based on competitive layer model with LT neurons

Discrete-time recurrent neural networks for medical image se...

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2010 International Conference on Biomedical engineering and computer Science, ICBECS 2010

作者： Zhou, Wei Zurada, Jacek M. School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Computational Intelligence Laboratory Electrical and Computer Engineering Department University of Louisville Louisville KY 40292 United States

ISBN: (纸本)9781424453153

This paper discusses a class of Discrete-Time Recurrent Neural Networks with LT neurons based on Competitive Layer Model(CLM-DT-LT-RNNs). It first addresses the boundedness and complete stability of the networks, then a theorem is given to let the networks have CLM phenomena. Such networks are applied to medical image segmentation by using the global gray-level information and the contextual information of pixels. In order to alleviate time and storage consuming, a technique of divide-and-merge(DAM) is used. Simulation results are used to illustrate the application in image segmentation. ©2010 IEEE.

关键词： Image segmentation

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H-DIBCO 2010 - Handwritten document image binarization competition

H-DIBCO 2010 - Handwritten document image binarization compe...

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International Conference on Frontiers in Handwriting Recognition

作者： Pratikakis, Ioannis Gatos, Basilis Ntirogiannis, Konstantinos Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos GR-15310 Agia Paraskevi Athens Greece Department of Electrical and Computer Engineering Democritus University of Thrace GR-67100 Xanthi Greece

ISBN: (纸本)9780769542218

H-DIBCO 2010 is the International Document Image Binarization Contest which is dedicated to handwritten document images organized in conjunction with ICFHR 2010 conference. The general objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 17 submitted methods along with a short description of each method. © 2010 IEEE.

关键词： H-DIBCO 2010 ICFHR 2010 conference binarization document image processing handwritten character recognition handwritten document image handwritten document image binarization performance evaluation performance evaluation performance evaluation

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Quantum-Inspired Evolutionary Algorithms applied to numerical optimization problems

Quantum-Inspired Evolutionary Algorithms applied to numerica...

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Congress on Evolutionary Computation

作者： André Vargas Abs da Cruz Marley M. B. R. Vellasco Marco Aurélio C. Pacheco Applied Computational Intelligence Laboratory Electrical Engineering Department Pontifical Catholic University of Rio de Janeiro Brazil

Since they were proposed as an optimization method, the evolutionary algorithms have been successfully used for solving complex problems in several areas such as, for example, the automatic design of electronic circuits and equipments, task planning and scheduling, software engineering and data mining, among many others. However, some problems are computationally intensive when it concerns the evaluation of solutions during the search process, making the optimization by evolutionary algorithms a slow process for situations where a quick response from the algorithm is desired (for instance, in online optimization problems). Several ways to overcome this problem, by speeding up convergence time, were proposed, including Cultural Algorithms and Coevolutionary Algorithms. However, these algorithms still have the need to evaluate many solutions on each step of the optimization process. In problems where this evaluation is computationally expensive, the optimization can take a prohibitive time to reach optimal solutions. This work presents an evolutionary algorithm for numerical optimization problems (Quantum-Inspired Evolutionary Algorithm for Problems based on Numerical Representation - QIEA-R), inspired in the concept of quantum superposition, which allows the optimization process to be carried on with a smaller number of evaluations. It extends previous works by presenting a broader range of tests and improvements on the algorithm. The results show the good performance of this algorithm in solving numerical problems.

关键词： Optimization Benchmark testing Equations Quantum computing Particle swarm optimization Mathematical model

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Modified multiband multiple ring monopole antenna

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Progress In Electromagnetics Research C 2010年 14卷 173-183页

作者： Mirkamali, A. Akhoondzadeh-Asl, L. Hall, P.S. Moussakhani, K. Electrical Engineering Department Faculty of Engineering Zanjan University Zanjan Iran Antennas and Applied Electromagnetics Laboratory Electronic Electrical and Computer Engineering Department University of Birmingham Edgbaston United Kingdom Computational Electromagnetics Research Laboratory Electrical and Computer Engineering Department McMaster University Hamilton Canada

In this paper, a modified version of the multi-band multiple ring monopole antenna is proposed. The height of the new design which consists of multiple half rings is half of the original one. The modified design is more attractive for low profile applications due to its lower height. The antenna is simulated and measured. It is shown that the simulation and measurement results are in good agreement. The performance of the modified version of the antenna is compared with the original design in terms of input characteristic and far field radiation patterns. It is shown that the multiband behaviour of the modified design is similar to the original one. However, there is a frequency shift between the operating bands of the new and the original antennas. The radiation patterns of the both antennas are similar to the conventional monopole antenna in lower operating frequency bands. However, degradation in radiation patterns of the both antennas is observed as frequency increases.

关键词： Monopole antennas

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Clustering with differential evolution particle swarm optimization

Clustering with differential evolution particle swarm optimi...

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Congress on Evolutionary Computation

作者： Rui Xu Jie Xu Donald C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical & Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Industrial Engineering & Management Sciences Northwestern University Evanston IL USA Department of Electrical & Computer Engineering Missouri University of Science and Technology Rolla MO USA

关键词： Clustering algorithms Iris recognition Indexes Particle swarm optimization Partitioning algorithms Optimization Encoding

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Discrete-Time Recurrent Neural Networks for Medical Image Segmentation Based on Competitive Layer Model with LT Neurons

Discrete-Time Recurrent Neural Networks for Medical Image Se...

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International Conference on Biomedical engineering and computer Science (ICBECS)

作者： Wei Zhou Jacek M. Zurada School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Computational Intelligence Laboratory Electrical and Computer Engineering Department University of Louisville Louisville KY USA

This paper discusses a class of Discrete-Time Recurrent Neural Networks with LT neurons based on Competitive Layer Model (CLM-DT-LT-RNNs). It first addresses the boundedness and complete stability of the networks, then a theorem is given to let the networks have CLM phenomena. Such networks are applied to medical image segmentation by using the global gray-level information and the contextual information of pixels. In order to alleviate time and storage consuming, a technique of divide-and-merge (DAM) is used. Simulation results are used to illustrate the application in image segmentation.

关键词： Recurrent neural networks Biomedical imaging Image segmentation Neurons Medical diagnostic imaging Biomedical engineering computer science Paper technology Laboratories computer networks

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H-DIBCO 2010 - Handwritten Document Image Binarization Competition

H-DIBCO 2010 - Handwritten Document Image Binarization Compe...

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International Workshop on Frontiers in Handwriting Recognition

作者： Ioannis Pratikakis Basilis Gatos Konstantinos Ntirogiannis Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

关键词： Pixel Image edge detection Histograms PSNR Measurement Image segmentation Laboratories

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