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Lymph diseases diagnosis approach based on support vector machines with different kernel functions 9

Lymph diseases diagnosis approach based on support vector ma...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Elshazly, Hanaa Ismail Elkorany, Abeer Mohamed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781479965946

In this paper, a Genetic algorithm (GA) hased supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The Lymphography database was obtained from the University Medical Center, Institute of Oncology, Ljubljana, Yugoslavia. The obtained classification accuracy was very promising with regard to the other classification applications in the literature for this problem. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, area under curve (AUC) or (ROC), Matthews Correlation Coefficient (MCC) and F-Measure. Linear kernel function obtained highest results which verifies the efficiency of GA-linear stategy.

关键词： diseases genetic algorithms medical diagnostic computing patient diagnosis pattern classification support vector machines GA based supporting vector machine classifier GA-SVM GA-linear stategy Gaussian kernel function Institute of Oncology Ljubljana Lymphography database SVM classifier performance University Medical Center Yugoslavia classification accuracy classification applications genetic algorithm linear kernel function lymph disease dataset lymph disease diagnosis approach performance indices quadratic kernel function algorithm design and analysis Classification algorithms Extraterrestrial measurements Indexes Kernel Prediction algorithms Support vector machines Support Vector Network KERNEL FUNCTIONS Genetic algorithm (GA) medical diagnostic computing Extraterrestrial measurements algorithm design and analysis Academic Medical Centers performance indicators patient diagnosis Prediction algorithms sorting algorithm Pattern recognition Disease diagnosis Kernel maladjustment

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Open-Circuit Fault Diagnosis and Fault-Tolerant Control with Sequential Indirect Model Predictive Control for Modular Multilevel Converters 4

Open-Circuit Fault Diagnosis and Fault-Tolerant Control with...

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4th IEEE Workshop on the Electronic Grid (eGRID)

作者： Sun, Yuanxiang Li, Zhen Zhang, Zhenbin Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Shandong Univ Suzhou Inst Suzhou 215123 Peoples R China

ISBN: (纸本)9781728157047

Fault diagnosis and fault-tolerant control are significant for the reliability of an MMC system, which usually has a large number of switching devices and high potential of operation failure. This paper proposes a simple fault diagnosis method for sub-module open-circuit faults and a new fault-tolerant control method. The proposed methods combine a novel sequential indirect MPC structure with sorting algorithm. Different from the well-known solutions, we detect and locate open-circuit faults in any sub-module efficiently with the maximum voltage output of the capacitor voltage sorting result, which is based on the physical characteristics of circuits when capacitor voltage rises. Thereafter, a new fault-tolerant control solution based on MPC is implemented through modifying circuit structure and control algorithm. The proposed methods are easy to implement and add little computational complexity to the control algorithm. Finally, the MATLAB/Simulink verification results confirm the effectiveness of the proposed solutions.

关键词： fault diagnosis fault-tolerant control MMC sequential indirect MPC sorting algorithm

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A Novel Optimal Modulation Strategy for Modular Multilevel Converter Based HVDC Systems 2

A Novel Optimal Modulation Strategy for Modular Multilevel C...

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IEEE 2nd International Conference on Renewable Energy and Power Engineering (REPE)

作者： Khanal, Saroj Disfani, Vahid R. Univ Tennessee Chattanooga TN 37403 USA

ISBN: (纸本)9781728145624

Unlike conventional converters, modular multilevel converter (MMC) has a higher switching frequency - which has direct implication on important parameters like converter loss and reliability - mainly due to increased number of switching components. However, conventional switching techniques, where submodule sorting is just based on capacitor voltage balancing, are not able to achieve switching frequency reduction objective. A novel modulation algorithm for modular multilevel converters (MMCs) is proposed in this paper to reduce the switching frequency of MMC operation by defining a constrained multiobjective optimization model. The optimized switching algorithm incorporates all control objectives required for the proper operation of MMC and adds new constraints to limit the number of submodule switching events at each time step. Variation of severity of the constraints leads to a desired level of controllability in MMC switching algorithm to trade-off between capacitor voltage regulation and switching frequency reduction. Finally, performance of the proposed algorithm is validated against a seven-level back-to-back MMC-HVDC system under various operating conditions.

关键词： capacitor voltage balancing high voltage direct current (HVDC) model predictive control (MPC) optimal switching sorting algorithm

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Determining the impact of demographic features in predicting student success in Croatia

Determining the impact of demographic features in predicting...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Trstenjak, Bruno Donko, Dzenana Medimurje Univ Appl Sci Cakovec Dept Comp Engn Cakovec Croatia Fac Elect Engn Dept Comp Sci Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330816

Predicting the success of students is a topic which has been studied for a long time in different scientific fields. Evaluation of importance of the features used in the prediction and their subsequent selection is an immensely important step in the process of classification and data mining. This paper presents a study on the importance of student demographic features in the process of predicting. The study and performed analyses used the demographic data collected from the Information System for Higher Education (ISVU). For determining the importance of demographic features in the study the following methods have been used: Information Gain (IG), Gain Ratio (GR), Sequential Backward Selection (SBS), Sequential Forward Selection (SFS). The results show the features rank, their importance weight in the prediction and comparison of the results and the use of different methods. Two classification algorithms for evaluating the impact of ranking features to the quality of prediction are used: Naive Bayes i Support Vector Machine (SVM). Final results provide guidelines for the development of a new prediction model.

关键词： Accuracy Classification algorithms Educational institutions Machine learning algorithms Prediction algorithms Support vector machines sorting algorithm Machine learning algorithms Support Vector Network Prediction algorithms Educational institutions Students Croatia Computer based information systems

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Generalized Learning Vector Quantization Particle Swarm Optimization (GLVQ-PSO) FPGA Implementation for Real-Time Electrocardiogram

Generalized Learning Vector Quantization Particle Swarm Opti...

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International Workshop on Big Data and Information Security (IWBIS)

作者： Wardhana, Yulistiyan Jatmiko, Wisnu Rachmadi, M. Febrian Univ Indonesia Fac Comp Sci Depok Indonesia Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9781509034772

Cardiovascular system is the most important part of human body which has role as distribution system of Oxygen and body's wastes. To do the job, there are more than 60.000 miles of blood vessels participated which can caused a problem if one of them are being clogged. Unfortunately, the conditions of clogged blood vessels or diseases caused by cardiovascular malfunction could not be detected in a plain view. In this matter, we proposed a design of wearable device which can detect the conditions. The device is equipped with a newly neural network algorithm, GLVQ-PSO, which can give recommendation of the heart status based on learned data. After the research is conducted, the algorithm produce better accuracy than LVQ, GLVQ and FNGLVQ in the high level language implementation. Yet, GLVQ-PSO still has relatively worse performance in its FPGA implementation.

关键词： algorithm design and analysis Field programmable gate arrays Clustering algorithms Classification algorithms Particle swarm optimization Heart High level languages Field programmable gate arrays high level language(hll) algorithm design and analysis sorting algorithm Clustering algorithms Particle swarm optimization Heart learning vector quantization Human Body Blood Vessels Cardiovascular System

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ECG Classification Based on Time and Frequency Domain Features Using Random Forests 44

ECG Classification Based on Time and Frequency Domain Featur...

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44th Computing in Cardiology Conference (CinC)

作者： Kropf, Martin Hayn, Dieter Schreier, Guenter Charite Virchow Klinikum Berlin Germany AIT Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9781538645550;9781538666302

We present a combined method of classical signal analysis and machine learning algorithms for the automated classification of 1-lead ECG recordings, which was developed in the course of the Computing in Cardiology Challenge 2017. To classify ECG recordings into the four classes as defined for the Challenge (normal, suspicious to AF, suspicious to other arrhythmia, noise) we used MATLABand a set of algorithms for detection of beats, wave point detection on detected beats, quality evaluation of the detection, averaging of beats, beat classification, rhythm classification and many more. We extracted a variety of features from both time and frequency domain etc. as input features for the classifier. A total of 380 features were used to train a Random Forest - based classifier (bagged decision trees). Since classes for the Challenge were severely unbalanced, weights based on the class distribution were applied. To train the classifier and for our internal evaluation we used cross-validation on all available ECGS from the training-set. 10-fold cross-validated F-1 score on the training set is 0.83. Final F-1 score from the official challenge evaluation on the enhanced dataset is 0.81, which is quite close to the other top performing algorithms.

关键词： Electrocardiography Feature extraction Classification algorithms Machine learning algorithms Training Predictive models Cardiology Machine learning algorithms Electrocardiography sorting algorithm Cardiology Prediction models Feature extraction Functional training Frequency-domain analysis Signal analysis Classifiers electrochemical grinding Beat generation

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kNN estimation of the unilateral dependency measure between random variables 5

kNN estimation of the unilateral dependency measure between ...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Cataron, Angel Andonie, Razvan Chueh, Yvonne Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Comp Sci Ellensburg WA USA Cent Washington Univ Dept Math Ellensburg WA USA

ISBN: (纸本)9781479945184

The informational energy (IE) can be interpreted as a measure of average certainty. In previous work, we have introduced a non-parametric asymptotically unbiased and consistent estimator of the IE. Our method was based on the kth nearest neighbor (kNN) method, and it can be applied to both continuous and discrete spaces, meaning that we can use it both in classification and regression algorithms. Based on the IE, we have introduced a unilateral dependency measure between random variables. In the present paper, we show how to estimate this unilateral dependency measure from an available sample set of discrete or continuous variables, using the kNN and the naive histogram estimators. We experimentally compare the two estimators. Then, in a real-world application, we apply the kNN and the histogram estimators to approximate the unilateral dependency between random variables which describe the temperatures of sensors placed in a refrigerating room.

关键词： pattern classification regression analysis IE classification algorithms informational energy kth nearest neighbor kNN estimation kNN method nai?ve histogram estimators random variables regression algorithms unilateral dependency Approximation methods Estimation Histograms Ice Probability density function Random variables Temperature sensors Random variable histograms Temperature Sensor Device Component Approximation method Pattern recognition Probability density function Ice Nearest neighbor Joint Research Centre regression analysis sorting algorithm Unilateral

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Highly scalable asynchronous trigger system for PET using UDP/IP

Highly scalable asynchronous trigger system for PET using UD...

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IEEE Nuclear Science Symposium/Medical Imaging Conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Hines, K. E. Rissi, M. Bolle, E. Rohne, O. Univ Oslo N-0316 Oslo Norway

ISBN: (纸本)9781467301206

Asynchronous coincidence triggering in PET can be realized by using UDP over 1 Gbps Ethernet. This will bypass problems related to point-to-point connections and treestructures in real-time coincidence trigger units. By using UDP over Ethernet, the trigger system is easy to scale, and off-the-shelf components can be used. However, due to unknown and network load dependent delay times of UDP communication packages, and due to possible package losses, the system must react tolerantly towards these occurrences. In our FPGA based readout system, event packages (consisting of an event time, energy, and channel number) are stored in a FIFO. The event times are sent to a Central Trigger Unit (CTU) through a UDP/IP link where trigger decisions are made and broadcasted back to the readout cards. The broadcasted trigger times are compared to existing event times to decide if the event package is discarded or sent to a computer farm. Since the UDP packages with event times and trigger times are self-contained there is no need for sorting algorithms usually associated with UDP networking. The UDP/IP stack is implemented solely in hardware without using large amount of resources.

关键词： Ethernet FPGA based readout system IP networks Microwave integrated circuits PET UDP communication package UDP/IP asynchronous circuits asynchronous coincidence triggering system central trigger unit field programmable gate arrays high energy physics instrumentation computing highly scalable asynchronous trigger system local area networks nuclear electronics off-the-shelf components point-to-point connection process positron emission tomography readout electronics real-time coincidence trigger units sorting algorithm transport protocols trigger circuits

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LOCAL INSERTION SORT REVISITED 2nd

LOCAL INSERTION SORT REVISITED

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2ND INTERNATIONAL SYMP ON OPTIMAL algorithmS

作者： KATAJAINEN, J LEVCOPOULOS, C PETERSSON, O Department of Computer and Information Science Linköping University Linköping S-58183 Sweden Department of Computer Science University of Turku Lemminkäisenkatu 14A Turku SF-20520 Finland

ISBN: (纸本)3540518592

Two measures of presortedness, Dist(X) and logDist(X), are presented. The measures are motivated by a geometric interpretation of the input sequence X. Using these measures, an exact characterization of the local insertion sort algorithm of Mannila is achieved. Variants of local insertion sort, using many fingers (i.e., pointers into a finger search tree) instead of only one, are suggested. The number can either be fixed beforehand or dynamic, i.e., vary during the sorting process. Different strategies for moving the fingers yield different algorithms. Some general conditions which all strategies should satisfy are stated, and two such strategies are given. Furthermore, a method for designing dynamic algorithms that allocate an optimal number of fingers is provided. For any specific strategy satisfying our conditions, this method yields an optimal algorithm. © Springer-Verlag Berlin Heidelberg 1989.

关键词： Finger search trees Geometric interpretation Local insertion sort Measures Presortedness sorting algorithm

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Evaluations of a multiple SOMs method for estimating missing values 7

Evaluations of a multiple SOMs method for estimating missing...

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IEEE/SICE International Symposium on System Integration (SII)

作者： Arima, Kouki Okada, Nobuhiro Tsuji, Yasutaka Kiguchi, Kazuo Kyushu Univ Grad Sch Engn Fukuoka 812 Japan Univ Kitakyushu Fac Environm Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781479969449

Data mining, which is a technique to extract variable information from enormous data, becomes more and more important. Real data often has missing values. Therefore, a method for estimating the missing data is required in application of data mining. Using multiple self-organizing maps (MSOM) proposed by Kikuchi et al. is one of such estimating method. This method does not need a concrete mathematical model and is also available for nonlinear data. However the performance for various missing patterns were unclear, in addition, the comparisons with conventional imputation methods were not provided. This paper demonstrates the performance and the comparison results through simulation experiments with various missing patterns and conventional methods.

关键词： data mining self-organising feature maps MSOM concrete mathematical model imputation methods missing values estimation multiple SOM method multiple self-organizing maps nonlinear data Accuracy Classification algorithms Data mining Data models Educational institutions Neurons Vectors data mining self-organising feature maps sorting algorithm data models Educational institutions Mathematical Model missing value Neurons

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