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Classification of Patients with Heart Failure

Classification of Patients with Heart Failure

引用

18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Bayrak, Tuncay Ogul, Hasan Baskent Univ Bilgisayar Muhendisligi Bolumu Etimesgut Ankara Turkey

ISBN: (纸本)9781479975723

Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.

关键词： Bayes methods biomedical transducers echocardiography feature extraction learning (artificial intelligence) medical signal processing signal classification ultrasonic transducers dataset echocardiogram signals feature classification heart anatomy heart failure heart physiology high-frequency sound waves machine learning repository database naive Bayes database patient classification Abstracts Classification algorithms Echocardiography Educational institutions Heart Indexes Echocardiography ultrasonic transducers cardiac functions biomedical transducers Heart Failure sorting algorithm medical signal processing signal classification Educational institutions Bayes methods Heart Dataset Feature extraction Heart—Anatomy

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Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Feature Selection for Classification Incorporating Less Mean...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Wosiak, Agnieszka Zakrzewska, Danuta Lodz Univ Technol Inst Informat Technol PL-90924 Lodz Poland

ISBN: (纸本)9788360810583

In medical diagnostics there is a constant need of searching for new methods of attribute acquiring, but it is difficult to asses if these new features can support the existing ones and can be useful in medical inference. In the paper the methodology of discovering features which are less informative while considering independently, however meaningful for diagnosis making, is investigated. The proposed methodology can contribute to better use of attributes, which have not been considered in the diagnostics process so far. The experimental study, which concerns arterial hypertension as one of the civilization diseases demanding early detection and improved treatment is presented. The experiments confirmed that additional attributes enable obtaining the diagnostic results comparable to the ones received by using the most obvious features.

关键词： algorithm design and analysis Classification algorithms Clustering algorithms Hypertension Machine learning algorithms Medical diagnostic imaging Strain algorithm design and analysis Machine learning algorithms sorting algorithm Clustering algorithms Hypertension Feature extraction diagnostic imaging Diagnosis Medical

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Experimental Study on the Multiple Use of Training Patterns in Confidence-Weighted On-Line Learning for Fuzzy Classifiers

Experimental Study on the Multiple Use of Training Patterns ...

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IEEE International Conference on Granular Computing (GrC)

作者： Nakashima, Tomoharu Piera, Julien Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479954643

On-line learning for fuzzy classifiers are investigated in this paper. In the on-learning problems, it is assumed that there are only a single training patterns available at a time. This paper extends the assumption by allowing multiple training patterns available for incrementally update the fuzzy classifiers. Confidence-weighted learning is used for the on-line learning of the fuzzy classifiers. Computational experiments are conducted to show the high-performance of the fuzzy classifiers with the confidence-weighted learning.

关键词： Classification algorithms Fuzzy sets Gaussian distribution Mathematical model Optimization Support vector machine classification Training Support vector machine classification sorting algorithm Functional training Fuzzy Set Theory Gaussian distribution Classifiers Fuzzy Mathematical Model Training confidence IN-LINE

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An Online System for algorithm Benchmarking 5

An Online System for Algorithm Benchmarking

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5th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Toth, Janos Kovacs, Laszlo Harangi, Balazs Kiss, Csaba Mohacsi, Andras Orosz, Zoltan Hajdu, Andras Univ Debrecen Inst Math POB 12 H-4010 Debrecen Hungary Univ Debrecen Fac Informat H-4010 Debrecen Hungary

We present a platform that will aid researchers in developing algorithms for specific (primarily image processing) tasks by providing training data sets with ground truth and with providing evaluation of the outputs o... 详细信息

ISBN: (纸本)9781479972807

关键词： image processing online algorithm benchmarking system training data sets Benchmark testing Classification algorithms Educational institutions Image processing Retinopathy Standards Training data Image processing Benchmark testing Training data sorting algorithm Educational institutions Retinopathy Online Systems Dataset

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Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

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International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms Clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease Clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

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Embedded and Modular Video Processing Design Platform

Embedded and Modular Video Processing Design Platform

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IEEE International Conference on Electro/Information Technology (EIT)

作者： Gonnot, Thomas Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9781479947744

Research and industry increasingly rely on image processing to analyze an environment. Most image processing requires significant computing power and consequently a complex processing unit. This paper presents a hardware specific platform that computes common image processing algorithms and extracts the information from a video stream. By this arrangement, the image processing can be customized for a specific application, using the same embedded system. This platform also simplifies the development of image processing systems and algorithms by focusing on higher level algorithmic operations.

关键词： algorithm design and analysis Cameras Classification algorithms Colored noise Feature extraction Image color analysis algorithm design and analysis image colour analysis Colored noise sorting algorithm Cameras Feature extraction Image processing Process design Image processing systems Platform

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CONSTRUCTION ACTIVITY RECOGNITION FOR SIMULATION INPUT MODELING USING MACHINE LEARNING CLASSIFIERS

CONSTRUCTION ACTIVITY RECOGNITION FOR SIMULATION INPUT MODEL...

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Winter Simulation Conference

作者： Akhavian, Reza Behzadan, Amir H. Univ Cent Florida 4000 Cent Florida Blvd Orlando FL 32816 USA

ISBN: (纸本)9781479974863

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The presented research is an effort to alleviate this problem by exploring whether built-in mobile sensors such as global positioning system (GPS), accelerometer, and gyroscope can be used as ubiquitous data collection and transmission nodes to extract activity durations for construction simulation input modeling. Collected sensory data are classified using machine learning algorithms for detecting various construction equipment actions. The ability of the designed methodology in correctly detecting and classifying equipment actions was validated using sensory data collected from a front-end loader. Ultimately, the developed algorithms can supplement conventional simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions.

关键词： civil engineering computing construction equipment digital simulation learning (artificial intelligence) loading equipment pattern classification GPS accelerometer built-in mobile sensors construction activity recognition construction equipment action classification construction equipment action detection construction simulation input modeling field data sensing front-end loader global positioning system gyroscope machine learning classifiers ubiquitous data collection nodes ubiquitous data transmission nodes Accelerometers Classification algorithms Data models Feature extraction Global Positioning System Gyroscopes Sensors Global Positioning Systems Gyroscopes accelerometers Front end loaders loading equipment Construction equipment sorting algorithm data models Digital simulation Computer Simulation Pattern recognition Feature extraction Sensors

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JA-trie: Entropy-Based Packet Classification 15

JA-trie: Entropy-Based Packet Classification

引用

15th IEEE International Conference on High Performance Switching and Routing (HPSR)

作者： Antichi, Gianni Callegari, Christian Moore, Andrew W. Giordano, Stefano Anastasi, Enrico Univ Cambridge Comp Lab Cambridge CB2 1TN England Univ Pisa Dept Informat Engn I-56100 Pisa Italy

ISBN: (纸本)9781479916337

Any improvement in packet classification performance is crucial to ensure Internet functions continue to track the ever-increasing link capacities. Packet classification is the foundation of many Internet functions: from fundamental packet-forwarding to advanced features such as Quality of Service enforcement, monitoring and security functions. This work proposes a novel trie-based classification algorithm, named Jump-Ahead Trie (JA-trie), utilizing an entropy-based pre-processing phase and a novel approach to wildcard matching. Through extensive experimental tests, we demonstrate that our proposed algorithm is able to outperform a range of state-of-the-art classification algorithms.

关键词： Data structures Entropy Hardware IP networks Internet Memory management Quality of service Store management packet classification Quality of service Internet Protocol networks Data structures Computer hardware Internets entropy sorting algorithm

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Sentence-based Plot Classification for Online Review Comments

Sentence-based Plot Classification for Online Review Comment...

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IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Iwai, Hidenari Hijikata, Yoshinori Ikeda, Kaori Nishida, Shogo Osaka Univ Grad Sch Engn Sci Suita Osaka 565 Japan

ISBN: (纸本)9781479941438

Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence in a user review as plot-related or non-plot-related and hides plot descriptions from user reviews. We tested five common machine-learning algorithms to ascertain the appropriate algorithm to address this problem. We also proposed a method of generalizing people's names, which we think is strongly related to the plot description. We verified its contribution to the classification results. Finally, we implemented a display interface of user reviews in which users can control the level of plot hiding.

关键词： customer satisfaction learning (artificial intelligence) pattern classification retail data processing user interfaces classilication results display interface machine-learning algorithms online review comments plot descriptions plot hiding purchased item review author sentence-based plot classification shopping sites user review Classification algorithms DVD Educational institutions Logistics Machine learning algorithms Motion pictures Support vector machines bag of words machine learning plot classification sentiment analysis text mining Machine learning algorithms Cinema business data processing sorting algorithm Educational institutions text mining User Interface Device Component Pattern recognition Consumer Satisfaction Logistics Support Vector Network Films Digital versatile discs DIGITAL VIDEO DISC Consumers

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Using ABC algorithm for Classification and Analysis on Effects of Control Parameters

Using ABC Algorithm for Classification and Analysis on Effec...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Dilmac, Selim Korurek, Mehmet Istanbul Tech Univ Fen Bilimleri Enstitusu Maslak Sariyer Is Turkey

ISBN: (纸本)9781479975723

In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.

关键词： classification data analysis electrocardiography feature extraction feature selection learning (artificial intelligence) medical information systems medical signal processing signal classification swarm intelligence ABC algorithm effectiveness BUPA IRIS MABC algorithm based classifier MCN parameter MITBIH ECG database PIMA SCTR control parameter UCI Machine Learning Repository WINE analysis algorithm artificial bee colony algorithm based classifier class overlap classification algorithm classification success rate control parameter effect feature effect limit parameter multidimensional dataset classification validity index Classification algorithms Educational institutions Electrocardiography Indexes Iris Machine learning algorithms Particle swarm optimization Feature extraction sorting algorithm Electrocardiography Particle swarm optimization Wine medical information systems control parameter INTEGRATED RISK INFORMATION SYSTEM Machine learning algorithms medical signal processing signal classification Educational institutions Classifiers activity based costing Iris

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