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Comparing classification models in the final exam performance prediction

Comparing classification models in the final exam performanc...

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

作者： Gamulin, Jasna Gamulin, Ozren Kermek, Dragutin Univ Zagreb Sch Med Salata 3 Zagreb 41001 Croatia Univ Zagreb Sch Med Dept Phys & Biophys Salata Zagreb Croatia Univ Zagreb Fac Org & Informat Dept Comp & Technol Dept Theoret & Appl Foundat Informat Sci Varazhdin Croatia

ISBN: (纸本)9789532330816

The use of Learning Management Systems (LMS) and the web-based formative and summative assessments during the traditional teaching in classroom provides the huge amount of data on students' behavior and results at the point of time when the course is still in progress. This data could be used for the final exam performance prediction so that the excellent as well as the students requiring help could be detected. The data on 302 students enrolled into the first year Physics course of a biomedical university study program in 2011/2012 have been used. The data were preprocessed by dividing Croatian grading system which comprises 5 grades (range 1-5;1=fail, 5=excellent) into 2 categorical classes in one version of the experiment and into 3 categorical classes in the second version. Several up to date algorithms for classification were applied without and with attributes optimization by a genetic algorithm. Also, since students have 4 chances at 5 exam terms during an academic year, 3 different dependent variables were constructed. In order to evaluate the performance that each of the classification models had the following performance criteria were used: accuracy for all models and sensitivity and area under curve (AUC) for binary classifier systems.

关键词： Accuracy Classification algorithms Data models Education Genetic algorithms Predictive models Support vector machines sorting algorithm data models Genetic algorithm (GA) Prediction models Support Vector Network Education performance prediction Students Performance metrics

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Detection of Aortic Valve Dynamics in Bridge-to-Recovery Feedback Control of the Left Ventricular Assist Device 13

Detection of Aortic Valve Dynamics in Bridge-to-Recovery Fee...

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13th European Control Conference (ECC)

作者： Wang, Yu Faragallah, George Simaan, Marwan A. Univ Louisville Dept Bioengn Cardiovasc Innovat Inst Louisville KY 40202 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9783952426913

Aortic valve dynamics - which implies continuous opening and closing of the aortic valve in each cardiac cycle during the feedback control of the rotary Left Ventricular Assist Devices (LVAD) support - has important clinical implications for patients with mild congestive heart failure. When the LVAD is implanted in such patients as a bridge-to-recovery device, permanent closure of the aortic valve must be avoided by maintaining proper control on the power delivered to the device. In this paper, a new aortic valve dynamics detection algorithm based on a Lagrangian Support Vector Machine (LSVM) model is presented. A detection indicator is derived from the systemic vascular flow signal in the circulatory system using a nonlinear mathematical model of the combined cardiovascular-LVAD system and forms the input to the LSVM classifier. The LSVM classifier is trained and tested to classify the aortic valve dynamics into two states: aortic valve opening and closing (i.e. operating normally) and aortic valve permanently closed. Our results show that the proposed algorithm can detect the aortic valve dynamics effectively in terms of classification accuracy and stability. This classifier will be an integral part in the development of a feedback controller for the LVAD when used on patients as a bridge-to-recovery device. The output of the classifier will be used to adjust the power delivered to the LVAD to ensure that the aortic valve opens and closes normally within each cardiac cycle while at the same time making sure that the physiological demands of the patient are met.

关键词： Adaptive control Classification algorithms Heart Heuristic algorithms Mathematical model Support vector machines Valves Aortic Valve Left Ventricular Assist Device sorting algorithm Heuristic algorithms Support Vector Network Mathematical Model VALVE GATE adaptive control Heart feedback control Classifiers valve opening feedback controller

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One Double-Stranded DNA probes as Classifier of Multi Targeting Strand

One Double-Stranded DNA probes as Classifier of Multi Target...

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International Symposium on Micro-NanoMechatronics and Human Science (MHS)

作者： Adi, Wibowo Sekiyama, Kosuke Nagoya Univ Dept Micronano Syst Engn Nagoya Aichi 4648603 Japan Diponegoro Univ Dept Informat Semarang Central Java Indonesia

ISBN: (纸本)9781479966783

In this paper, we investigate an alternative double-stranded probe (dsProbe) system as detection platform that employs a similarity-based classifiers in DNA strand displacement reactions. We formulate label propagation based on DNA toehold domain as classifier, where a node propagates to neighboring nodes according to their proximity. We observe mismatch attributes of DNA strand sequences to recognize the effect of this model. The attributes are position, length and location number of mismatches. We carry out simulation using Nupack software and also experimental investigation.

关键词： DNA biochemistry biology computing molecular biophysics molecular configurations pattern classification DNA strand displacement reactions DNA strand sequences DNA toehold domain Nupack software double-stranded DNA probes dsProbe system multitargeting strand similarity-based classifiers Classification algorithms Educational institutions Fluorescence Probes Sensors Software biology computing DNA molecular biology sorting algorithm Probes Educational institutions Molecular Conformation Pattern recognition Classifiers computer software biochemistry Sensors Fluorescence DNA strand

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A P300-based BCI Classification algorithm using Median Filtering and Bayesian Feature Extraction

A P300-based BCI Classification Algorithm using Median Filte...

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14th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Li, Xiao-ou Wang, Feng Chen, Xun Ward, Rabab K. Shanghai Univ Sci & Technol Sch Med Instrument & Food Engn Shanghai Med Instrumentat Coll 101 Yingkou Shanghai 200093 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

ISBN: (纸本)9781467345729

A brain computer interface (BCI) system translates a person's brain activity into useful control or communication signals. In this paper, an effective P300-based BCI identification algorithm using median filtering and Bayesian classifier is proposed to improve the classification accuracy and computation efficiency of P300-based BCI. Median filtering is firstly applied to remove noises and Bayesian Linear Discriminant Analysis (BLDA) is then employed for classification. Testing on the P300 speller paradigm in dataset II of 2004 BCI Competition III, we show that a 90% average classification accuracy can be achieved and the highest accuracy is 100%. The proposed method is also computationally efficient and thus it represents a practical implementation for man-computer communication control, especially for on-line applications.

关键词： median filters Filtration Bayesian classifier sorting algorithm Bayesian inference Noise Median Feature extraction computer-mediated communication Common Channel Signaling Brain-Computer Interfaces

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Viseme recognition - A comparative study

Viseme recognition - A comparative study

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IEEE Conference on Advanced Video and Signal Based Surveillance

作者： Leszczynski, M Skarbek, W Warsaw Univ Technol Fac Elect & Informat Technol PL-00665 Warsaw Poland

ISBN: (纸本)0780393856

Three classification algorithms for visual mouth appearances (visemes) which correspond to phonemes and their speech contexts, were compared w.r.t. recognition rate, time complexity, and ROC performance. Two feature extraction procedures were verified The first one is based on the normalized triangle MESH covering mouth area and the color image texture vector indexed by barycentric coordinates. The second procedure performs DFT on the image rectangle including mouth w.r.t. small blocks of DFT coefficients. The classifiers has been designed by PCA approach and by the optimized LDA method which uses two singular subspaces approach. It appears that DFT+LDA exhibits higher recognition rate than MESH+LDA and MESH+PCA methods - 97.6% versus 94.4 and 90.2%, respectively. It is also much faster than MESH+PCA (5ms per one video frame versus 26ms on Pentium IV 3.2GHz) and slower than MESH+LDA (5ms versus 1ms).

关键词： discrete Fourier transforms feature extraction gesture recognition image classification image colour analysis image texture principal component analysis DFT PCA approach barycentric coordinates classification algorithms color image texture vector feature image colour analysis image texture Gesture recognition Image classification Feature extraction sorting algorithm MILLISECOND Centrobaric

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Geometric PDEs on weighted graphs for semi-supervised classification 13

Geometric PDEs on weighted graphs for semi-supervised classi...

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13th International Conference on Machine Learning and Applications (ICMLA)

作者： Toutain, Matthieu Elmoataz, Abderrahim Lezoray, Olivier Normandie Univ UNICAEN ENSICAEN GREYC UMR CNRS 6072 Caen France

ISBN: (纸本)9781479974153

In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classification tasks. These equations are a discrete analogue of well known geometric PDEs, which are widely used in image processing. While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal equation for data classification. The methods are illustrated through semi-supervised classification tasks on databases, where we compare the two algorithms. The results show that these methods perform well regarding the state-of-the-art and are applicable to the task of semi-supervised classification.

关键词： database management systems graph theory partial differential equations pattern classification data classification databases eikonal equations geometric PDE image processing p-Laplacian equations semisupervised classification tasks weighted graphs Classification algorithms Databases Equations Image processing Labeling Standards Vectors data classification Image processing Database eikonal equation database management systems sorting algorithm Partial differential equations Pattern recognition Graph theory Differential equations

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Identification of Features for Machine Learning Analysis for Automatic Arrhythmogenic Event Classification 44

Identification of Features for Machine Learning Analysis for...

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44th Computing in Cardiology Conference (CinC)

作者： Gliner, Vadim Yaniv, Yael Technion IIT Biomed Engn Fac Haifa Israel

ISBN: (纸本)9781538645550;9781538666302

Cardiac arrhythmias are the leading cause of death in the western world, where atrial fibrillation (AF) is the most common arrhythmias. The PhysioNet/CinC 2017 Challenge aimed to trigger a design of an algorithm that accurately classifies short single ECG lead record to 4 categories: normal rhythm, atrial fibrillation, noisy segment or other arrhythmias. The algorithm was optimized on randomly selected records out of the challenge learning set (8528 records after reassuring it includes 60.43% of normal records, 0.54% of noisy records, 9.04% of AF records and 30% of other rhythm disturbance) and tested on hidden test database. A novel R peak detector was used to accurately detect the R peaks. Based on the R peak annotation, the P, Q, S and T peaks were detected and ECG beat morphology was extracted. Quadratic SVM classifier that include combination of 62 features was used to classify the short ECG record to one of the four categories mentioned above. For records which were classified as "normal" additional neural network classifier was applied. Our algorithm reached results of total score (F1) of 0.8 (ranked 24 out a total of 90 open-source software entries), whereas normal rhythm score (F-1n) was 0.9, AF rhythm score (F-1a) of 0.81, and other rhythm score (F-1o) of 0.69.

关键词： Rhythm Classification algorithms Electrocardiography Atrial fibrillation Morphology Support vector machines Noise measurement Rhythms Noise measurement sorting algorithm Electrocardiography Western World Support Vector Network atrial fibrillation Cardiac Arrhythmias Cause of Death Noise

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An Improved KNN Text Classification algorithm based on Simhash 16

An Improved KNN Text Classification Algorithm based on Simha...

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IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Liu, Jie Jin, Ting Pan, Kejia Yang, Yi Wu, Yan Wang, Xin State Grid Sichuan Elect Power Co Informat & Commun Branch Chengdu Sichuan Peoples R China HaiNan Univ Sch Informat Sci & Technol Haikou Hainan Peoples R China Chengdu Duhang Technol Dev Co Ltd Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538607718

An improved KNN text classification algorithm based on Simhash has been proposed by introducing Simhash and the average Hamming distance of adjacent texts as a unit, which solves the problems caused by data imbalance and the large computational overhead in the traditional KNN text classification algorithms. Experimental results demonstrate that the proposed algorithm performs a higher precision, a higher recall and a better F1 value, which shows the validity of the proposed algorithm.

关键词： Hamming distance Training Computational modeling Classification algorithms Feature extraction Electronic mail Internet Hamming distance electronic mail sorting algorithm Computational modeling Feature extraction Text categorization Internets Functional training Training

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Determination of the Defining Features of Texts Written in Isolation with a Naive Bayesian Classifier 8

Determination of the Defining Features of Texts Written in I...

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8th IEEE Integrated STEM Education Conference (ISEC)

作者： Becker, Emily J. Burkart, Dominic Mildner, Judith Tamir, Diana Princeton High Sch Princeton NJ 08540 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781538633090

This study seeks to identify differences between textual samples written in isolation and controls. Isolation is the state of deprivation of one's typical level of social interaction and falls into three categories: prison, seclusion, and isolation. We coded a Naive Bayesian Classifier using the Python package NLTK and ran it with different training to test set ratios and a Leave One Out with authors. The results yielded that accuracy is proportional to training set size. Currently we are analyzing the key features the classifier used to sort the texts and calculating a chance value for the classifier. This is a highly relevant area of study because we hope to elucidate key differences in the thoughts and cognitive states of isolated people, which could predict behavior for socially isolated people.

关键词： Training Bayes methods Python Conferences Classification algorithms Prediction algorithms Sentiment analysis Bayesian classifier Pythonidae Prediction algorithms sorting algorithm Bayes methods Functional training Python Sentiment analysis Classifiers texts Training

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AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017 44

AF Classification from a Short Single Lead ECG Recording: th...

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44th Computing in Cardiology Conference (CinC)

作者： Clifford, Gari D. Liu, Chengyu Moody, Benjamin Lehman, Li-Wei H. Silva, Ikaro Li, Qiao Johnson, A. E. Mark, Roger G. Emory Univ Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA Southeast Univ Sch Instrument Sci & Engn Nanjing Jiangsu Peoples R China MIT Inst Med Engn & Sci Cambridge MA 02139 USA

ISBN: (纸本)9781538645550;9781538666302

The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.

关键词： Electrocardiography Heart beat Noise measurement Training Classification algorithms Lead Cardiology Heart beat Cardiology Noise measurement sorting algorithm Electrocardiography Functional training Lead ecg lead Training alachlor Professional personnel

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