Search results for: topography. Subject classification: 86 A 05
1404 Electronic Nose Based On Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Oat Milk
Authors: A. Deswal, N. S. Deora, H. N. Mishra
Abstract:
The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyze spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), Discriminant Factorial Analysis (DFA) and Soft Independent Modelling by Class Analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable countsshowed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20hrs and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.
Keywords: Electronic-nose, bacteriological, shelf-life, classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32611403 Aerodynamic Interaction between Two Speed Skaters Measured in a Closed Wind Tunnel
Authors: Ola Elfmark, Lars M. Bardal, Luca Oggiano, H˚avard Myklebust
Abstract:
Team pursuit is a relatively new event in international long track speed skating. For a single speed skater the aerodynamic drag will account for up to 80% of the braking force, thus reducing the drag can greatly improve the performance. In a team pursuit the interactions between athletes in near proximity will also be essential, but is not well studied. In this study, systematic measurements of the aerodynamic drag, body posture and relative positioning of speed skaters have been performed in the low speed wind tunnel at the Norwegian University of Science and Technology, in order to investigate the aerodynamic interaction between two speed skaters. Drag measurements of static speed skaters drafting, leading, side-by-side, and dynamic drag measurements in a synchronized and unsynchronized movement at different distances, were performed. The projected frontal area was measured for all postures and movements and a blockage correction was performed, as the blockage ratio ranged from 5-15% in the different setups. The static drag measurements where performed on two test subjects in two different postures, a low posture and a high posture, and two different distances between the test subjects 1.5T and 3T where T being the length of the torso (T=0.63m). A drag reduction was observed for all distances and configurations, from 39% to 11.4%, for the drafting test subject. The drag of the leading test subject was only influenced at -1.5T, with the biggest drag reduction of 5.6%. An increase in drag was seen for all side-by-side measurements, the biggest increase was observed to be 25.7%, at the closest distance between the test subjects, and the lowest at 2.7% with ∼ 0.7 m between the test subjects. A clear aerodynamic interaction between the test subjects and their postures was observed for most measurements during static measurements, with results corresponding well to recent studies. For the dynamic measurements, the leading test subject had a drag reduction of 3% even at -3T. The drafting showed a drag reduction of 15% when being in a synchronized (sync) motion with the leading test subject at 4.5T. The maximal drag reduction for both the leading and the drafting test subject were observed when being as close as possible in sync, with a drag reduction of 8.5% and 25.7% respectively. This study emphasize the importance of keeping a synchronized movement by showing that the maximal gain for the leading and drafting dropped to 3.2% and 3.3% respectively when the skaters are in opposite phase. Individual differences in technique also appear to influence the drag of the other test subject.Keywords: Aerodynamic interaction, drag cycle, drag force, frontal area, speed skating.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10191402 ANN-Based Classification of Indirect Immuno Fluorescence Images
Authors: P. Soda, G.Iannello
Abstract:
In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
Keywords: Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15591401 Using PFA in Feature Analysis and Selection for H.264 Adaptation
Authors: Nora A. Naguib, Ahmed E. Hussein, Hesham A. Keshk, Mohamed I. El-Adawy
Abstract:
Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.
Keywords: Adaptation, feature selection, H.264, Principal Feature Analysis (PFA)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15991400 Clustering Methods Applied to the Tracking of user Traces Interacting with an e-Learning System
Authors: Larbi Omar, Elberrichi Zakaria
Abstract:
Many research works are carried out on the analysis of traces in a digital learning environment. These studies produce large volumes of usage tracks from the various actions performed by a user. However, to exploit these data, compare and improve performance, several issues are raised. To remedy this, several works deal with this problem seen recently. This research studied a series of questions about format and description of the data to be shared. Our goal is to share thoughts on these issues by presenting our experience in the analysis of trace-based log files, comparing several approaches used in automatic classification applied to e-learning platforms. Finally, the obtained results are discussed.Keywords: Classification, , e-learning platform, log file, Trace.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14721399 A New Method of Combined Classifier Design Based on Fuzzy Neural Network
Abstract:
To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12131398 A Methodology for Characterising the Tail Behaviour of a Distribution
Authors: Serge Provost, Yishan Zang
Abstract:
Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.
Keywords: Arctangent transformation, change of variables, heavy-tailed distributions, tail classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6661397 Effective Sonar Target Classification via Parallel Structure of Minimal Resource Allocation Network
Authors: W.S. Lim, M.V.C. Rao
Abstract:
In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity than PNN. An enhanced version called parallel MRAN (pMRAN) is proposed to solve this problem and is proven to be stable in prediction and also outperformed the original MRAN.Keywords: Ultrasonic sensing, target classification, minimalresource allocation network (MRAN), probabilistic neural network(PNN), stability-plasticity dilemma.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15851396 A Model to Determine Atmospheric Stability and its Correlation with CO Concentration
Authors: Kh. Ashrafi, Gh. A. Hoshyaripour
Abstract:
Atmospheric stability plays the most important role in the transport and dispersion of air pollutants. Different methods are used for stability determination with varying degrees of complexity. Most of these methods are based on the relative magnitude of convective and mechanical turbulence in atmospheric motions. Richardson number, Monin-Obukhov length, Pasquill-Gifford stability classification and Pasquill–Turner stability classification, are the most common parameters and methods. The Pasquill–Turner Method (PTM), which is employed in this study, makes use of observations of wind speed, insolation and the time of day to classify atmospheric stability with distinguishable indices. In this study, a model is presented to determination of atmospheric stability conditions using PTM. As a case study, meteorological data of Mehrabad station in Tehran from 2000 to 2005 is applied to model. Here, three different categories are considered to deduce the pattern of stability conditions. First, the total pattern of stability classification is obtained and results show that atmosphere is 38.77%, 27.26%, 33.97%, at stable, neutral and unstable condition, respectively. It is also observed that days are mostly unstable (66.50%) while nights are mostly stable (72.55%). Second, monthly and seasonal patterns are derived and results indicate that relative frequency of stable conditions decrease during January to June and increase during June to December, while results for unstable conditions are exactly in opposite manner. Autumn is the most stable season with relative frequency of 50.69% for stable condition, whilst, it is 42.79%, 34.38% and 27.08% for winter, summer and spring, respectively. Hourly stability pattern is the third category that points out that unstable condition is dominant from approximately 03-15 GTM and 04-12 GTM for warm and cold seasons, respectively. Finally, correlation between atmospheric stability and CO concentration is achieved.Keywords: Atmospheric stability, Pasquill-Turner classification, convective turbulence, mechanical turbulence, Tehran.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 64381395 A Human Activity Recognition System Based On Sensory Data Related to Object Usage
Authors: M. Abdullah-Al-Wadud
Abstract:
Sensor-based Activity Recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.
Keywords: Naïve Bayesian-based classification, Activity recognition, sensor data, object-usage model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18141394 Fuzzy Based Visual Texture Feature for Psoriasis Image Analysis
Authors: G. Murugeswari, A. Suruliandi
Abstract:
This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Psoriasis Area Severity Index value. For classification and segmentation, the Nearest Neighbor algorithm is applied. We have obtained promising results for identifying affected lesions by using the roughness index and severity level estimation.
Keywords: Fuzzy texture feature, psoriasis, roughness feature, skin disease.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21041393 Mapping Paddy Rice Agriculture using Multi-temporal FORMOSAT-2 Images
Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu
Abstract:
Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17871392 A General Framework for Knowledge Discovery Using High Performance Machine Learning Algorithms
Authors: S. Nandagopalan, N. Pradeep
Abstract:
The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17021391 Opinion Mining Framework in the Education Domain
Authors: A. M. H. Elyasir, K. S. M. Anbananthen
Abstract:
The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.
Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29551390 1/Sigma Term Weighting Scheme for Sentiment Analysis
Authors: Hanan Alshaher, Jinsheng Xu
Abstract:
Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.
Keywords: Sentiment analysis, term weighting scheme, 1/sigma.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5111389 Evaluation of Natural Drainage Flow Pattern, Necessary for Flood Control, Using Digitized Topographic Information: A Case Study of Bayelsa State Nigeria
Authors: Collins C. Chiemeke
Abstract:
The need to evaluate and understand the natural drainage pattern in a flood prone, and fast developing environment is of paramount importance. This information will go a long way to help the town planners to determine the drainage pattern, road networks and areas where prominent structures are to be located. This research work was carried out with the aim of studying the Bayelsa landscape topography using digitized topographic information, and to model the natural drainage flow pattern that will aid the understanding and constructions of workable drainages. To achieve this, digitize information of elevation and coordinate points were extracted from a global imagery map. The extracted information was modeled into 3D surfaces. The result revealed that the average elevation for Bayelsa State is 12 m above sea level. The highest elevation is 28 m, and the lowest elevation 0 m, along the coastline. In Yenagoa the capital city of Bayelsa were a detail survey was carried out showed that average elevation is 15 m, the highest elevation is 25 m and lowest is 3 m above the mean sea level. The regional elevation in Bayelsa, showed a gradation decrease from the North Eastern zone to the South Western Zone. Yenagoa showed an observed elevation lineament, were low depression is flanked by high elevation that runs from the North East to the South west. Hence, future drainages in Yenagoa should be directed from the high elevation, from South East toward the North West and from the North West toward South East, to the point of convergence which is at the center that flows from South East toward the North West. Bayelsa when considered on a regional Scale, the flow pattern is from the North East to the South West, and also North South. It is recommended that in the event of any large drainage construction at municipal scale, it should be directed from North East to the South West or from North to South. Secondly, detail survey should be carried out to ascertain the local topography and the drainage pattern before the design and construction of any drainage system in any part of Bayelsa.
Keywords: Bayelsa, Digitized Topographic Information, Drainage, Flood.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22491388 In Search of an SVD and QRcp Based Optimization Technique of ANN for Automatic Classification of Abnormal Heart Sounds
Authors: Samit Ari, Goutam Saha
Abstract:
Artificial Neural Network (ANN) has been extensively used for classification of heart sounds for its discriminative training ability and easy implementation. However, it suffers from overparameterization if the number of nodes is not chosen properly. In such cases, when the dataset has redundancy within it, ANN is trained along with this redundant information that results in poor validation. Also a larger network means more computational expense resulting more hardware and time related cost. Therefore, an optimum design of neural network is needed towards real-time detection of pathological patterns, if any from heart sound signal. The aims of this work are to (i) select a set of input features that are effective for identification of heart sound signals and (ii) make certain optimum selection of nodes in the hidden layer for a more effective ANN structure. Here, we present an optimization technique that involves Singular Value Decomposition (SVD) and QR factorization with column pivoting (QRcp) methodology to optimize empirically chosen over-parameterized ANN structure. Input nodes present in ANN structure is optimized by SVD followed by QRcp while only SVD is required to prune undesirable hidden nodes. The result is presented for classifying 12 common pathological cases and normal heart sound.Keywords: ANN, Classification of heart diseases, murmurs, optimization, Phonocardiogram, QRcp, SVD.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20581387 File Format of Flow Chart Simulation Software - CFlow
Authors: Syahanim Mohd Salleh, Zaihosnita Hood, Hairulliza Mohd Judi, Marini Abu Bakar
Abstract:
CFlow is a flow chart software, it contains facilities to draw and evaluate a flow chart. A flow chart evaluation applies a simulation method to enable presentation of work flow in a flow chart solution. Flow chart simulation of CFlow is executed by manipulating the CFlow data file which is saved in a graphical vector format. These text-based data are organised by using a data classification technic based on a Library classification-scheme. This paper describes the file format for flow chart simulation software of CFlow.Keywords: CFlow, flow chart, file format.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25371386 Improved Body Mass Index Classification for Football Code Masters Athletes, A Comparison to the Australian National Population
Authors: Joe Walsh, Mike Climstein, Ian Timothy Heazlewood, Stephen Burke, Jyrki Kettunen, Kent Adams, Mark DeBeliso
Abstract:
Thousands of masters athletes participate quadrennially in the World Masters Games (WMG), yet this cohort of athletes remains proportionately under-investigated. Due to a growing global obesity pandemic in context of benefits of physical activity across the lifespan, the prevalence of obesity in this unique population was of particular interest. Data gathered on a sub-sample of 535 football code athletes, aged 31-72 yrs ( =47.4, s =±7.1), competing at the Sydney World Masters Games (2009) demonstrated a significantly (p<0.001), reduced classification of obesity using Body Mass Index (BMI) when compared to data on the Australian national population. This evidence of improved classification in one index of health (BMI<30) implies there are either improved levels of this index of health due to adherence to sport or possibly the reduced BMI is advantageous and contributes to this cohort adhering (or being attracted) to masters sport. Given the worldwide focus on the obesity epidemic and the need for a multi-faceted solution to this problem, demonstration of these middle to older aged adults having improved BMI over the general population is of particular interest.Keywords: BMI, masters athlete, rugby union, soccer, touch football
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16641385 The Classification Performance in Parametric and Nonparametric Discriminant Analysis for a Class- Unbalanced Data of Diabetes Risk Groups
Authors: Lily Ingsrisawang, Tasanee Nacharoen
Abstract:
The problems arising from unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many researchers have found that the performance of existing classifiers tends to be biased towards the majority class. The k-nearest neighbors’ nonparametric discriminant analysis is a method that was proposed for classifying unbalanced classes with good performance. In this study, the methods of discriminant analysis are of interest in investigating misclassification error rates for classimbalanced data of three diabetes risk groups. The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification of class-imbalanced data of diabetes risk groups. Data from a project maintaining healthy conditions for 599 employees of a government hospital in Bangkok were obtained for the classification problem. The employees were divided into three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data including the variables of diabetes risk group, age, gender, blood glucose, and BMI were analyzed and bootstrapped for 50 and 100 samples, 599 observations per sample, for additional estimation of the misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples showed nonnormality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. Searching the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions of (0.90:0.05:0.05), (0.80: 0.10: 0.10) and (0.70, 0.15, 0.15). The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k=3 or k=4 and the defined prior probabilities of non-risk: risk: diabetic as 0.90: 0.05:0.05 or 0.80:0.10:0.10 gave the smallest error rate of misclassification. The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.Keywords: Bootstrap, diabetes risk groups, error rate, k-nearest neighbors.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20001384 Two Class Motor Imagery Classification via Wave Atom Sub-Bants
Authors: Nebi Gedik
Abstract:
The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.
Keywords: motor imagery, EEG, Wave atom transform sub-bands, SVM, k-NN
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5761383 Amelioration of Cardiac Arrythmias Classification Performance Using Artificial Neural Network, Adaptive Neuro-Fuzzy and Fuzzy Inference Systems Classifiers
Authors: Alexandre Boum, Salomon Madinatou
Abstract:
This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.
Keywords: Adaptive neuro-fuzzy, artificial neural network, cardiac arrythmias, fuzzy inference systems.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6901382 Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features
Authors: T. Thendral, M. S. Vijaya, S. Karpagavalli
Abstract:
Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Keywords: Classification, Feature extraction, Support vector machine, Training, Writer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23031381 Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features
Authors: T. Thendral, M. S. Vijaya, S. Karpagavalli
Abstract:
Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.Keywords: Classification, Feature extraction, Support vector machine, Training, Writer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16941380 Curriculum of Ethical Education in Slovakia
Authors: Petra Fridrichová, Eva Balážová
Abstract:
Ethical Education is a compulsorily optional subject in primary and secondary schools. The Ethical Education objective is the education of a personality with one´s own identity, with interiorized ethical standards, with mature moral judgement and therefore with the behaviour determined by one´s own beliefs; with a positive attitude to himself/herself and other people and that is why he/she is able to cooperate and to initiate cooperation. In the paper we describe the contents and the principles of Ethical education. We also shows that Ethical education is subject supported primary socialpathological prevention and education to citizenship. In this context we try to show that ethical education contributes to the education of good people who are aware of the necessity to respect social norms and are able to assume responsibility for their own behaviour in any situation at present and in the future.
Keywords: Ethical education, Curriculum of Ethical education, The Contents of Ethical education in Slovakia, The principles of Ethical education
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18461379 Determining Senses for Word Sense Disambiguation in Turkish
Authors: Zeynep Orhan, Zeynep Altan
Abstract:
Word sense disambiguation is an important intermediate stage for many natural language processing applications. The senses of an ambiguous word are the classification of usages for that specific word. This paper deals with the methodologies of determining the senses for a given word if they can not be obtained from an already available resource like WordNet. We offer a method that helps us to determine the sense boundaries gradually. In this method, first we decide on some features that are thought to be effective on the senses and divide the instances first into two, then according to the results of evaluations we continue dividing instances gradually. In a second method we use the pseudo words. We devise artificial words depending on some criteria and evaluate classification algorithms on these previously classified words.
Keywords: Word sense disambiguation, sense determination, pseudo words, sense granularity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13961378 Hybrid Structure Learning Approach for Assessing the Phosphate Laundries Impact
Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed
Abstract:
Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).
Keywords: Classification, Bayesian network; structure learning, K2 algorithm, expert knowledge, surface water analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4971377 Extraction of Significant Phrases from Text
Authors: Yuan J. Lui
Abstract:
Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Although keyphrases are useful, not many documents have keyphrases assigned to them, and manually assigning keyphrases to existing documents is costly. Therefore, there is a need for automatic keyphrase extraction. This paper introduces a new domain independent keyphrase extraction algorithm. The algorithm approaches the problem of keyphrase extraction as a classification task, and uses a combination of statistical and computational linguistics techniques, a new set of attributes, and a new machine learning method to distinguish keyphrases from non-keyphrases. The experiments indicate that this algorithm performs better than other keyphrase extraction tools and that it significantly outperforms Microsoft Word 2000-s AutoSummarize feature. The domain independence of this algorithm has also been confirmed in our experiments.
Keywords: classification, keyphrase extraction, machine learning, summarization
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20431376 Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio
Authors: Urvee B. Trivedi, U. D. Dalal
Abstract:
As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.Keywords: Cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary User (PU), secondary user (SU), Fast Fourier transform (FFT), signal to noise ratio (SNR).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14591375 Calcification Classification in Mammograms Using Decision Trees
Authors: S. Usha, S. Arumugam
Abstract:
Cancer affects people globally with breast cancer being a leading killer. Breast cancer is due to the uncontrollable multiplication of cells resulting in a tumour or neoplasm. Tumours are called ‘benign’ when cancerous cells do not ravage other body tissues and ‘malignant’ if they do so. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage it is the primary imaging modality for screening and diagnosis of this cancer type. This paper presents an automatic mammogram classification technique using wavelet and Gabor filter. Correlation feature selection is used to reduce the feature set and selected features are classified using different decision trees.
Keywords: Breast Cancer, Mammogram, Symlet Wavelets, Gabor Filters, Decision Trees
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1740