Search Results - (( data identification using algorithm ) OR ( pattern classification using algorithm ))
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1
MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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2
Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…In this project, a classification system is proposed with the one-dimensional local binary pattern algorithm (1D-LBP) with the K-Nearest Neighbour (K-NN) classifier and the system is developed by using a Raspberry Pi 3.There are eight different subjects used to classify in this classification system and each subject consists of seven samples of normalized iris image as input to the system. …”
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Monograph -
3
MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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Citation Index Journal -
4
The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment
Published 2007“…This , paper discusses the development of data mining and pattern recognition algorithm to handle the complexity of hyperspectral remote sensing images in Geographical Information Systems environment. …”
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5
Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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6
Frequent patterns minning of stock data using hybrid clustering association algorithm
Published 2009“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data. …”
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7
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
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8
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
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9
Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…It has applied in different aspects such as pattern recognition, alphabet or digit classification, handwriting recognition, speech recognition, fingerprint identification, data mining, robots and etc. …”
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10
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…First we briefly give some data analysis background. Then a review of different methods currently available that can be used to solve clustering and classification problems is also given. …”
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11
Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari
Published 2018“…For the ANN, Multilayer feed-forward networks are trained using Back Propagation (BP) learning algorithm and for the KNN, is used the most common distance which is Euclidean. …”
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Student Project -
12
Data Classification and Its Application in Credit Card Approval
Published 2004“…This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. …”
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Final Year Project -
13
Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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14
Identification model for hearing loss symptoms using machine learning techniques
Published 2014“…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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15
An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…Sparse representation prevents over-fitting in the classifier by eliminating redundancies and evaluating high-frequency patterns between feature vectors. Performance of proposed methods surpass the classic bag of words algorithm for plant identification tasks.…”
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Article -
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…They look for the procedures or algorithms that maximize the use of leaf databases for plant predictive modelling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. …”
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17
Invariants discretization for individuality representation in handwritten authorship
Published 2008“…An experiment has been conducted using IAM database with 3520 training data and 880 testing data (70% training data and 30% testing data) and 2639 training data and 1760 testing data (60% training data and 40% testing data). …”
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Conference or Workshop Item -
18
APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN
Published 2011“…In our research, extraction pattern from the first module will be fed to this algorithm and is used to make the prediction of named entity candidate category. …”
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19
Feature selection for Malaysian medicinal plant leaf shape identification and classification
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20
A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar
Published 2007“…It is well known that the use of a gas sensor array and pattern recognition system offers an effective technique for the identification of volatile organic molecules because of the poor selectivity of a lot of other gas sensors. …”
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