Search Results - (( pattern matching algorithm ) OR ((( patterns among algorithm ) OR ( patterns acs algorithm ))))*

The application of voltage sags pattern to locate a faulted section in distribution network by Mokhlis, Hazlie, Li, H.Y., Khalid, A.R.

“…A pattern matching approach between simulated voltage sag and measurement is proposed to identify the faulted section. …”
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Block Matching Algorithm (BMA) of the Hybrid Adaptive Rood Pattern Search (ARPS) Based on Its Motion Speed by Faizul Hadi Jamil, Ali Chekima, Wong Hock Tze @ Farrah Wong, Rosalyn R Porle, Razak Ali Lee, Ismail Saad

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Investigation of block matching algorithm for video coding by Faizul Hadi Mohamad Jamil

“…The Spatial model exploits the redundancy in a single video frame (I frame) while the temporal model exploits the redundancy among frames (P frame). The temporal model deals with motion estimation (ME) and motion compensation (MC) algorithm with the matching technique called “Block Matching Algorithm” (BMA) to produce the next encoded video frame with motion vector. …”
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Facial image retrieval on semantic features using adaptive mean genetic algorithm by Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli

“…The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.…”
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Mobile Malware Classification via System Calls and Permission for GPS Exploitation by Madihah Mohd Saudi, Husainiamer, MAB

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Nor Hasrul Akhmal Ngadiman, Nor Hasrul Akhmal Ngadiman, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Salwa Mahmood, Salwa Mahmood

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Nor Hasrul Akhmal Ngadiman, Nor Hasrul Akhmal Ngadiman, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Salwa Mahmood, Salwa Mahmood

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Composing multi-relations association rules from crowdsourcing remuneration data by Siti Salwa Salleh, Nurhayati Zakaria, Norjansalika Janom, Syaripah Ruzaini Syed Aris, Noor Habibah Arshad

“…The data comprise a lot of redundancies, therefore an apriori algorithm is used to normalise it by removing redundancies and then extracting significant patterns. …”
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Improving named entity recognition accuracy of gene and protein in biomedical text by Tohidi, Hossein

“…In this study, to handle the above issues in recognizing gene and protein names, a statistical similarity measurement as a pattern matching function is proposed. Our approach is based on an assumption that a named entity occurs among a noun group which is extracted using Brill Part of Speech tagger. …”
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Music Recommender System Using Machine Learning Content-Based Filtering Technique by Foong, Kin Hong

“…These algorithms identify hidden patterns or data groupings without the assistance of a human. …”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…Size was extracted using Extreme Point Detection algorithm and Hit or Miss Transformation method was used to extract the stroke formation pattern. …”
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Herb recognition for mobile apps / Jumriyanie Baba by Baba, Jumriyanie

“…Two texture features have been experimented that are Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG). …”
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Performance Analysis Of Different Hash Functions Using Bloom Filter For Network Intrusion Detection Systems In 32-Bit And 64-Bit Computer Operation Mode. by Tan , Beng Ghee

“…A Bloom Filter is an array of bits that determine whether a given structure of information belongs to it. A Bloom Filter pattern matching algorithm with fast hashing functions is developed for 32-bit and 64-bit computer system. …”
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