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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, 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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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

“…There are a lot of feature extraction methods and classification methods for iris classification. Classic local binary pattern (LBP) is one of the most useful feature extraction methods. …”
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Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

“…On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. …”
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The classification of urban growth pattern using topological relation border length algorithm: An experimental study by Ghani N.L.A., Abidin S.Z.Z.

The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition by M. A., Ameedeen, Marhaini, M. S.

“…While the algorithm helped to classify different types of wavelengths produced from the sensor, a classification-based algorithm via Pattern Recognition Method will be used to classify and match the food components. …”
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Songket pattern classification using backpropagation neural network / Nik Aidil Syawalni Nik Mazlan by Nik Mazlan, Nik Aidil Syawalni

“…Despite to the several system limitations, the project on classifies Songket pattern using BPNN is consider successful. The outcomes of this investigation show the originality and efficacy of employing BPNNs for Songket pattern classification, resulting in good accuracy rates in the classification of Songket. …”
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Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

“…Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. …”
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MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, khan

“…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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Experimental study of urban growth pattern classification using moving window algorithm by Ghani N.L.A., Abidin S.Z.Z.

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

“…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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A hybrid-based modified adaptive fuzzy inference engine for pattern classification by Sayeed, Md. Shohel, Ramli, Abdul Rahman, Hossen, Md. Jakir, Samsudin, Khairulmizam, Rokhani, Fakhrul Zaman

“…The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher's Iris data set and shown to be very competitive.…”
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