Search Results - (( pattern machine algorithm ) OR ( patterns among algorithmic ))*

Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

“…An algorithm called Flex (Frequent lexicographic patterns) has been proposed in obtaining a good performance of searching li-equent patterns. …”
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The analysis of road traffic fatality pattern for Selangor, Malaysia case study by Radzuan, N. Q., Mohd Hasnun Ariff, Hassan, Abu Kassim, K. A., Ab. Rashid, A. A., Intan Suhana, Mohd Razelan, Nur Aqilah, Othman

“…The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. …”
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Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

“…This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
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Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

“…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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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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Prediction of sleep pattern for university students using machine learning by Widya, Muhammad Faishal Atha, Utoro, Rio Korio, Roedavan, Rickman, Soegiarto, Duddy, Moorthy, Kohbalan, Pratondo, Agus

“…This study aims to predict sleep patterns among university students using machine learning techniques, focusing on the classification of regular and irregular sleep behaviors. …”
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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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DNA enhancer prediction using machine learning techniques with novel feature representation by Fong, Pui Kwan

“…Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences.…”
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Predicting the success of suicide terrorist attacks using different machine learning algorithms by Hossain, Md Junayed, Abdullah, Sheikh Md, Barkatullah, Mohammad, Miahh, Md Saef Ulla, Sarwar, Talha, Monir, Md Fahad

“…With an accuracy rate of 98.4% and an AUC-ROC score of 99.9%, the Random Forest classifier was the most accurate among all other algorithms. This model is more trustworthy than previous work and provides a useful comparison between machine learning methods and an artificial neural network because it is less dependent and has a multiclass target feature.…”
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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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Underwater Image Recognition using Machine Learning by Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S

“…It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. …”
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Early Detection Of ADHD Among Children Using Machine Learning by Nur Atiqah, Kamal

“…Machine learning algorithms can analyze fMRI data and identify biomarkers indicative of ADHD, enabling accurate classification. …”
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