Search Results - (( pattern means algorithm ) OR ( patterns ((based algorithm) OR (acs algorithm)) ))

Facial image retrieval on semantic features using adaptive mean genetic algorithm by Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli

“…The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). …”
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Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
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Pattern Classification of Human Epithelial Images by Mohd Isa, Mohd Fazlie

“…Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage.…”
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Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering by Rahmah, Mokhtar, Raza, Muhammad Ahsan, Fauziah, Zainuddin, Nor Azhar, Ahmad, Raza, Muhammad Fahad, Raza, Binish

“…In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. …”
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HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC by Jamil, Nur Farahim

“…The algorithm gives a mean accuracy of 84% out of 125 test images.…”
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Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

“…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. Nonetheless, the process of detecting anomalies in streaming data is laborious. …”
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Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control by Mohd Hanafi, Muhammad Sidik

“…Addition of hybrid automata algorithm to run pattern and non-pattern recognition based control methods is an advantage to increase accuracy in differentiating forward stroke or hand return activity. …”
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Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

“…Besides, some real data sets were examined to validate the proposed algorithm. Empirical evidences based on simulated data sets indicated that the proposed modified k-means algorithm is able to recognise the optimum number of clusters for uncorrelated data sets. …”
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Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means by Chimphlee, Witcha, Md. Sap, Mohd. Noor, Abdullah, Abdul Hanan, Chimphlee, Siriporn

“…The objective of this paper is to describe a rough sets and fuzzy c-means algorithms and discuss its usage to detect intrusion in a computer network. …”
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Customer segmentation on clustering algorithms by Toh, Wei Xuan

“…Firstly, descriptive analysis is performed to explore the characteristics of the dataset. Then, k-means, DBSCAN, and GMM clustering algorithms are applied to segment customers based on their buying behaviour. …”
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A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques by Sharifah Sakinah, Syed Ahmad

“…Clustering analysis has been considered as a useful means for identifying patterns in dataset. The aim for this paper is to propose a comparison study between two well-known clustering algorithms namely fuzzy c-means (FCM) and k-means. …”
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Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing by Alobaedy, Mustafa Muwafak Theab

“…The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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Pemahaman guru matematik Tahun Enam tentang pembahagian nombor bulat / Hoi Sim Min by Hoi , Sim Min

“…This study also found that teachers have four general patterns of thought and an algorithm to solve the problem of division of whole numbers, that is, the general pattern of thought of partition, measurement, repeated subtraction, the inverse of multiplication , and long division algorithm. …”
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A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

“…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. …”

Clustering of rainfall data using k-means algorithm by Mohd Sham, Mohamad, Yuhani, Yusof, Ku Muhammad Na’im, Ku Khalif, Mohd Khairul Bazli, Mohd Aziz

“…K-Means algorithm is used to obtain optimal rainfall clusters. …”
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Biceps brachii surface EMG classification using neural networks by Chong, Yee Lin

“…In this module, statistical features such as mean, maximum, variance and standard deviation are computed to represent the signal pattern. …”
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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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Sistem Pengecaman Bentuk Berdasarkan FPGA by Zainal Ariffin, Nur Bayani

“…Pattern recognition means that the definition, identification and the classification by deciding the characteristics of that pattern which is need to be known. …”
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