Search Results - (( developing function mining algorithm ) OR ( learning implementation modified algorithm ))

Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network by Kang, Miew How

“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

“…This work proposed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning algorithm. …”
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The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment by Nanna Suryana, Herman

“…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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Development and applications of metaheuristic algorithms in engineering design and structural optimization / Ali Sadollah by Ali, Sadollah

“…In addition, two novel optimization methods are developed and presented which are named the mine blast algorithm (MBA) and the water cycle algorithm (WCA). …”
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

“…The developed logic mining will be used to analyze the Alzheimer's Disease Neuroimaging Initiative dataset.…”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task by Yana Mazwin Mohmad Hassim, Rozaida Ghazali

“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level by Mohd Harizi, Muhammad Hafizam Afiq

“…Adding the uncertain class the best accuracy obtained was 98.5% using the SMO algorithm. A predictive classification model that determines the accuracy for three classification classes was developed accordingly using the SMO algorithm.…”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

“…The research implements stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. …”
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Context aware app recommendation using email semantic analysis by Tang, Zi Cong

“…The recommendation system is planned to develop by using data mining and machine learning. …”
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Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives by Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.

“…This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. …”

A new text-based w-distance metric to find the perfect match between words by Ali, M., Jung, L.T., Hosam, O., Wagan, A.A., Shah, R.A., Khayyat, M.

“…The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. …”
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Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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Stock price monitoring system by Ng, Chun Ming

“…Consequently, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of the prediction algorithms. The methodology used in this project is Cross-Industry Standard Process for Data Mining (CRISP-DM), which is a common standard for data mining projects. …”
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An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Muhammad Ikram, Mohd Rashid

“…This paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…An efficient iterative algorithm is developed to optimize the objective function of the proposed algorithm since it is non-smooth and difficult to solve. …”
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Prediction Of Leaf Mechanical Properties Based On Geometry Features With Data Mining by H’ng, Choo Wooi

“…The linear models and rules developed from the M5P algorithm were adopted for the FT indicator prediction modelling of 14 attributes. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition by Koh J.S., Tan R.H.G., Lim W.H., Tan N.M.L.

“…A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. …”