Search Results - (( developing function method algorithm ) OR ( learning selection method algorithm ))

Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks by Krishnasamy, Ganesh, Paramesran, Raveendran

“…We develop an efficient iterative algorithm to optimize it since the objective function of the proposed method is non-smooth and difficult to solve. …”
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Development of multi-objective load shedding optimization via back tracking search algorithm with novel reactive power tracing index by Verayiah R., Mohamed A., Shareef H., Abidin I.H.Z.

Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…This paper�s novelty includes introducing a new method for selecting inputs and developing a new model for predicting water levels. …”

Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Muttaqi K.M.

Support directional shifting vector: A direction based machine learning classifier by Kowsher, Md., Hossen, Imran, Tahabilder, Anik, Prottasha, Nusrat Jahan, Habib, Kaiser, Zafril Rizal, M Azmi

“…The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. …”
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Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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Feedforward neural network for solving particular fractional differential equations by Admon, Mohd Rashid

“…This research aims to develop a scheme based on a feedforward neural network (FNN) with a vectorized algorithm (FNNVA) for solving FDEs in the Caputo sense (FDEsC) using selected first-order optimization techniques: simple gradient descent (GD), momentum method (MM), and adaptive moment estimation method (Adam). …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…Three algorithms of Linear (PURELIN), hyperbolic tangent sigmoid (TANSIG) and logistic sigmoid (LOGSIG) activation functions were selected for output layer. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm by Lipu M.S.H., Ayob A., Saad M.H.M., Hussain A., Hannan M.A., Faisal M.

Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness by Noori, Mustafa Sabah, Sahbudin, Ratna K.Z., Sali, Aduwati, Hashim, Fazirulhisyam

“…Many machine learning algorithms excel at handling problems with conflicting objectives. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Effective query structuring with ranking using named entity categories for XML retrieval by Roko, Abubakar

“…Finally, the system employs a query formulation via node algorithm (QRYFv) algorithm to improve the selection of structured queries that best match user query Experiments have been conducted to evaluate the performance of the proposed enrichment method, XKQSS and RAXKQSS. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction by Shoorangiz, Mohammadreza

“…The development sequence of such method can be divided into two parts: Developing network structure to employ complex fuzzy logic and proposing learning algorithm to train the system. …”
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A Stepper Motor Design Optimization Using by Wong, Chin Wei

“…In order to achieve the optimum design, Genetic Algorithms (GAs) approach has been applied. GAs approach is selected because it is a powerful and broadly applicable stochastic search and optimization techniques that works for many problems that are very difficult to solve by conventional methods. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

“…MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

“…Particularly, GA is utilized to determine the optimal number of hidden layers, number of neurons in each hidden layer, type of training algorithm, type of activation function of hidden and output neurons, initial weight, learning rate, momentum term, and epoch size of a multilayer feed-forward ANN. …”
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Random forest algorithm for co2 water alternating gas incremental recovery factor prediction by Belazreg, L., Mahmood, S.M., Aulia, A.

“…Based on literature review, Random Forest (RF) learning method was selected to predict the WAG incremental recovery factor and rank the input vector based on their importance. …”
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