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Improved voting technique for ensemble of MLP system applied on various classification data / Saodah Omar, Iza Sazanita Isa and Junita Mohd Saleh.
Published 2010“…The most well-known ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. …”
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Research Reports -
4
A combinatorial optimization technique using genetic algorithm :a case study in machine layout problem
Published 2007“…Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. …”
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Final Year Project Report / IMRAD -
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A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru
Published 2018“…Therefore, this study will compare the accuracy and the effectiveness of ANFIS and GRNN with the conventional RBFN and MLP algorithms throught experimental verification. …”
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Thesis -
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Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey
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Conference or Workshop Item -
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Conference or Workshop Item -
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Maximizing deep learning-based energy efficiency in 5G downlink MIMO-NOMA systems by using MLP-CNN.
Published 2024“…It can be utilized with multiple convolutional and hidden layers, trained using specific algorithms to solve power allocation problems. …”
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Article -
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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Thesis -
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Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron
Published 2025“…The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). …”
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River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia
Published 2012“…It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. …”
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Citation Index Journal -
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Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting
Published 2015“…Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. The main process in extracting rules from MLP using decision tree for currency exchange rate forecasting can be divided into two stages. …”
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Thesis -
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Pengesanan Kerosakan Bahan Penebat Transformer Dengan Menggunakan Rangkaian Neural Buatan
Published 2006“…Neural network can define the transformer fault through the learning process. Matlab7 is used to design the multilayer perceptron (MLP). Three types of learning algorithm are used in this project to train the MLP network, which are resilient backpropagation, Bayesian regularization and Levenberg-Marquardt. …”
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Monograph -
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An improved multilayer perceptron based on wavelet approach for physical time series prediction
Published 2014“…W-MLP, a network model with a wavelet technique added in the network, is trained using the standard backpropagation gradient descent algorithm and tested with historical temperature, evaporation, humidity and wind direction data of Batu Pahat for 5-years-period (2005-2009) and earthquake data of North California for 4-years-period (1995-1998). …”
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Thesis -
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Water level predictio for Limbang basin using multilayer perceptron (mlp) and radial basis function (rbf) neural network
Published 2010“…MLP is trained with conjugate gradient algorithms, trainscg and RBF with newrb. …”
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Final Year Project Report / IMRAD -
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Ensemble Separable Recurs ive Techniques for MLP Networks
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Article -
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RSA Encryption & Decryption using JAVA
Published 2006“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Final Year Project -
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Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification
Published 2017“…Meanwhile, the improved GA-MLP classification performance has been evaluated using datasets that vary in input features and output sizes. …”
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Thesis -
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An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2013“…To overcome this, a Functional Link Neural Networks (FLNN), which has single layer of trainable connection weight is used. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. …”
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