Search Results - (( data selection method algorithm ) OR ( variable training based algorithm ))

Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…The efficacy of these models depends upon many factors such as, neural network architecture, type of training algorithm, input training and testing data set and initial values of synaptic weights. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration by Momeni, M., Hosseini, S.J., Ridha, S., Laruccia, M.B., Liu, X.

“…This paper discusses bit selection by employing a method of combining Artificial Neural Network (ANN) and the computation of Genetic Algorithm (GA). …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…The results indicated that good classification performance depends on these factors. All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…A total of 24 sites which were eligible in terms of adequate rainfall and runoff observed data were selected in this region. This area contains 604 pairs of observed data which was grouped into 60%, 20% and 20% for training, validation and testing, respectively. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…Effect of spray drying parameter on the powder quality is further analyzed using response surface methodology (RSM) method. The ANN model topology is designed using selection from the best training algorithm, transfer function, number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15). …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

“…The optimal combination of λ and training sequence can be computed efficiently using a genetic permutation algorithm. The best combinations were selected to train individual ARTMAPs as voting members, and the final class predictions were determined using probabilistic ensemble voting strategy. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models by Hilal, Yousif Yakoub

“…Finally, this research concluded that a genetic algorithm is useful for selecting input variables in oil palm production. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…However, despite the PM model being accepted as a universal method for determining the ET0, this method is often criticised due to the high number of meteorological variables needed. …”
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Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor by Mamat, Nor Hana, Mohd Noor, Samsul Bahari, Che Soh, Azura, Taip, Farah Saleena, Ab Rashid, Ahmad Hazri, Jufika Ahmad, Nur Liyana, Mohd Yusuff, Ishak

“…Thus it is beneficial to model the relationship of DO concentration with these variables based on real process data for further use in controller design. …”
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Precipitation forecasting using multilayer neuralNetwork and support vector machine optimization based on flow regime algorithm taking intoAccount uncertainties of soft computing m... by Banadkooki F.B., Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., Ridwam W.M., Sefelnasr A., El-Shafie A.

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri by Basri, Katrul Nadia

“…Dimension reduction algorithm such as LDA and CNN were applied on the spectra to reduce the number of variables to be trained. …”
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What, how and when to use knowledge in neural network application by Wan Ishak, Wan Hussain, Abdul Rahman, Shuzlina

“…The methodology comprises five steps namely variable selection, data collection, data preprocessing, training &validation, and testing.…”
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Recommendation System Model For Decision Making in the E-Commerce Application by Mohammed Mahmudur Rahman

Model Prediction Of Pm2.5 And Pm10 Using Machine Learning Approach by Hamid, Norfarhanah

“…Based on the feature selection, model development was built with and without input selection using the Nonlinear Autoregressive with Exogeneous Input (NARX) neural network model which made use of 10 number of hidden neurons and 2 number of delays, implementing Levenberg-Marquardt as training algorithm. …”
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Neural network based adaptive pid controller for shell-and-tube heat exchanger by Othman, Mohamad Hakimi

“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. Single hidden layer feed forward neural networks with 20 neurons in hidden layer was selected. …”
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