Search Results - (( using optimization method algorithm ) OR ( data normalization means algorithm ))
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Integrating genetic algorithms and fuzzy c-means for anomaly detection
Published 2005“…Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…In the next phase, the datasets are optimized using the Salp Swarm Algorithm (SSA), which improves classification accuracy. …”
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Logic Programming In Radial Basis Function Neural Networks
Published 2013“…The analysis revealed that performance of particle swarm optimization algorithm and Prey predator algorithm are better to use in training the networks. …”
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Technical report: genetic algorithm for vehicle routing problem / Mohammad Izwan Jamaluddin and Muhamad Syahmie Adeeb Mohd Shukri
Published 2016“…We are using four step in methodology as determine of genetic algorithm characteristic, data input, the process by using operator selection and prediction. the results have been compares with two operator selection to determine the minimum routes in cities. …”
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Entropy in portfolio optimization / Yasaman Izadparast Shirazi
Published 2017“…More specifically, we use multi-objective models that are the mean-entropy-entropy (MEE). The purpose of this new model is to overcome the limitations as observed in a traditional model; that is, having performance close to Markowitz’s mean-variance (MV) model when data comes from a normal distribution, but exhibit better performance when data comes from a non-normal distribution. …”
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Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems
Published 2009“…In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. …”
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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…The backpropagation algorithm is one of the most famous algorithms to train neural network based on the mean square error (MSE) of ordinary least squares (OLS). …”
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Book Section -
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Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Published 2017“…The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. …”
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Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. …”
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Performance comparison of GA and PSO based ANN training on medical dataset / Muhammad Amirul Danish Jamal
Published 2025“…Data preprocessing was carried out using min-max normalization, and an ANN architecture featuring 20 hidden neurons was created and optimized with MATLAB. …”
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14
Speech processing for makhraj recognition: The design of adaptive filter for noise canceller
Published 2011“…This paper focuses on noise removal in makhraj recognition using Normalized Least Mean Square (NLMS) Algorithm based on Adaptive Filter to search for the optimal solution. …”
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A study on component-based technology for development of complex bioinformatics software
Published 2004“…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris
Published 2019“…To evaluate the accuracy of building energy model, hourly criteria for Normalized Mean Biased Error (NMBE) and Coefficient of Variance Root Mean Squared Error (CV(RMSE)) as proposed by the IPMVP are used. …”
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Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
Published 2022“…The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. …”
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Robust estimation methods for fixed effect panel data model having block-concentrated outliers
Published 2019“…Firstly, we proposed robust panel data transformation to be performed around the MM-estimate of location as an alternative to the non-robust centering by the mean. …”
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Improved normalization and standardization techniques for higher purity in K-means clustering
Published 2016“…Based on its simplicity, the K-means algorithm has been used in many fields. This paper proposes improved normalization and standardization techniques for higher purity in K-means clustering experimented with benchmark datasets from UCI machine learning repository and it was found that all the proposed techniques’ performance was much higher compared to the conventional K-means and the three classic transformations, and it is evidently shown by purity and Rand index accuracy results.…”
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