Search Results - (( data normalization means algorithm ) OR ( data optimization method 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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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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Logic Programming In Radial Basis Function Neural Networks
Published 2013“…I used different types of optimization algorithms to improve the performance of the neural 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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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…The direct idea of making the conventional neural network learning algorithm more powerful towards outlying data is by replacing the mean square error (MSE) with a different symmetric and continuous cost function. …”
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Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems
Published 2009“…The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. …”
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Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Published 2017“…The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.…”
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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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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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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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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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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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Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
Published 2022“…The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. …”
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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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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques
Published 2007“…The ANFIS models were built using the best data subset resulting from ANN modeling. The models were trained with normalized and non-normalized data. …”
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