Search Results - (( parameter evaluation bat algorithm ) OR ( pattern classification problem algorithm ))
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A study on the parameter selection of bat algorithm in in optimizing parameters in camera auto calibration problem
Published 2022“…Each bat in the Bat Algorithm represents a potential solution to the issue, and each dimension in the Bat Algorithm's search space represents one of the basic parameters: skew, focal length, and magnification factor. …”
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A Preliminary Study on Camera Auto Calibration Problem Using Bat Algorithm
Published 2013“…Each bat in Bat Algorithm represents a candidate solution for the problem and each dimension in the search space of the Bat Algorithm represents a parameter of intrinsic parameters: skew, focal length, and magnification factor. …”
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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting
Published 2020“…Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. …”
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…The purpose of this research is to apply and evaluate the performance of Bat Algorithm for classification. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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Pattern Recognition for Human Diseases Classification in Spectral Analysis
Published 2022“…The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.…”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. …”
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Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…Pattern recognition/classification has received a considerable attention in engineering fields. …”
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Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization
Published 2022“…Experimental and statistical analyses show that, in comparison with the bat as well as variants of the bat and state-of-the-art algorithms, the proposed algorithm substantially outperforms them significantly, based on MSE and BER.…”
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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
Published 2010“…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm
Published 2018“…The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). …”
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Classification for large number of variables with two imbalanced groups
Published 2020“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Multilevel learning in Kohonen SOM network for classification problems
Published 2006“…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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Bat algorithm and neural network for monthly streamflow prediction
Published 2023“…Therefore, this study proposed on the development of streamflow prediction model AI techniques namely Bat algorithm (BA) and backpropagation neural network (BPNN). …”
Conference Paper -
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A derivative-free optimization method for solving classification problem
Published 2010“…For optimization generalized pattern search method has been applied. The results of numerical experiments allowed us to say the proposed algorithms are effective for solving classification problems at least for databases considered in this study.…”
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Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification
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Conference or Workshop Item -
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Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification
Published 2017“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. …”
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