Search Results - (( code classification matching algorithm ) OR ( evolution optimization matching algorithm ))
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…In the classic Bag of visual words model, the Fuzzy c-means algorithm is replaced with K-means and the accuracy of SIFT matching is increased. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…For next, instead of k-means clustring, Fuzzy cmeans clustering is combined with Spatial Pyramid Matching image representation to improve the accuracy of classification results. …”
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A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation
Published 2019“…First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. …”
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A filtering algorithm for efficient retrieving of DNA sequence
Published 2009“…The biologists frequently demand for optimal comparison result so that the perfect structure of living beings evolution can be constructed. …”
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Automated Student Timetable Scheduling System based on Genetic Algorithm
Published 2019“…Genetic algorithm works based on natural evolutions that comprises of several iterations. …”
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Analysing ratio-based controllers against motivational game theory in peer-to-peer renewable energy trading
Published 2024“…Yet, the transition from generation to utilization presents challenges, particularly in terms of tracking and matching energy supply with consumer demand. This inefficiency highlights the need for a sophisticated controller equipped with optimal algorithms. …”
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Proceeding Paper -
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Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali
Published 2016“…The subsequent step is using the DAUB3 wavelet transform for feature extraction along with the application of an additional step for biometric template security that is the Non-invertible transform (cancelable biometrics method) and finally utilizing the Support Vector Machine (Non-linear Quadratic kernel) for matching/classification. The experimental results showed that the recognition rate achieved are of 99.9% on Bath-A data set, with a maximum decision criterion of 0.97.…”
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