Search Results - (( code classification matching algorithm ) OR ( parallel optimization mining algorithm ))
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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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Optimizing OLAP heterogeneous computing based on Rabin-Karp algorithm
Published 2013“…In this paper, through experimental results and based on Rabin-Karp Algorithm; we propose an optimized heterogeneous solution that takes into account the benefits and the boundaries in order to achieve a better OLAP performance in terms of response time with three times gain. …”
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Proceeding Paper -
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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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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour
Published 2022“…On that basis, the second contribution of this thesis is proposing a fast fuzzy evolutionary algorithm with minimal stochastic behaviour. Furthermore, this thesis contributes to the body of knowledge by introducing a novel method called Fuzzy Rough set Genetic Algorithm (FRGA) that take on efficiency of metaheuristics while reducing search space by leveraging the data mining aspect of rough set theory. …”
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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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