Search Results - (( code classification issues algorithm ) OR ( parallel optimization means algorithm ))
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Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function
Published 2012“…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
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Crow Search Freeman Chain Code (CS-FCC) feature extraction algorithm for handwritten character recognition
Published 2023“…With so many algorithms developed to improve classification accuracy, interest in feature extraction in Handwritten Character Recognition (HCR) has increased. …”
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Conference or Workshop Item -
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Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line
Published 2010“…As the total objective values in most of problems could not be improved by simulated algorithm, it proved the well performing of proposed intelligence based genetic algorithm in reaching the near optimal solutions.…”
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Hybrid flow shop scheduling with energy consumption in machine shop using moth flame optimization
Published 2022“…Based on the optimization results, the MFO outperformed other comparison algorithms for the mean fitness and also the best fitness. …”
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Automatic multilevel medical image annotation and retrieval
Published 2008“…To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. …”
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
Published 2023Article -
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An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title
Published 2019“…The enhanced method combines three transformation methods for analyzing the iris image and extracting its local features. It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). …”
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Phishing image spam classification research trends: Survey and open issues
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Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
Published 2022“…Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. …”
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Dengue outbreak prediction: hybrid meta-heuristic model
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Optimization of Workload Allocation Problem in a Network of Heterogeneous Computer Systems
Published 2005“…Other service distributional models such as exponential, Erlang-k and Gamma have also been used to expand the work applicability. A new algorithm of workload allocation scheme using First Come First Serve discipline in conjunction with optimization of GE queueing systems is proposed for minimizing mean queue length and mean response time in a network of computer systems. …”
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Thesis -
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A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks
Published 2019“…The multilayered version of CSC(ML-CSC) is shown to be connected to forward pass of CNNs and dictionary learning and sparse coding algorithms of this model are analyzed for solving classification and inverse problems in image processing. …”
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Proceeding Paper -
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Improving hand written digit recognition using hybrid feature selection algorithm
Published 2022“…While mRMR was capable of identifying a subset of features that were highly relevant to the targeted classification variable, it still carry the weakness of capturing redundant features along with the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Deep learning based emotion recognition for image and video signals: matlab implementation
Published 2021“…This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. …”
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