Search Results - (( simulation optimization using algorithm ) OR ( using sparse method algorithm ))
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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Robust correlation feature selection based support vector machine approach for high dimensional datasets
Published 2025“…Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. …”
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Energy balancing mechanisms for decentralized routing protocols in wireless sensor networks
Published 2012“…Finally, we propose Self-Decision Route Selection scheme which is an improvement of the Hop-based Spanning Tree (HST) algorithm that is used in some routing protocols such as AODV and DSR. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Published 2018“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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Evaluation of sparsifying algorithms for speech signals
Published 2012“…Sparse representations of signals have been used in many areas of signal and image processing. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…Performance of proposed methods surpass the classic bag of words algorithm for plant identification tasks.…”
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Proximal linearized method for sparse equity portfolio optimization with minimum transaction cost
Published 2023“…The efficiency of the algorithm is demonstrated using real stock data and the model is promising in portfolio selection in terms of generating higher expected return while maintaining good level of sparsity, and thus minimizing transaction cost.…”
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Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field
Published 2018“…Theoretically,β and α is parameters that used to vary the NMF2D algorithm in order to yield high SDR value. …”
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Rain streaks removal using total variation and sparse coding based on case based reasoning approach
Published 2024journal::journal article -
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. …”
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Thesis -
15
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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Counting roots of the Polynomial systems by using mixed volume of the Newton polytopes / Nur Suhailah Norazhar, Nur Ain Sofiya Zainuri and Nor Suhada Mohd Rosdi
Published 2024“…These sparse matrices are then used to generate the determinant by using Maple 2015. …”
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Student Project -
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Prime-based method for interactive mining of frequent patterns
Published 2010“…Moreover, the results verify that the proposed method speeds up interactive mining of frequent patterns over both sparse and dense datasets with more scalable total runtime for very low values of minsup over sparse datasets as compared to results from the previous work.…”
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Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation
Published 2024“…Channel pruning and layer pruning are applied to the sparse model, and post-processing methods using knowledge distillation are used to effectively reduce the model size and forward inference time while maintaining model accuracy. …”
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Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems
Published 2022“…In this case, the effectiveness and reliability of RMS is increase by combining the simulation with the optimization algorithm.…”
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