Search Results - (( parallel optimization means algorithm ) OR ( variable extraction function 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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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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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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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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Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement
Published 2016“…First, unified ADALINEs based fundamental component extraction algorithm is proposed for dual functionality. …”
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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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Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphso...
Published 2022“…The proposed guaranteed convergence arithmetic optimization algorithm based on efficient modified third order Newton Raphson (GCAOAEmNR) method highlights important contributions to the literature in terms of methodology (explorer-exploiter phases) and objective function design. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living
Published 2024“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
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Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA
Published 2025“…To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Investigation of QAM based on mean square error (MSE) channel estimation (CE) for MIMO-OFDM pilot based system / Mohd Ariff Ibrahim
Published 2013“…Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) are the main optimizer as it convert input signal or output signal from frequency domain to time domain or vice versa either on serial or parallel block. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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Communication and computation rate-cheating problems in divisible load scheduling: revisited
Published 2015“…In the real applications, the processors may cheat the algorithm which means that the processors might not report their true computation or communication rates. …”
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Mitigation of atmospheric turbulences using mode division multiplexing based on decision feedback equalizer for free space optics
Published 2017“…This paper investigates the mitigation of atmospheric turbulences of FSO using MDM and decision feedback equalizer (DFE) with minimum mean square error (MMSE) algorithm. The implementation of the MMSE algorithm is used to optimize both the feedforward and the feedback filter coefficients of DFE. …”
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A novel theoretical and practical methodology for extracting the parameters of the single and double diode Photovoltaic models
Published 2022“…Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOA ENR ) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Therefore, the core classifier in the hyper-heuristic approach of Intrusion Detection System (IDS) is developed to the parallel structure NN. This enables more controllability of reaching optimal learning without falling into sub-optimality because of over-fitting or under-fitting. …”
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