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An efficient likelihood-based modulation classification algorithm for multiple-input multiple-output systems
Published 2016“…This can be performed by employing a Modulation Classification (MC) algorithm, which can be feature-based or likelihood-based. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…2024 - IOS Press. All rights reserved.…”
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An efficient control implementation for inverter based harmony search algorithm
Published 2023“…The mean absolute error (MAE) is used as an optimization problem to minimize the output voltage error for the developed controller (PI-HS) as compared to the PI controller based particale swarm optimization algorithm (PI-PSO). …”
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The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction
Published 2023“…This paper proposes an optimization algorithm based on the Binary Bat algorithm methodology for replacing the conventional orthogonal array approach. …”
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Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
Published 2020“…This paper modifies the classical fuzzy time series (FTS) algorithm by applying weighted subsethood based algorithm (WSBA) in FTS algorithm using segmented electricity load demand time series data. …”
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A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST
Published 2010“…The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. …”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…The mean closure performance of the BSCCH algorithm is compared against seven selected state-of-the-art algorithms, namely Differential Evolution with Adaptive Trial Vector Generation Strategy and Cluster-replacement-based Feasibility Rule (CACDE), Improved Teaching Learning Based Optimization (ITLBO), Modified Global Best Artificial Bee Colony (MGABC), Stochastic Ranking Differential Evolution (SRDE), Novel Differential Evolution (NDE), Partical Swarm Optimization for solving engineering problems-a new constraint handling mechanism (CVI-PSO) and Ensemble of Constraint Handling Techniques (ECHT). …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…All rights reserved.…”
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A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre
Published 2018“…The reliability of genetic algorithm may vary based on implementation case, hence it is necessary to investigate its performance pattern for each implementation case. …”
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A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre
Published 2018“…The reliability of genetic algorithm may vary based on implementation case, hence it is necessary to investigate its performance pattern for each implementation case. …”
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Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm
Published 2021“…© 2021 - IOS Press. All rights reserved.…”
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Taguchi's T-method with nearest integer-based binary bat algorithm for prediction
Published 2023“…In this paper, a swarm-based binary bat optimization algorithm with a nearest integer discretization approach is integrated with the Taguchi�s T-method. …”
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Preserving brightness in histogram equalization based contrast enhancement techniques
Published 2023Article -
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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. …”
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State of charge estimation in lithium-ion batteries: A neural network optimization approach
Published 2023“…The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. …”
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