Search Results - (( data distribution methods algorithm ) OR ( parameter optimization approach algorithm ))
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Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…In actual APP problems, input data or parameter values, including resource, demand, cost, and objective functions, may be inaccurate. …”
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2
Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…In this study, a software defect prediction framework that combines metaheuristic optimization methods for feature selection and parameter optimization, with meta learning methods for solving imbalanced class problem on datasets, which aims to improve the accuracy of classification models has been proposed. …”
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Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching
Published 2016“…A Monte Carlo simulation technique is examined and employed to overcome the computational issues arising from the intractability of the probability mass function of some mixed Poisson distributions. For parameter estimation, the simulated annealing global optimization routine and an EM-algorithm type approach for maximum likelihood estimation are studied. …”
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4
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…In this task, two neural network algorithms, Recurrent Neural Networks (RNN) and Multi-Layer Perceptron Neural Networks (MLP-NN) were used and the hyper-parameters of the network architecture was optimized based on a systematic grid search. …”
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5
Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
Published 2024“…The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. …”
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Optimizing LSTM S2S models with Evolutionary Mating Algorithm (EMA) for direct multi-step forecasting of household electrical power consumption
Published 2026“…EMA successfully identifies optimal parameters, and EMA-optimized model achieved a significant improvement in accuracy over to baseline methods. …”
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Conference or Workshop Item -
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A predictive approach to improve a fault tolerance confidence level on grid resources scheduling
Published 2008“…Therefore, finding a stable and fault tolerance resource require designing a predictive method that doing this work. Many methods are presented in a few years ago, but in these algorithms, some parameters such as job requirements and clear predictor method are not truly considered and also some methods apply optimistic view in grid scheduling cycle. …”
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OFF-GRID SOLAR PHOTOVOLTAIC (PV) DESIGN BASED ON FUZZY TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) APPROACH
Published 2022“…The aforementioned technique is the MCDM method, particularly the fuzzy TOPSIS algorithm. This algorithm is embedded in the PV system to select the best PV components’ configuration based on multiple criteria. …”
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Final Year Project Report / IMRAD -
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Optimal overcurrent relay coordination in wind farm using genetic algorithm
Published 2015“…Many suggestions have been made and approaches implemented, however one of the most prominent methods is the use of Genetic Algorithm (GA) to improve the function and coordination of OCRs. …”
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11
Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…To obtain the unknown vector of parameters of the MHTan, three heuristic optimization algorithms are employed to minimize the sum of squared residuals. …”
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12
Analysis and decentralised optimal flow control of heterogeneous computer communication network models
Published 1993“…A new method of general model reduction using the Norton' s theorem for general queueing networks in conjunction with the universal maximum entropy algorithm is proposed for the analysis of xix large general closed queueing networks. …”
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13
Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit
Published 2024“…After conducting stepwise experiments and comparing with three benchmark methods - a baseline FL method and two PFL approaches in similar optimization scheme - the results shows that POLA not only systematically optimizes its internal outputs but also outperforms the comparison methods. …”
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Bio-inspired snake robot locomotion: a CPG-based control approach
Published 2015“…The performances of the proposed method are verified by simulation results.…”
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Proceeding Paper -
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A novel fuzzy – backward/forward sweep power flow for uncertainty management in radial distribution network with photovoltaic generation
Published 2025“…This research presented a novel framework of fuzzy backward/forward sweep (F-BFS) power flow to address uncertainties in radial distribution networks with photovoltaic generation. The F-BFS framework integrated fuzzified values to model uncertainty parameters in radial distribution network power flow analysis, whereas the Grey Wolf Optimizer (GWO) was employed to optimize PVDG placement and sizing, aiming to minimize power losses and improve voltage deviations. …”
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Context enrichment framework for sentiment analysis in handling word ambiguity resolution
Published 2024“…For classification performances, optimization of machine learning parameters and exploration of deep learning approaches can be applied for further enhancement.…”
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18
Heterogenous adaptive ant colony optimization with 3-opt local search for the travelling salesman problem
Published 2020“…The majority of optimization algorithms require proper parameter tuning to achieve the best performance. …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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20
Application of conjugate gradient approach for nonlinear optimal control problem with model-reality differences
Published 2018“…In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. …”
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