Search Results - (( data optimization method algorithm ) OR ( parameters variation from algorithm ))

Identification of continuous-time model of hammerstein system using modified multi-verse optimizer by Most. Julakha, Jahan Jui

“…The statistical analysis value (mean) was taken from the parameter deviation index to see how much our proposed algorithm has improved. …”
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

“…Several researchers have reported different optimization methods for blade parameters such as Blade Element Momentum theory (BEM), Computational Fluid Dynamics (CFD) and Supervisory Control and Data Acquisition (SCADA) system. …”
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques by Hammid, Ali Thaeer

“…To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…The cumbersome numerical computation and rudimentary empirical solutions hinder faster analysis over a wide range of parameters. However, machine and deep learning methods have higher accuracy but rely heavily on the quality and amount of training data, and the solution may become inconclusive if data is sparse. …”
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Hybrid OCSSA-VMD and optimized deep learning networks for runoff forecasting by Ma, Hong, Shareduwan Mohd Kasihmuddin, Mohd, Mansor, Mohd. Asyraf, Mohd Jamaludin, Siti Zulaikha, Marsani, Muhammad Fadhil, Che Rose, Farid Zamani

“…The Osprey-Cauchy-Sparrow Search Algorithm (OCSSA) is employed to fine-tune the parameters of Variational Mode Decomposition (VMD), which is utilized to break down the original runoff data into multiple Intrinsic Mode Functions (IMFs). …”
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Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending by Abu Khadra, Fayiz Y. M.

“…The training data required to train the two-metamodeling techniques were generated using a verified nonlinear finite element algorithm developed in the current research. …”
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Analysis of inverted planar perovskite solar cells with graphene oxide as HTL using L9 OA Taguchi method by Salehuddin, Fauziyah, Kaharudin, Khairil Ezwan, Md Junos@Yunus, Siti Aisah, Ahmad Jalaludin, Nabilah, Mohd Zain, Anis Suhaila, Johari, Nur Aliesa, Arith, Faiz, Ahmad, Ibrahim

“…By using this method, the data from the numerical modelling Solar Cell Capacitance Simulator-One Dimensional (SCAPS-1D) was optimized. …”
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Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method by Delgoshaei, Aidin

“…Then, design of experiments is used to examine the sensitivity of the parameters of each solving algorithm using Taguchi method. …”
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Multivariate Based Analysis of Methane Adsorption Correlated to Toc and Mineralogy Impact from Different Shale Fabrics by Irfan, S.A., Azli, N.M., Abdulkareem, F.A., Padmanabhan, E.

“…The analysis from machine learning SVR method shows the good predictability of the adsorption in the variation with shale fabric parameters. …”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…The nonlinear effects resulting from the multiple variations of the uncertainty parameters, which are difficult to adjust or control in the experimental analysis, lead to a significant deviation between the experimental data and the predictions. …”
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Analysis of Inverted Planar Perovskite Solar Cells with Graphene Oxide as HTL using L9 OA Taguchi Method by Johari N.A., Jalaludin N.A., Salehuddin F., Arith F., Kaharudin K.E., Zain A.S.M., Junos S.A.M., Ahmad I.

“…By using this method, the data from the numerical modelling Solar Cell Capacitance Simulator-One Dimensional (SCAPS-1D) was optimized. …”

Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. Two parameters (population size and generation numbers) are adaptively adopted from number of remaining ranking features. …”
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Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller by Zaridah, Mat Zain

“…The optimization technique involved from two points to four points and end with six points. …”
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Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms by Lai V., Ahmed A.N., Malek M.A., Afan H.A., Ibrahim R.K., El-Shafie A., El-Shafie A.

Image Splicing Detection With Constrained Convolutional Neural Network by Lee, Yang Yang

“…With the datasets prepared and assembled, the proposed CNN model will have a series of experiments to test for the various parameters as well as to investigate other non-parametric factors such as the data variation itself. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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Parametric analysis of critical buckling in composite laminate structures under mechanical and thermal loads: a finite element and machine learning approach by Shabbir Ahmed, Omar, Syed Mohamed Ali, Jaffar, Aabid, Abdul, Hrairi, Meftah, Mohd Yatim, Norfazrina Hayati

“…Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. …”
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Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

“…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). …”
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