Search Results - (( parameters variation method algorithm ) OR ( parameter estimation machine algorithm ))

Restoration of blurred images using geometric and chebichef moments / Ahlad Kumar by Ahlad, Kumar

“…Once the blur parameters are estimated, image restoration of the proposed method is carried out using split Bregman algorithm. …”
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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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A new method for decreasing cell-load variation in dynamic cellular manufacturing systems by Delgoshaei, Aidin, Mohd Ariffin, Mohd Khairol, Baharudin, Btht Hang Tuah, Leman, Zulkiflle

“…The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty by Delgoshaei, Aidin, Ali, Ahad, Ariffin, Mohd Khairol Anuar, Gomes, Chandima

“…In continue a Taguchi method (an orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. …”
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Indirect Rotor Field Oriented Control of Induction Motor With Rotor Time Constant Estimation by Moh'd Radwan, Eyad Moh'd

“…Since the position of the rotor flux vector is estimated in an IRFOC scheme, and is dependent on the motor model (more specifically the rotor parameters), these parameters must be obtained accurately and match the motor parameters at all times. …”
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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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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.

Three-dimensional craniometrics identification model and cephalic index classification of Malaysian sub-adults: A multi-slice computed tomography study / Sharifah Nabilah Syed Mohd... by Sharifah Nabilah , Syed Mohd Hamdan

“…MIMICS software version 21.0 (Materialise, Leuven, Belgium) was used to construct 3D models and plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA), binary logistic regression (BLR), and several machine learning (ML) algorithms (random forest (RF), support vector machines (SVM), and linear discriminant analysis (LDA)) were used to statistically analyse the data. …”
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Estimation of optimal machining control parameters using artificial bee colony by Norfadzlan, Yusup, Arezoo, Sarkheyli, Azlan, Mohd Zain, Siti Zaiton, Mohd Hashim, Norafida, Ithnin

“…This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. …”
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Geometrical and dimensional defect evaluation of cold forged AA6061 propeller blade by Abdullah, Ahmad Baharuddin

“…In addition, the defect was predicted by investigating the design parameters and its effect on the material flow pattern. …”
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Forecasting of meteorological drought using ensemble and machine learning models by Pande C.B., Sidek L.M., Varade A.M., Elkhrachy I., Radwan N., Tolche A.D., Elbeltagi A.

“…The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. …”

Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram by Faris Francis Singaram, Fareena

“…The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. …”
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Processing time estimation in precision machining industry using AI / Lim Say Li by Lim, Say Li

“…An AI approach for processing time estimation by implementing desired input parameters and machining data is tested and completed. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data by Joshua, Ibidoja Olayemi

“…After the heterogeneity parameters were excluded from the model, the support vector machine with the MM estimator showed that better significant results were obtained with 2.09% outliers. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

“…Genetic Algorithm (GA) is used to search for the best parameter of SVM classification by using combination of random and pre-populated genomes from Pre-Populated Database (PPD). …”

Predictive modelling of machining parameters of S45C mild steel by Abbas, Adnan Jameel

“…This research adopts the utilization of three types of heurestic algorithms to achieve the minimization operation; Genetic Algorithm (GA). …”
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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

“…Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. …”
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Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat by Chia , Yu Huat

“…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. In addition, Coati Optimization algorithm, Particle Swarm Opimisation (PSO) and Bayesian Optimsiation (BO) are integrated to identify optimal parameters and minimize settlement during twin tunnel excavation and GBT with the optimisation algorithm has shown consistent capability identifying the least SS induced by twin tunnels Keyword: …”
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