Modeling of semiconductors refractive indices using hybrid chemometric model

Oloore, Luqman E.; Owolabi, Taoreed O.; Fayose, Sola; Adegoke, Muideen; Akande, Kabiru O.; Olatunji, Sunday O.

doi:10.18280/mmc_a.910301

Cited by 7 publications

(5 citation statements)

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“…In this approach, the solution search space is explored and exploited by the agents, which are known as objects in Newtonian description. The interaction of these agents with one another is controlled by the value of their masses as well as gravitational pull [27,47,48]. An agent with heavy mass corresponds to a good or global solution and moves very slowly in the solution space, whereas agents of lighter masses are attracted towards heavy agents.…”

Section: Physical Principles Of the Gravitational Search Algorithmmentioning

confidence: 99%

“…It acquires support vectors linking the descriptors with the desired target at the training phase while the acquired vectors are further validated. The algorithm maintains a high level of precision and accuracy while dealing with many real-life as well as complex problems [27][28][29][30]. The robustness and effectiveness of an SVR-based model have been attributed to its non-convergence to local minima, sound mathematical background and proper tuning of its user-defined hyperparameters [31,32].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.

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Section: Physical Principles Of the Gravitational Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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“…Support vector regression (SVR) is a machine learning algorithm that is capable of relating descriptors to the desired target through pattern acquisition and generation of support vectors [ 32 , 33 ]. It has been extensively applied in many real-life applications due to its unique features, such as strong mathematical background, non-convergence to a local minimum, and excellent predictive strength in the presence of few data-points and descriptive features [ 34 , 35 , 36 , 37 , 38 , 39 ]. The user-defined parameters in SVR algorithm, such as regularization factor, epsilon, hyper-parameter lambda, kernel option, and kernel function play a significant role when it comes to model performance [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

et al. 2021

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This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.

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“…The initial definition of error threshold called “epsilon” and the possible inclusion of non-zero slack variables enable the algorithm to address non-linear regression problems with close proximity between the measured and estimated target. As such, real-life applications of the SVR algorithm cuts across many fields of study [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. The algorithm parameters such as the defined error threshold epsilon, penalty factor and mapping function parameter are very germane to the successful acquisition of patterns connecting the desired model target with the descriptors.…”

Section: Introductionmentioning

confidence: 99%

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

et al. 2021

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Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

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Modeling of semiconductors refractive indices using hybrid chemometric model

Cited by 7 publications

References 0 publications

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

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