Genetic programming based quantitative structure–retention relationships for the prediction of Kovats retention indices

Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta S.; Tambe, Sanjeev S.

doi:10.1016/j.chroma.2015.09.086

Cited by 15 publications

(12 citation statements)

References 73 publications

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“…However, these techniques were neither sufficient nor comprehensive enough to clearly show the interactions of parameters and crop yield and could not capture the highly nonlinear and complex relationships between OC and other traits (Khairunniza‐Bejo, Mustaffha, & Ismail, ; Singh, Kanchan, Verma, & Singh, ). These complex relationships need nonlinear methods such as artificial neural networks (ANN), genetic expression programming (GEP), adaptive neuro‐fuzzy inference system (ANFIS), or Bayesian classification (BC) to overcome the drawbacks of linear methods (Goel, Bapat, Vyas, Tambe, & Tambe, ; Iquebal et al, ; Khoshnevisan, Rafiee, & Mousazadeh, ; Samadianfard, Nazemi, & Ashraf Sadraddini, ; Silva et al, ; Zeng, Xu, Wu, & Huang, ). In the last few decades, ANN have been widely used to predict SY in different crops like soybean, corn (Kaul, Hill, & Walthall, ), rice (Ji, Sun, Yang, & Wan, ), wheat (Alvarez, ), barley (Gholipour, Rohani, & Torani, ), sunflower (Zeng et al, ), and sesame (Emamgholizadeh, Parsaeian, & Baradaran, ) as well as genomic selection (Yong‐Jun, Lei, Wang, & Chang‐Hong, ).…”

Section: Introductionmentioning

confidence: 99%

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Abdipour

Ramazani

Younessi-Hmazekhanlu

et al. 2018

J Americ Oil Chem Soc

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Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content (OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability leads to a low profit. The OCS modeling and indirect selection through high-heritable characters associated with OCS using advanced modeling techniques is a beneficial approach to overcome this limitation that allows breeder to get a better idea of the plant properties that should be monitored during breeding experiments. This study, carried out in 2013 and 2014, compared the potential of artificial neural network (ANN) and multilinear regression (MLR) to predict OCS in the Imamzadeh Jafar plain of Gachsaran, Iran. Principal component analysis (PCA) and stepwise regression (SWR) were used to evaluate 18 input variables. Based on PCA and SWR, the 6 traits of number of capsules per plant (NCP), number of days from flowering to maturity (NDFM), plant height (PH), thousand seed weight, capsule length, and seed yield were chosen as input variables. The network with the sigmoid axon transfer function and 2 hidden layers was selected as the final ANN model. Results showed that the ANN predicted the OCS with more accuracy and efficacy (R 2 = 0.861, root mean square error [RMSE] = 0.563, and mean absolute error [MAE] = 0.432) compared with the MLR model (R 2 = 0.672, RMSE = 0.742, and MAE = 0.552). These results showed the potential of the ANN as a promising tool to predict OCS with good performance. Based on sensitivity tests, NCP followed by NDFM and PH, respectively, were the most influential factors in predicting OCS in both models. It seems that a breeding program to select or create long sesame genotypes with a long period from flowering to maturity can be a good approach to address OCS in the future. Keywords Artificial neural networks Á Diallel cross Á Multiple regression Á Oil content Á Principal component analysis Á Sesame J Am Oil Chem Soc (2018) 95: 283-297.

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Section: Introductionmentioning

confidence: 99%

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Abdipour

Ramazani

Younessi-Hmazekhanlu

et al. 2018

J Americ Oil Chem Soc

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“…Steps (ii) to (iv) are performed iteratively (see the flowchart in Figure 2) until a best-fitting candidate solution (expression) is secured. An in-depth treatment of the GPSR procedure can be found in several studies [24][25][26][27] .…”

Section: Modelling Methodsmentioning

confidence: 99%

Bubble Size Prediction in Gas–Solid Fluidized Beds using Genetic Programming

Sonolikar¹,

Patil²,

Mankar³

et al. 2018

Current Science

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The hydrodynamics of a gas-solid fluidized bed (FB) is affected by the bubble diameter, which in turn strongly influences the performance of a fluidized bed reactor (FBR). Thus, determining the bubble diameter accurately is of crucial importance in the design and operation of an FBR. Various equations are available for calculating the bubble diameter in an FBR. It has been found in this study that these models show a large variation while predicting the experimentally measured bubble diameters. Accordingly, the present study proposes a new equation for computing the bubble diameter in a fluidized bed. This equation has been developed using an efficient, yet infrequently employed computational intelligence (CI)-based datadriven modelling method termed genetic programming (GP). The prediction and generalization performance of the GP-based equation has been compared with that of a number of currently available equations for computing the bubble diameter in a fluidized bed and the results obtained show a good performance by the newly developed equation.

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“…More details of the GP-based SR method and its implementation procedure can be found in, for example, Ghugare et al (2014), Goel et al (2015), Poli et al (2008), and Vyas et al (2015).…”

Section: Experimental Data and Need For Nonlinear Aft Modelsmentioning

confidence: 99%

Prediction of coal ash fusion temperatures using computational intelligence based models

Tambe

Naniwadekar

Tiwary

et al. 2018

Int J Coal Sci Technol

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In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs. Int J Coal Sci Technol (2018) 5(4):486-507 https://doi.org/10.1007/s40789-018-0213-6Prediction of coal ash fusion temperatures using computational intelligence based models 487

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Genetic programming based quantitative structure–retention relationships for the prediction of Kovats retention indices

Cited by 15 publications

References 73 publications

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Bubble Size Prediction in Gas–Solid Fluidized Beds using Genetic Programming

Prediction of coal ash fusion temperatures using computational intelligence based models

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