Quinquennial Review of Progress in Degradation Studies and Impurity Profiling: An Instrumental Perspective Statistics

Shelke, Madhav; Deshpande, Shirish; Sharma, Sanjay

doi:10.1080/10408347.2019.1615863

Cited by 19 publications

(13 citation statements)

References 196 publications

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“…ANFIS can be defined as a multi-layer feedforward neural network with learning algorithms and FIS through the inputs to the output [16]. Jang had introduced ANFIS to apply the main stage to build suitable FIS.…”

Section: A Hybrid Mlr-nf Methodsmentioning

confidence: 99%

Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets

Shukur¹

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The forecasting of time series data is essential by following statistical and intelligent techniques. Particular matter (PM10) is a time series dataset used to scale the air pollution as a dependent variable while there are many types of pollutants used as independent variables. MLR model has been used as a traditional linear approach to forecasting PM 10 data. Combining NF as a nonlinear intelligent method with MLR in a hybrid MLR-NF method has been proposed for improving PM 10 forecasts and handling the nonlinearity of datasets. The forecasting results reflected that the hybrid method outperformed the traditional method. Although a multiple linear regression (MLR) model has been used for air quality forecasting depending on several meteorological variables in many recent studies, the MLR model is unable to identify the nonlinear pattern of these types of data. Malaysian datasets of PM10 and several climate pollutants will be studied in this paper. The objective of this study is to forecast PM10 and obtain the best results and minimum forecasting error. In this paper, the dependent variable will be forecasted by using traditional and intelligent methods. MLR has been used as a traditional method to forecast PM10. Neuro-fuzzy (NF) in the adapted copy, which calls the adaptive neurofuzzy inference system (ANFIS) is combined with MLR and used as an intelligent method to forecast PM10. The results reflect that MLR-NF outperformed MLR for forecasting PM10 data. In conclusion, MLR-NF can be used to forecast PM10 for more accurate results compared to traditional methods.

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Section: A Hybrid Mlr-nf Methodsmentioning

confidence: 99%

Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets

Shukur¹

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…GIs were quantified using the SIM acquisition mode at the 75 ppm level with respect to vortioxetine and the method was developed and validated according to the requirements of regulatory agencies 140 . Shelke and coworkers recently reviewed the trends in analytical methods used for degradant, foreign matter, genotoxic impurity and impurity profiling studies during the years 2013-2017 141 . Recently, Guo and coworkers studied the degradation behavior of sutezolid (oxazolidinone anti-TB compound used for the treatment of M. Tuberculosis) under different conditions, identifying 4 impurities by LC-ESI(+)-QTOF-MS/MS.…”

Section: Analysis and Identification Of Impurities And Degradation Prmentioning

confidence: 99%

Current developments in LC-MS for pharmaceutical analysis

Beccaria

Cabooter

2020

Analyst

173

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“…There are two types of (WT), Continuous Wavelet Transformation (CWT), and Discrete Wavelet Transformation (DWT). In this paper, we used the (DWT), [13], [14].…”

Section: A Wavelet Transformationmentioning

confidence: 99%

Wavelet Estimation of Semi-parametric Regression Model

Tahir¹,

Jassim²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The semi-parametric regression model combines parametric and nonparametric regression. However, non-parametric estimation may provide flexible solutions to the problems suffers by the regression model, but the problem of dimensionality that this estimator suffers, which occurs due to the increasing number of explanatory variables, still remain, this, in turn, may reduce the accuracy of the estimation process. Estimate the non-parametric part of the semi-parametric models that can be studied using conventional non-parametric methods such as the Spline Smoothing and Kernel Smoothing. However, there are other non-parametric methods that can be used, therefore, in this paper, the semi-parametric regression model was estimated by employing the wavelet estimate for the soft threshold, according to the "Speckman" method, and then comparing it with the two methods, Nadaraya-Watson and Local Linear, through the implementation of simulation experiments that included different sample sizes and threshold values. The parametric part estimation of the partially linear model according to the least-squares method was not identical to those estimates using the Speckman method, that is because the least-squares method was not appropriate for the uneven nature of the number of weekly work hours. Simulation experiments have demonstrated the efficiency of the wavelet estimation method and its superiority over other methods. The above estimation methods were applied to real data related to the study of the production value for the public industrial sector in Iraq, and some factors affect it, such as the value of industrial supplies, the total wages of workers, and the number of workers.

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Quinquennial Review of Progress in Degradation Studies and Impurity Profiling: An Instrumental Perspective Statistics

Cited by 19 publications

References 196 publications

Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets

Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets

Current developments in LC-MS for pharmaceutical analysis

Wavelet Estimation of Semi-parametric Regression Model

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