Learning to predict test effectiveness

Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work.

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“…( 1 – 4 ). 23 , 24 where N is the number of recorded samples, y i is the predicted pH value, and is the actual pH value.…”

Section: Materials and Apparatusmentioning

confidence: 99%

Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and supported mobile devices

Elsenety

Mohamed

Sultan

et al. 2022

Sci Rep

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“…Practically, as the number of statistical features increases, the system's accuracy improves [20]. The accuracy of the proposed model was improved when using statistical (minimum, maximum, sum, mean, and standard deviation) values of the metrics.…”

Section: Introductionmentioning

confidence: 99%

“…The authors have already introduced a model to predict test effectiveness in terms of a new metric called Coverageability [20]. Coverageability indicates the extent to which a given source code may be covered with test data generated automatically.…”

Section: Introductionmentioning

confidence: 99%

An ensemble meta-estimator to predict source code testability

Zakeri‐Nasrabadi

Parsa

2022

Applied Soft Computing

Self Cite

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“…Figure S14 and Table S4, Supporting Information, show the corresponding regression evaluation metrics concerning mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), which can be calculated by Scikit‐class metrics according to Equation (). [ 59,60 ]

R^{2} = 1 - \frac{\sum_{i} {(y_{i} - {\hat{y}}_{i})}^{2}}{\sum_{i} {(y_{i} - \bar{y})}^{2}}

MSE = \frac{1}{N} \sum_{i = 1}^{N} {(y_{i} - {\hat{y}}_{i})}^{2}

MAE = \frac{1}{N} \sum_{i = 1}^{N} | y_{i} - {\hat{y}}_{i} |

RMSE = \sqrt{\frac{\sum_{i = 1}^{N} {(y_{i} - {\hat{y}}_{i})}^{2}}{N}}

where N is the number of recorded samples,

y_{i}

predicted PCE values, and

{\overset{3extrue^}{y}}_{i}

actual PCE values.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

2023

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To prevent the degradation of perovskite solar cells (PSCs) and optimize the solar energy conversion process, a donor–π–acceptor (D–π–A) organic blue dye as a passivation layer and as a hole‐transporting layer is introduced. The terminal chains of D–π–A dye confer the ultrahydrophobic character (contact angle > 100°) of the interface layer, protecting the perovskite from ambient moisture while mitigating ionic diffusion in the device. The dye interlayer primarily improves the perovskite by reducing grain boundary defects. The perovskite/D–π–A architecture enhances the interfacial hole extraction, suppressing nonradiative carrier recombination and enabling power conversion efficiency (PCE) reaching 20.90%, outperforming by 2.05% the PCE of control cells. Unsealed PSCs retain 84% and 62% of their efficiency after photovoltaic operation for 1000 and 3000 h, respectively. Statistical correlation of bivariant and multivariant analyses of photovoltaic parameters is performed and Pearson's correlation identifies underlying patterns in experimental data collections. Machine learning (ML) of regression algorithms is used to predict the minimum errors and the coefficient of determination, which confirm the analysis quality. The linear regression ML model suggests the importance of photovoltaic parameters (Rs > Vmpp > Jsc > Voc > fill factor > Jmpp > Rsh) toward higher PCE. An efficient online prediction model is also developed to support the estimation of PCEs with high accuracy.

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Learning to predict test effectiveness

Cited by 10 publications

References 41 publications

Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and supported mobile devices

Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and supported mobile devices

An ensemble meta-estimator to predict source code testability

Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

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