Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Abdulazeez, Ismail; Abba, Sani I.; Usman, Jamilu; Usman, Abdullahi G.; Aljundi, Isam H.

doi:10.1021/acsanm.3c04421

Cited by 10 publications

(2 citation statements)

References 69 publications

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“…In other to judge the accurateness and effectiveness of the model, four statistical measures were (performance evaluation criteria) employed, which comprise of Linear Correlation Coefficient (R), Coefficient of Determination (R 2 ), Mean Square error (MSE), as well as Root Mean Square Error (RMSE) (Abdulazeez et al, 2023;Abdullahi et al, 2021;Ghali et al, 2020;Pham et al, 2019aPham et al, , 2019bPham et al, , 2019bSaood et al, 2022;Usman et al, 2021).…”

Section: Evaluating Criteria Of the Modelsmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10

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Section: Evaluating Criteria Of the Modelsmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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“…Uzun Ozsahin et al, 2022;Yassin et al, 2022). In the concept of data mining in pharmaceutical sector, most of the analysis involves the second type of data mining; even though, the detection of various signals occurs towards a known background dataset, hence, discussion regarding the first type is equally necessary (Abba, Pham, et al, 2020a;Abdulazeez et al, 2023;Ahmad et al, 2021;Farooqui & Mehra, 2018;Jibril et al, 2024;Jibril, Malami, et al, 2023;Mahmoud et al, 2021). Modern data mining employs the use of statistics with other tools, ideas and approaches from computer science, database technology, machine learning, deep learning as well as other classical analytical tools.…”

Section: Introductionmentioning

confidence: 99%

Chemometrics Feasibility for Modelling Chromatographic Behavior of Diazepam using Linear and Non-Linear Techniques: A Data Mining Based Approach

A. D.,

M. A.,

M. Z.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Modern data mining employs the use of statistics with other tools, ideas and approaches from computer science, database technology, machine learning, deep learning as well as other classical analytical tools. In this research, three distinct algorithms have been employed for modelling the performance properties of Diazepam (DIA) utilizing the method of high-performance liquid chromatography (HPLC). The algorithms developed include support vector machine (SVM), adaptive-neuro fuzzy inference systems (ANFIS), and linear regression (LR). Temperature and mobile phase inform of methanol (MeOH) were used as predictors, while the time recorded for the retention was deemed to be the dependent variable. The performance accuracy of the models was assessed using two statistical metrics, including determination co-efficient (R2) and root mean square error (RMSE). The obtained results were shown both qualitatively and graphically using different charts. The comparative performance accuracy of the models demonstrates that the non-linear models (ANFIS and SVM) displays a higher performance efficiency than LR and has the ability of enhancing its performance ability by 51.2% and 76.1% both throughout the training and testing phase.

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Optimizing sustainable desalination plants with advanced ML-based uncertainty analysis

Abba,

Usman,

Bafaqeer

et al. 2024

Applied Soft Computing

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Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Cited by 10 publications

References 69 publications

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Chemometrics Feasibility for Modelling Chromatographic Behavior of Diazepam using Linear and Non-Linear Techniques: A Data Mining Based Approach

Optimizing sustainable desalination plants with advanced ML-based uncertainty analysis

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