Development of chemometrics-based neurocomputing paradigm for simulation of manganese extraction using solid-phase tea waste

Ismail, Salihu; Abdulkadir, Rabiu Aliyu; Usman, A. G.; Abba, S.I.

doi:10.1007/s40808-022-01369-8

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…Other feasible alternatives models may also be used, but they were adopted here due to their outstanding performances in various literature in hydro-environmental studies. Some alternatives are Genetic programing, ARIMA models, machine learning models as presented in [21]- [28].…”

Section: Proposed Methodologymentioning

confidence: 99%

Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques

Okeke¹,

Aminu²,

Rotimi³

et al. 2022

GSC Adv. Eng. Technol.

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In order to provide an effective tool for the simulation of wastewater treatment plant performance and control, a reliable model is essential. In the present study, two different artificial intelligence (AI) models; Adaptive Neuro-Fuzzy inference system (ANFIS), and a classical multi-linear regression analysis (MLR) were applied for predicting the performance of Abuja wastewater treatment plant (AWWTP), in terms of Conductivity, pH, Iron content, BOD, COD, TSS and TDS. The daily data were obtained from Abuja Wastewater treatment plant, for this purpose, single and ensemble models were employed to compare and improve the prediction performance of the plant. The obtained result of single models proved that, MLR model provides an effective analysis in comparison to the other single model. The result showed that, conductivity influences the performance and efficiency of the water treatment plant by an increased efficiency performance of AI modelling up to 99.6% testing phase and 6.8% Error value of same phase. This shows that MLR model was more robust and reliable method for predicting the Abuja WWTP performance.

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Section: Proposed Methodologymentioning

confidence: 99%

Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques

Okeke¹,

Aminu²,

Rotimi³

et al. 2022

GSC Adv. Eng. Technol.

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show abstract

“…There are two phases to the data used in this study: training (70%) and testing (30%). More information about model validation can be found in (Ahmad et al, 2021;Benaafi et al, 2022;Ghali, Usman, Chellube, et al, 2020b;Ismail et al, 2022;. Moreover, Fig.…”

Section: Model Validationmentioning

confidence: 99%

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Usman,

Mati,

Naganna

et al. 2024

Preprint

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The need for qualitative determination of mycotoxins in food samples is of paramount importance in designing procedures for their prevention, as well as understanding their adverse effects on humans and animals. In this article, a machine learning technique, specifically support vector regression (SVR) hyphenated with two metaheuristic algorithms - Harris-hawks optimization (HHO) and Particle Swarm Optimization (PSO) models (i.e., SVR-HHO and SVR-PSO) was used to forecast the chromatographic behaviour of various classes of mycotoxins in food samples. Three different metrics were employed to anticipate the model's performance: mean square error (MSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency (NSE). The simulation results showed that the M3 input variable combination demonstrated higher performance accuracy than M1 and M2 with both SVR and its hybridized versions (SVR-HHO and SVR-PSO) during both the training and testing stages. In general, the hybridized model, based on the used assessment measures, SVR-HHO performed better in the training and testing phases than the other two data-driven techniques. Overall, the results show that employing chromatographic techniques, machine learning and metaheuristic approaches can both accurately predict the qualitative characteristics of mycotoxins in food samples.

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“…It represents a distribution of functions rather than a single fixed function. GPR provides a predictive mean for each input point, which is the expected value of the output variable given the input features [49][50][51]. This predictive mean represents the best estimate of the target variable.…”

Section: Gpr Machine Learning-based Approachesmentioning

confidence: 99%

Validation of Instruments for the Improvement of Interprofessional Education through Educational Management: An Internet of Things (IoT)-Based Machine Learning Approach

Mohamed,

Altinay,

Altinay

et al. 2023

Sustainability

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Educational management is the combination of human and material resources that supervises, plans, and responsibly executes an educational system with outcomes and consequences. However, when seeking improvements in interprofessional education and collaborative practice through the management of health professions, educational modules face significant obstacles and challenges. The primary goal of this study was to analyse data collected from discussion sessions and feedback from respondents concerning interprofessional education (IPE) management modules. Thus, this study used an explanatory and descriptive design to obtain responses from the selected group via a self-administered questionnaire and semi-structured interviews, and the results were limited to averages, i.e., frequency distributions and summary statistics. The results of this study reflect the positive responses from both subgroups and strongly support the further implementation of IPE in various aspects and continuing to improve and develop it. Four different artificial intelligence (AI) techniques were used to model interprofessional education improvement through educational management, using 20 questions from the questionnaire as the variables (19 input variables and 1 output variable). The modelling performance of the nonlinear and linear models could reliably predict the output in both the calibration and validation phases when considering the four performance metrics. These models were shown to be reliable tools for evaluating and modelling interprofessional education through educational management. Gaussian process regression (GPR) outperformed all the models in both the training and validation stages.

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Development of chemometrics-based neurocomputing paradigm for simulation of manganese extraction using solid-phase tea waste

Cited by 12 publications

References 42 publications

Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques

Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Validation of Instruments for the Improvement of Interprofessional Education through Educational Management: An Internet of Things (IoT)-Based Machine Learning Approach

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