Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)

Ahmad, Mubarak Hussaini; Usman, A. G.; Abba, S.I.

doi:10.1007/s40203-021-00090-1

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…Multilinear regression (MLR) is a statistical model that determines the relationship between a dependent variable and at least one independent variable [55]- [57]. The main idea of the research by Fullerton Jr et al (2016) was to determine the dynamics of water necessity for the city of El Paso (Texas, USA) using several predicting techniques, comprising the Linear Transfer Function (LTF) by Box and Jenkins (1976) [14], [57]- [60]. [61] Adopted ANN and MLR techniques to predict WQI in Shivganga River basin India.…”

Section: Linear and Statistical Methodsmentioning

confidence: 99%

“…The metrics for appraising the model accuracy were the mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE), mean absolute error, coefficient of determination (R 2 ), and correlation coefficient (R). The choice of these parameters stemmed from their application in numerous related studies as effective means of establishing the accuracy of a prediction model [26]- [31], [46]- [50], [52]- [54], [60], [63]- [67]. Some of the most important evaluation criteria metrics that were used for evaluating the performance in this review are explained below; RMSE (RMSE (Root Mean Squared Error) is the error rate by the square root of MSE.…”

Section: Evaluation Of Performancementioning

confidence: 99%

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A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Aminu¹

2022

Global J. Eng. Technol. Adv.

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The development of computer models for water quality index forecasting has been a leading research topic worldwide which has been considerably recognized over the last two decades; the balance between efficient water quality requires a good water management technique. The balance is said to be achieved through various procedures many of which require the application of computer-aided forecasting tools. In this paper, a decade research review on the water quality index in the field of artificial intelligence was carried out with the aim to present the most viable or most suitable methods and models to be adopted for future researchers in the field of water quality. The review incorporates the developed models such as ANN, ANFIS, SVM, other regression or time-series, and other soft computing models. This research shows that the study focused on a decade review of the methods and models, and also, there is room for long-term forecasts. It also shows that there is no single AI model that outperforms all the remaining AI models but It is necessary to evaluate the strength of each model combination for each region thus to know what type of method or model that works best for the country or region. The use of AI has grown significantly in recent decades however there is enough room for researchers to duel in and improve in the field of water quality index.

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Section: Linear and Statistical Methodsmentioning

confidence: 99%

Section: Evaluation Of Performancementioning

confidence: 99%

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Aminu¹

2022

Global J. Eng. Technol. Adv.

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“…In order to efficiently train AI base model, these data need to be clean and filtered properly, because the raw data often comprised of missing records, outliers, noise, discrepancies of codes and names or was infected by all kind of error including human and instrumental. More information on normalization and statistical analysis can be found in [17], [18], [19], [16], [20].…”

Section: Data Processing and Statistical Analysismentioning

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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“…This layer's nodes are either fixed or non-adaptive, with the circle node labelled as N. The ratio between the 𝑖-th rule's firing strength and the sum of all rules' firing strengths is calculated at each node. The normalized firing strength is the name given to this result [23]…”

Section: Layermentioning

confidence: 99%

“…As a result, the generally used error metrics are used to evaluate the outputs of prediction models as well as to compare them to one another. Metrics such as Coefficient of determination (R 2 ), Correlation coefficient (R), Mean square error (MSE) and Root mean square error (RMSE) were used to compare the performance success of the forecasting models used in this study more information on performance evaluation can be found in the following references [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [23], [40], [41], [42], [43], [44], [45], [46] and [47].…”

Section: Performance Evaluationmentioning

confidence: 99%

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

Yakubu¹,

B²,

Najashi³

et al. 2022

GSC Adv. Eng. Technol.

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Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.

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Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)

Cited by 9 publications

References 20 publications

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

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

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

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