Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN

Haruna, Sadi İbrahim; Malami, Salim Idris; Adamu, Musa; Usman, A. G.; Farouk, Aib; Ali‬, Shaban Ismael Albrka; Abba, S.I.

doi:10.1007/s13369-021-05715-3

Cited by 51 publications

(23 citation statements)

References 42 publications

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“…Some swimmers have weak safety awareness, blind selfconfidence, and enter deep water by mistake, which has great potential safety hazards [20]. Although some parents are at the poolside, due to chatting or gazing at mobile phones, they are negligent and cannot monitor their children, which also poses a threat to the safety of children.…”

Section: Analysis Of Intelligent Management Model Of Swimming Place W...mentioning

confidence: 99%

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Yuan

Han

2022

Mathematical Problems in Engineering

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In this paper, a nonlinear random matrix approach is used to analyze the management of swimming place waters, and in this way, an intelligent management model is designed and applied to the actual swimming place waters management. Firstly, some basic small deviation results of the random matrix are presented. Then, several types of small deviation inequalities are obtained for the maximum eigenvalues of independent stochastic semi-positive definite (PSD) matrices. These small deviation inequalities are independent of the matrix dimension, and the results apply to high-dimensional and even infinite-dimensional matrices. For the inverse eigenvalue problem of higher-order doubly random matrices, the conclusion that two symmetric doubly random matrices are combined into a higher-order symmetric doubly random matrix is proved. In other words, the method of constructing new symmetric random matrices using smaller matrices with known spectra is applied to the inverse eigenvalue problem of higher-order symmetric random matrices, and sufficient conditions for the existence of the solution of the inverse eigenvalue problem of higher-order random matrices are derived, avoiding the discussion of the parity of the number of eigenvalues. This paper focuses on realistic management, to explore the standardization of safety management of swimming venues, and is based on modern management theory, risk management theory and methods, using research integration method, fieldwork method, questionnaire survey method, expert interview method, and other research methods, detailing the methods and conclusions of risk identification in safety management of swimming venues, and further exploring the safety management standards of swimming venues on this basis. And, combined with risk management theory, depending on the probability of occurrence and the degree of harm of risk, four basic disposal methods such as risk avoidance, risk control, risk transfer, and risk self-retaining are proposed.

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Section: Analysis Of Intelligent Management Model Of Swimming Place W...mentioning

confidence: 99%

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Yuan

Han

2022

Mathematical Problems in Engineering

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“…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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“…AI and other computational machine learning models have been recently developed and have been demonstrated to be effective in comparison to various classical, statistical physics-based, and mathematical models [17][18][19][20]. The promising applications of AI-based models are not limited to the understanding and removal of HMs but also extend to the system identification of science and engineering problems [21][22][23][24][25][26][27]. The superiority of data-driven models is attributed to certain factors, such as the building of models, type of learning, data type, and basin characteristics.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

et al. 2022

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Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

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Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN

Cited by 51 publications

References 42 publications

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

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

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

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