“…The extracted features in the previous step are used in the classification process and to build the classification model for COVID-19 disease. All the features are used for training and testing the KNN and SVM classifiers [26] , [27] , [28] . Fig.…”
“…The extracted features in the previous step are used in the classification process and to build the classification model for COVID-19 disease. All the features are used for training and testing the KNN and SVM classifiers [26] , [27] , [28] . Fig.…”
“…The motive of this work is to solve the above NMDS in the heterogeneous environment using the layer structure of Morlet wavelet (MWNN) kernel together with global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. Numerical stochastic approaches have been widely applied to solve a wide variety of applications, like delay singular functional model [94]- [95], COVID-19 dynamical model [96]- [97], singular fractional models [98]- [99], preypredator system [100], singular nonlinear higher order models [101]- [103], HIV infection system [104], multisingular differential systems [105]- [106] and dengue fever nonlinear system [107]. Based on these renowned applications, the authors are motivated to solve the NMDS with the help of the MWNN-GA-ASA.…”
Section: Figure 2 Ecosystem Of Iot and Mosquito Releasementioning
The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology's use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito's density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10 -06 to 10 -09 . The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil's inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.INDEX TERMS Mosquito release ecosystem, IoT, SDN, artificial neural networks, heuristic algorithm, Adams's method.
“…Probabilistic methodologies via AI algorithms have been implemented exhaustively for a variety of linear/nonlinear systems arising in a spectrum of applications in social, economic, environment, physical, and engineering disciplines [13][14][15][16][17]. A few illustrations of paramount interest include ecological studies [18], acoustics [19], physics [20][21][22][23][24], bioinformatics [25][26][27][28][29][30][31], fluid dynamics [32][33][34][35], financial mathematics [36,37], and energy [38]. These motivational recent relevant and valuable reported articles inspired authors to investigate the intelligence computing paradigm for numerical treatment and analysis for EESs.…”
The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.
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