Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications. Группа авторов

the total population. This compartment may also be called “recovered” or “resistant”.

      1.3.2 SIR Model (Susceptible-Infected-Recovered)

      The outbreak prediction has become highly complicated for emerging scientific science due to the pandemic scenario of COVID-19 disease cases around the world. To accurately forecast the forecasts, many epidemiological mathematical models of spread are growing daily. In this analysis, to analysis the various parameters of this model for India, the classical susceptible-infected-recovered (SIR) modelling method was used. By considering various governmental lockdown initiatives in India, this method was studied [14].

      Estimation of parameters of SIR model of India using an actual data set:

      Fundamental models based on compartments, as seen in the following, were used for the epidemic mathematical model:

      1 (Susceptible->Infectible) SI model,

      2 (Susceptible->Infectible-> Susceptible) SIS model, and

      3 (Susceptible->Infectible-> Recovery/Removed) SIR model.

      The standard SIR model is basically a series of differential equations that can be classified as susceptible (if previously unexposed to pandemic disease), infected (if presently conquered by pandemic disease), and removed (either by death or recovery) [15].

1.4 Conclusion

      The goal of this chapter was to present some of the machine learning and AI principles and methodologies and explore some of their possible applications in different aspects of computational mechanics. The methodologies outlined herein are maturing rapidly, and many new applications are likely to be found in computational mechanics. Undoubtedly, AI methodologies would inevitably become, to the same degree as today’s “traditional” algorithmic devices, a natural and indispensable part of the set of computer-based engineering resources. These instruments would then greatly elevate the role of computers in engineering from the current focus on calculation to the much wider field of reasoning.

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      2. M. Muselli, A. Bertoni, M. Frasca, A. Beghini, F. Ruffino and G. Valentini, “A Mathematical Model for the Validation of Gene Selection Methods,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 5, pp. 1385-1392, Sept.-Oct. 2011, doi: 10.1109/TCBB.2010.83.

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      1 ^*Corresponding author: [email protected]

      2 ^† Corresponding author: [email protected]

      2

      Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms

       P. Vijayakumar*, Prithiviraj Rajalingam and S. V. K. R. Rajeswari

       ECE Department, SRMIST, Kattankulathur, Chennai, India

       Abstract

      The rapid growth of the Internet of Things (IoT) with advanced applications requires high speed and real-time computing power. Edge computing brings the computation of data closer to the machine where it is being collected. It leads to a decrease in latency, bandwidth usage, and resources for the server and its cost. The significant challenges in edge computing are 1) optimal offloading decision making, 2) resource allocation, 3) Meeting Quality-of-Service (QoS) and Experience (QoE). This chapter addresses the above challenges using mathematical models, Deep Learning and the Evolutionary algorithm. The deep learning algorithm solves the highly complex problem by developing a model from the training data or observation (reinforcement learning). The deep learning approach converts the optimization problem of edge computing into classification or regression or intelligent decision-making problems and solves them. The Evolution algorithm finds an optimum solution for the given problem through the natural process of evaluation, which is used to solve the edge computing multi-optimization problem. An evolution algorithm like a genetic algorithm and ant colony can solve a few research problems