Smart Development of Big Data App for Determining the Modelling of Covid-19 Medicinal Compounds Using Deep AI Core Engine System

Abstract

Smart Big Data App Using Deep Artificial Intelligence (AI) Core Engine System focuses on solving problems related to the difficulty in building a prototyping model computer simulation like in silico as a model initiation of the complex Covid-19 medicinal compound involving Big Data ecosystems such as Hadoop and Spark. The difficulty is for example, currently, it is still very arduous to measure the rate of mixture of a compound when combined with many other compounds, which can consider the trade-off in minimizing the negative effects but optimizing the positive effects. In addition, the computation time becomes very long when using a system that is not in the Big Data ecosystem since this is proportional to the large number of compounds that vary widely and also the number of different situations of Covid-19 patients with other congenital diseases (comorbid) or without congenital disease; thus, it can be considered to be included in a condition that requires a very complex computational process which is very difficult to model using a conventional mathematical approach because the calculations are certainly very complex when compared to approaches using meta-heuristics algorithms such as Particle Swarm Optimization (PSO) which is much easier but requires a very large particle population space and iterative process to achieve global convergence. As a consequence, it requires fast computation processes based on distributed computing such as using the Big Data ecosystem. From the review of these problems, the system was created based on the Computational Intelligence; hence, the end-users, especially the developers, can build an application easier in spite of the complex computations, one of the which is in the meta-heuristic technique to minimize the negative effects and optimize the positive effects of the medicinal compounds by including a lot of data to get a better modeling. Therefore, the prototyping modeling project can be quick and robust, and achieve high performance measurements.