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In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management.

The heart attack happens if the flow of blood leads to blocks in any of the blood veins and vessels liable for delivering blood into internal parts of the heart. In the modern life activities and habits, the males and females hold the same responsibility and burden of risk. The absence of understanding frequently leads to a postponement in dealing with the heart attack issues, which could worsen the injury and in most of the situations shown to be dead. Several researchers have applied data mining techniques to diagnose illnesses, and the results have been encouraging. Some methods forecast a specific illness, whereas others predict a wide spectrum of illnesses. In addition, the accuracy of sickness predictions can be improved. This post went into great length on the many approaches of data classification that are currently available. Algorithms primarily represent themselves through representations. Data classification is a typical but computationally intensive task in the area of information technology. A huge amount of data must be analysed in order to come up with an effective plan for fighting disease. Metaheuristics are frequently employed to tackle optimization issues. The accuracy of computing models can be improved by using metaheuristic techniques. Early disease diagnosis, severity evaluation, and prediction are all popular uses for artificial intelligence. For the sake of patients, health care costs, and slowed course of disease, this is a good idea. Machine learning approaches have been used to achieve this. Using machine learning and metaheuristics, this study attempts to classify and forecast human heart disease.

In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.