Abstract:

Due to their capacity to enable Data users to browse the Internet in a personalized manner, similarity systems are widely used. For instance, the collaborative Similarity system is a potent data personalization tool that makes a variety of helpful recommendations to a particular user based on feedback gathered fromneighbors. One of the elements influencing the success of the collaborative Similarity system is the similarity measure. Additionally, computer science's branch of machine learning and artificial intelligence, both of which are designed to improve human intelligence, are related technologies. AI and ML can be utilized in e-healthcare to improve workflow, automatically handle volumes of medical data, and offer useful medical decision assistance. The authors of this work take several popular artificial intelligence models that are now available in the present research studies. This paper proposes a mechanism for allocating jobs to store enormous amounts of data load for cloud resources to balance the infrastructure platforms' demands for big data and artificial intelligence. Using Bayesian theory, the maximum posterior probability for each physical host is determined. The experimental results on the benchmark datasets show that the proposed data classifier is computationally affordable and comparable with cutting-edge methods. The significance of the experimental results has been compared using the conventional multi-task load balancing method

Description:

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