Bibliometric Survey on Predictive Analysis using Multiple Regression for Invoice Generation System

The research is exploring the opportunities available for the application of Predictive analysis using multi regression for invoice generation systems. We work by analyzing the data generated and saved in the database of the clinic. This database contains the financial operations along and also contains other useful information that is being gathered about the patients. We are proposing a different approach for a billing system to make the task easier for the generation of invoices for the clinic. The mapping of the relational data and using a predictive model that is based on a regression analysis that will predict the future values involved in the business. We found that predicting the future scope of the treatments can be done using a multi-regression model. In this Bibliometric study, Different Evolutionary algorithms have been used for prediction. Different clustering techniques are carried out on bibliographic datasets which leads to getting a higher silhouette of data. The analysis based on these algorithms will be focused on further work. The advantage of our study is that we have worked on world data to generate models. These models are relatively easy to implement

CCCORE: Cloud Container for Collaborative Research

Cloud-based research collaboration platforms render scalable, secure and inventive environments that enabled academic and scientific researchers to share research data, applications and provide access to high- performance computing resources. Dynamic allocation of resources according to the unpredictable needs of applications used by researchers is a key challenge in collaborative research environments. We propose the design of Cloud Container based Collaborative Research (CCCORE) framework to address dynamic resource provisioning according to the variable workload of compute and data-intensive applications or analysis tools used by researchers. Our proposed approach relies on–demand, customized containerization and comprehensive assessment of resource requirements to achieve optimal resource allocation in a dynamic collaborative research environment. We propose algorithms for dynamic resource allocation problem in a collaborative research environment, which aim to minimize finish time, improve throughput and achieve optimal resource utilization by employing the underutilized residual resources