Abstract: Present disclosure generally relates to resource scheduling systems, more particularly relates to a method and a system for priority-based resource scheduling with load balancing. A method includes receiving, from client devices, client requests to execute tasks on servers associated with one or more Virtual Machines (VMs). Further, method includes determining usage-related information of each server, upon receiving client requests. Furthermore, method includes prioritizing received client requests, based on request parameters associated with client requests. Further, method includes assigning computing resources in servers to execute tasks for client devices using dynamic programming technique. The method includes monitoring dynamically, usage-related information of servers. Further, method includes migrating from first server to second server of servers, tasks using round-robin technique and/or graph theory technique, based on usage-related information.

Abstract: A system and method for bot processing automation are provided. The system includes a processor. The system also includes a memory comprising a set of instructions, which when executed by the processor cause the processor to: receive, from a repository, the images from VDI are converted to user defined text input (CSV, XLSX, etc.,) and uses the contextual AI that can contribute to the accuracy improvement and can help to build the context for the domain knowledge base. We use Named Entity Recognition (NER) technique in Natural Language Processing (NLP) for tagging the fields and associate with knowledge graphs. With this information, the contextual AI can act like a wrapper to the BOT and validate the front end rejections.