Editorial cloud collaborative service improves authorized industrial server database performance

Eswara Narayanan, Balasundaram Muthukumar


The E-commerce platform for the automotive industry has various obstacles regarding product distribution and sales marketing. Problems such as identifying the product's low defect rate and mapping failure features to the product's existing quality set, both of which occur in the actual world, are included in this category. With the help of an editorial cloud collaborative service, this study will focus primarily on identifying the core causes of product failure. The machine learning confidential (MLC) protocol is used to authenticate the editor's identity when accessing the industrial server's database. A cloud-based collaborative editing service can also be used to extract the root cause of a specific problem from customer complaints. There may be some product flaws that can be remedied with extra features gathered from the end-user to understand real-world practicality better and ensure product accuracy reaches 100% target.


Additional failure features; Authenticated industrial server's database; Editorial cloud collaborative service; Machine learning confidential protocol

Full Text:


DOI: http://doi.org/10.11591/ijeecs.v29.i1.pp441-450


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics