TY - GEN
T1 - Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources
AU - Karn, Arodh Lal
AU - Webber, Julian L.
AU - Mehbodniya, Abolfazl
AU - Stalin David, D.
AU - Subramaniam, Balu
AU - Rangasamy, Rajasekar
AU - Sengan, Sudhakar
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - In other growth trends and sustainable development of the economy’s globalization, low manufacturing utilization rates, production resource imbalances, and impaired coordination functions are growing. To tackle the difficulties mentioned above, organizations must securely determine how to share distant and heterogeneous production resources. Previses work is a network language learning framework for learning audio language clouds. The application offers a single computer user and Internet users economic and easy oral learning and large-scale oral assessment technologies. To select Cloud Manufacturing (CM) resources to fulfill service production enterprise assessment, five first-level metrics (time, cost, availability, reliability, security) and 13 s are used. Deep Learning (DL) is utilized to calculate the index weights, the ultimate total score, each indication, and membership at all levels. Degree determines it. It quantifies Cloud Manufacturing Resource Selection (CMRS). This model shows that manufacturing RS involves DL. Finally, it’s proven feasible to pick the resources to produce using a fuzzy comprehensive assessment.
AB - In other growth trends and sustainable development of the economy’s globalization, low manufacturing utilization rates, production resource imbalances, and impaired coordination functions are growing. To tackle the difficulties mentioned above, organizations must securely determine how to share distant and heterogeneous production resources. Previses work is a network language learning framework for learning audio language clouds. The application offers a single computer user and Internet users economic and easy oral learning and large-scale oral assessment technologies. To select Cloud Manufacturing (CM) resources to fulfill service production enterprise assessment, five first-level metrics (time, cost, availability, reliability, security) and 13 s are used. Deep Learning (DL) is utilized to calculate the index weights, the ultimate total score, each indication, and membership at all levels. Degree determines it. It quantifies Cloud Manufacturing Resource Selection (CMRS). This model shows that manufacturing RS involves DL. Finally, it’s proven feasible to pick the resources to produce using a fuzzy comprehensive assessment.
KW - Cloud manufacturing
KW - Deep learning
KW - Employees of multinational enterprises
KW - English training
KW - Service evaluation
UR - http://www.scopus.com/inward/record.url?scp=85161434829&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-7455-7_28
DO - 10.1007/978-981-19-7455-7_28
M3 - Conference Proceeding
AN - SCOPUS:85161434829
SN - 9789811974540
T3 - Lecture Notes in Networks and Systems
SP - 369
EP - 380
BT - Innovations in Computer Science and Engineering - Proceedings of the 10th ICICSE, 2022
A2 - Saini, H. S.
A2 - Sayal, Rishi
A2 - Govardhan, A.
A2 - Buyya, Rajkumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Conference on Innovations in Computer Science and Engineering, ICICSE 2022
Y2 - 16 September 2022 through 17 September 2022
ER -