Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal International Journal of Supply Chain Management
Tahir Kasim Aldabagh, Ghada Mohammad
Unknown Affiliation
Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Proposed Algorithm for Business Management by using Swarm Intelligence and Neural Network in Supply Chain System Tahir Kasim Aldabagh, Ghada Mohammad; Majeed Alneamy, Jamal Salahaldeen
International Journal of Supply Chain Management Vol 9, No 5 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v9i5.5593

Abstract

Abstract— Neural  network  technology  has  been  successfully  applied  in  many  business  areas.  This  paper  systematic  summarizes  the  applications  of  neural  network  technology  in  supply  chain  management, which include three areas: optimization , forecasting and decision support. It uses supply chain -specific parameter estimation using supply chain processing techniques to model the optimal network system. It then joined with generic data compression algorithms to form compact bitstream. Artificial neural network (ANN) in supply chain management was established from bussines neural network and also known as connectionist system. Artificial intelligence (AI) is shown by software and machines. The present study aim to evaluate the proposed algorithm for supply chain encoding by using random overlapped and swarm intelligence. The study can be concluded as the proposed algorithm take more time than overlapping algorithm. Because, the key is generated  by the lion optimization algorithm (LOA) and using of features make the algorithm faster. The restored signal accuracy 100% and the size of encoded file equal to the input file.
Co-Authors Majeed Alneamy, Jamal Salahaldeen