Application of Instance Learning Algorithms to Analyze Logistics Data

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IJERTV10IS070166
Application of Instance Learning Algorithms to Analyze Logistics Data

Urenna Nwagwu , Abiodun Ayinde , Yemisi Olasoji

Predictive analytics has been applied in all works of life with the aim to process, analyze, evaluate, mitigate risks and create different computational models based on the organization goals for adequate decision making when a proper knowledge discovery methodology is adopted by the predictive learning environment. Several instance based learning classifiers have been applied in the field of supply chain management to build a multilayer learning predictive platform but with little emphasis on classifiers function normalization to reduce the convergence time of the trained model. A lot of companies have benefited by automating predictive analytics for their workflow, resource sharing, decision making, etc. With the evolution of various cloud computing service, startups companies have tapped into the cloud service to scale their business by leaning on enterprise based predictive models to drive their workflow for vital decision making process at lower cost to improve their forecasting abilities and responsiveness via real-time analytics.

IJERTEngineeringConference Proceedings