Computer vision assisted human computer interaction for logistics management using deep learning

Abstract

Human-Computer Interaction is the secret to technological advancement in the area of logistics and supply chain. The key challenges are the degree of energy transferred to devices, like automated vehicles and robotic equipment, and lack of belief in intelligent decision-making, which may overrule the system in the event of misperceptions of automated decisions. This paper presents an efficient Logistics Management Framework Using Deep Learning (eLMF-DL) to implement the computer vision-assisted Human-Computer Interaction (HCI) in the logistic management sector. With a hybrid CNN-LSTM network, eLMF-DL implements a single-stage or one-step convergence optimum decision-support design model that intelligently combines production maximization and demand forecasting. The architecture with the integration of convolutional neural network and long short-term memory network models the machine dynamics and relationships in assorted diverse logistics services demand. To determine uncertainties through dynamic delivery and optimal decisions on allocating logistical service power, the eLMF-DL results in the highest performance.