The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Comparison between Nearest Neighbours and Bayesian Network for Demand Forecasting in Supply Chain Management
Machine Learning has found to be playing a significant role in solving issue of demand forecasting in supply chain management, where many traditional methods result in substandard accuracies. There is a high demand of robust computational systems for predicting the trends of demands for the purpose of Inventory Management in supply chain management of an organization. Every organization has Terabytes of transactions and shipments data. These terabytes of data help in defining and implementing robust techniques that can help in identifying stochastic dependency in the historical data to determine future trends. Attributes like Consignee address, shipper, shipper address, place of delivery, weight of container and country are important for prediction supply trends. Naive Bayes classifier is used to make decision in uncertainty and K nearest neighbor is lazy and supervised learning algorithm to determine the trends in supply chain. The purpose of this research is to bring a close comparison between Nearest Neighbor Algorithm and Bayesian Networks using confusion matrix as a performance metric and Walmart dataset has been used for simulation. The results show that Bayesian networks technique surpasses the Nearest Neighbor technique in detecting relations in dataset for prediction demand in supply chain. Bayesian networks, emerges to be robust in demand prediction instead of increasing K-neighbors in the supervised learning algorithm.
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