论文标题
机器学习可解释性及其对智能校园项目的影响
Machine Learning Interpretability and Its Impact on Smart Campus Projects
论文作者
论文摘要
在过去的几十年中，机器学习（ML）显示了预测分析的越来越多的能力。它在医疗保健，刑事司法，金融和智慧城市等不同领域变得无处不在。例如，北安普敦大学（University of Northampton）正在其新的Waterside校园建立一个具有多层物联网和软件定义网络（SDN）的智能系统。该系统可用于优化智能建筑能源效率，提高其租户和访客的健康和安全性，协助人群管理和寻路，并改善互联网连接。
Machine learning (ML) has shown increasing abilities for predictive analytics over the last decades. It is becoming ubiquitous in different fields, such as healthcare, criminal justice, finance and smart city. For instance, the University of Northampton is building a smart system with multiple layers of IoT and software-defined networks (SDN) on its new Waterside Campus. The system can be used to optimize smart buildings energy efficiency, improve the health and safety of its tenants and visitors, assist crowd management and way-finding, and improve the Internet connectivity.
