| Title | Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data |
|---|---|
| ID_Doc | 39284 |
| Authors | Chin, J; Callaghan, V; Lam, I |
| Title | Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data |
| Year | 2017 |
| Published | |
| Abstract | This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naive Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work. |
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