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All Outputs (18)

Confidentiality and linked data (2018)
Book Chapter
Ritchie, F., & Smith, J. Confidentiality and linked data. In G. Roarson (Ed.), Privacy and Data Confidentiality Methods – a National Statistician’s Quality Review (1-34). Newport: Office for National Statistics

This chapter considers the confidentiality issues around linked data. It notes that the use and availability of secondary (adminstrative or social media) data, allied to powerful processing and machine learning techniques, in theory means that re-ide... Read More about Confidentiality and linked data.

The internet of flying things (2018)
Book Chapter
Pigatto, D. F., Rodrigues, M., de Carvalho Fontes, J. V., Pinto, A. S. R., Smith, J., & Branco, K. R. L. J. C. (2018). The internet of flying things. In Q. Hassan (Ed.), Internet of Things A to Z: Technologies and Applications (529-562). Wiley. https://doi.org/10.1002/9781119456735.ch19

Popularly known as drones, unmanned aerial vehicles (UAVs) have been applied in several fields, usually operating in cooperative and collaborative swarms to enable the execution of more dynamic missions. Thus, the new Flying Ad Hoc Networks (FANETs)... Read More about The internet of flying things.

Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models (2017)
Book Chapter
Smith, E. M., Smith, J., Legg, P., & Francis, S. (2017). Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models. In F. Chao, S. Schockaert, & Q. Zhang (Eds.), Advances in Computational Intelligence Systems: UKCI 2017 (191-202). Springer Cham

The ability to predict future states is fundamental for a wide variety of applications, from weather forecasting to stock market analysis. Understanding the related data attributes that can influence changes in time series is a challenging task that... Read More about Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models.

Evolutionary algorithms (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). Evolutionary algorithms. In F. Neri, C. Cotta, & P. Moscato (Eds.), Handbook of Memetic Algorithms (9-27). Springer

What are evolutionary algorithms? (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). What are evolutionary algorithms?. In C. Cotta, & F. Neri (Eds.), Handbook of Memetic Algorithms (9-27). Berlin, Heidelberg, New York: Springer

Self-adaptive and coevolving MAs (2010)
Book Chapter
Smith, J. (2010). Self-adaptive and coevolving MAs. In C. Cotta, F. Neri, & P. Moscato (Eds.), Handbook of Memetic Algorithms. Berlin, Heidelberg, New York: Springer

Memetic evolutionary algorithms (2005)
Book Chapter
Hart, W., Krasnogor, N., & Smith, J. (2005). Memetic evolutionary algorithms. In W. Hart, N. Krasnogor, & J. Smith (Eds.), Recent Advances in Memetic Algorithms (3-30). Berlin, Heidelberg, New York: Springer

Multimodal problems and spatial distribution (2003)
Book Chapter
Eiben, A. E., Smith, J., & Computing-slides, C. (2003). Multimodal problems and spatial distribution. In A. Eiben, & J. Smith (Eds.), Introduction to Evolutionary Computing (155). Springer

Genetic algorithms (2002)
Book Chapter
Smith, J. (2002). Genetic algorithms. In P. M. Pardalos, & H. E. Romeijn (Eds.), Handbook of Global Optimization (275-362). Boston, USA: Kluwer Academic Publishers