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Data Mining and Predictive Analysis with Application to Blockchain, Smart Farming, Radiocarbon dating, and mHealth

Summary

The focus of this research line is to develop novel computer methods for feature selection, time series and predictive analysis. The algorithms that we develop are evaluated in various applications:

  • First-person image analysis with wearable cameras
  • Cryptocurrency and distributed ledgers
  • Radiocarbon dating
  • Mobile Health
  • Smart livestock management

Participants

  • George Azzopardi
  • Estefania Talavera Martinez
  • Andreas Neocleous
  • Ahmad Alsahaf
  • Xueyi Wang

Students

  • Sofie Lovdal
  • Steven Farrugia

Journal Publications

  1. A. Alsahaf, N. Petkov, V. Shenoy, G. Azzopardi (2021), A framework for feature selection through boosting, Expert Systems with Applications, vol. 187, 115895. https://doi.org/10.1016/j.eswa.2021.115895
  2. S. Lovdal, R. van den Hartigh, G. Azzopardi (2021), Injury Prediction in Competitive Runners with Machine Learning, International Journal of Sports Physiology and Performance, pp.1522-1531, vol. 16 (10). https://doi.org/10.1123/ijspp.2020-0518
  3. X. Wang, J. Ellul, and G. Azzopardi (2020), Elderly Fall Detection Systems: A Literature Survey, Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2020.00071
  4. E. van der Heide, C. Kamphuis, R. Veerkamp, I. Athanasiadis, M. van Pelt, G. Azzopardi, B. Ducro (2020), Improving predictive performance on survival in dairy cattle using an ensemble learning approach, Computers and Electronics in Agriculture, vol. 177, 105675. https://doi.org/10.1016/j.compag.2020.105675
  5. S. Farrugia, J. Ellul, and G. Azzopardi (2020), Detection of illicit accounts over the Ethereum Blockchain, Expert Systems with Applications, vol. 150. https://doi.org/10.1016/j.eswa.2020.113318
  6. A. Neocleous, G. Azzopardi, M. Kuitems, A. Scifo, M. Dee (2019), Trainable filters for the identification of anomalies in cosmogenic isotope data, IEEE Access vol. 7, p. 24585 -24592. https://doi.org/10.1109/ACCESS.2019.2900123
  7. A. Neocleous, G. Azzopardi, M. Dee (2019), Identification of possible D14C anomalies since 14 ka BP: A computational intelligence approach, Journal of Science of the Total Environment, vol. 663, pp. 162-169. https://doi.org/10.1016/j.scitotenv.2019.01.251
  8. A. Alsahaf, G. Azzopardi, B. Ducro, E. Hanenberg, R. F. Veerkamp, N. Petkov (2019), Estimation of muscle scores of live pigs using a Kinect camera, IEEE Access, vol. 7, p. 52238 – 52245. https://doi.org/10.1109/ACCESS.2019.2910986
  9. A. Alsahaf, B. Ducro, E. Hanenberg, R. Veerkamp, G. Azzopardi, N. Petkov (2018), Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest, Journal of Animal Science, vol. 96(12), pp. 4935-4943. https://doi.org/10.1093/jas/sky359

Publications in Conference Proceedings

  1. M. Spiteri, G. Azzopardi (2018), Customer churn prediction for a motor insurance company, 6th IWDS, Berlin, ICDIM Proceedings.
  2. A. Alsahaf, G. Azzopardi, B. Ducro, R.F. Veerkamp and N. Petkov (2018), Predicting Slaughter Weight in Pigs with Regression Tree Ensembles, Applications of Intelligent Systems – Proceedings of the 1st International APPIS Conference 2018, Frontiers in Artificial Intelligence and Applications 310, 1-9 . IOS Press, Amsterdam. https://doi.org/10.3233/978-1-61499-929-4-1
  3. A. Alsahaf, G. Azzopardi, B. Ducro, R. F. Veerkamp, N. Petkov (2018), Assigning pigs to uniform target weight groups using machine learning, World Congress on Genetics Applied to Livestock Production (WCGALP), Auckland, New Zealand

Poster presentation in Conferences

  1. F. Mohsen, G. Azzopardi (2021), “Predicting the Removability of Third-party Apps from the Google Play Store”, ICTOpen
  2. A. Alsahaf, G. Azzopardi, N. Petkov (2018), “Estimation of live muscle scores of pigs with RGB-D images and machine learning”, FAIR Data Science for Green Life Sciences, Wageningen
  3. A. Bhole, M. Biehl, G. Azzopardi (2018), “Automatic identification of Holstein cattle using non-invasive computer vision approach”, FAIR Data Science for Green Life Sciences, Wageningen
  4. A. Neocleous, G. Azzopardi, M. Dee (2018), “Identification of Possible Miyake Events using COSFIRE Filters”, International Radiocarbon Conference, Trondheim (Norway)
  5. A. Neocleous, G. Azzopardi, M. Dee (2018), “Signal Processing for the Identification of Miyake Events”, International Radiocarbon Conference, Trondheim (Norway)

Data Sets

  1. S. Farrugia; J. Ellul, G. Azzopardi (2021), "Detection of illicit accounts over the Ethereum blockchain", https://doi.org/10.34894/GKAQYN
    • The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier.
  2. S. Lovdal; R. den Hartigh; G. Azzopardi (2021), "Replication Data for: Injury Prediction In Competitive Runners With Machine Learning", https://doi.org/10.34894/UWU9PV
    • The data set consists of a detailed training log from a Dutch high-level running team over a period of seven years (2012-2019). We included the middle and long distance runners of the team, that is, those competing on distances between the 800 meters and the marathon. This design decision is motivated by the fact that these groups have strong endurance based components in their training, making their training regimes comparable. The head coach of the team did not change during the years of data collection. The data set contains samples from 74 runners, of whom 27 are women and 47 are men. At the moment of data collection, they had been in the team for an average of 3.7 years. Most athletes competed on a national level, and some also on an international level. The study was conducted according to the requirements of the Declaration of Helsinki, and was approved by the ethics committee of the second author’s institution (research code: PSY-1920-S-0007).