2023
1.
Jokar, Fatemeh; Azzopardi, George; Palotti, Joao
Towards Accurate and Efficient Sleep Period Detection using Wearable Devices Inproceedings
Links | BibTeX | Altmetric | Tags: predictive analysis, wearables
@inproceedings{Jokar2023,
title = {Towards Accurate and Efficient Sleep Period Detection using Wearable Devices},
author = {Fatemeh Jokar and George Azzopardi and Joao Palotti},
doi = {https://doi.org/10.1007/978-3-031-44240-7_5},
year = {2023},
date = {2023-09-20},
urldate = {2023-07-01},
booktitle = {Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science},
keywords = {predictive analysis, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
2.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel F; Petkov, Nicolai
Assigning pigs to uniform target weight groups using machine learning Inproceedings
Abstract | Links | BibTeX | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018assigning,
title = {Assigning pigs to uniform target weight groups using machine learning},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel F Veerkamp and Nicolai Petkov},
url = {https://research.rug.nl/en/publications/assigning-pigs-to-uniform-target-weight-groups-using-machine-lear},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the World Congress on Genetics Applied to Livestock Production, vol. Species-Porcine},
volume = {1},
pages = {112},
abstract = {A standard practice at pig farms is to assign finisher pigs to groups based on their live weight measurements or based on visual inspection of their sizes. As an alternative, we used machine learning classification, namely the random forest algorithm, for assigning finisher pigs to groups for the purpose of increasing body weight uniformity in each group. Instead of relying solely on weight measurements, random forest enabled us to combine weight measurements with other phenotypes and genetic data (in the form of EBV’s). We found that using random forest with the combination of phenotypic and genetic data achieves the lowest classification error (0.3409) in 10-fold cross-validation, followed by random forest with phenotypic and genetic data separately (0.3460 and 0.4591), then standard assignment based on birth weight (0.5611), and finally standard assignment based on the weight at the start of the finishing phase (0.7015).},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
A standard practice at pig farms is to assign finisher pigs to groups based on their live weight measurements or based on visual inspection of their sizes. As an alternative, we used machine learning classification, namely the random forest algorithm, for assigning finisher pigs to groups for the purpose of increasing body weight uniformity in each group. Instead of relying solely on weight measurements, random forest enabled us to combine weight measurements with other phenotypes and genetic data (in the form of EBV’s). We found that using random forest with the combination of phenotypic and genetic data achieves the lowest classification error (0.3409) in 10-fold cross-validation, followed by random forest with phenotypic and genetic data separately (0.3460 and 0.4591), then standard assignment based on birth weight (0.5611), and finally standard assignment based on the weight at the start of the finishing phase (0.7015).
3.
Spiteri, Maria; Azzopardi, George
Customer Churn Prediction for a Motor Insurance Company Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: pattern recognition, predictive analysis
@inproceedings{spiteri2018customer,
title = {Customer Churn Prediction for a Motor Insurance Company},
author = {Maria Spiteri and George Azzopardi},
doi = {10.1109/ICDIM.2018.8847066},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 Thirteenth International Conference on Digital Information Management (ICDIM)},
pages = {173--178},
organization = {IEEE},
abstract = {Customer churn poses a significant challenge in various industries, including motor insurance. Retaining customers within insurance companies is much more challenging than in any other industry as policies are generally renewed every year. The main aim of this research is to identify the risk factors associated with churn, establish who are the churning customers and to model time until churn. The dataset used includes 72,445 policy holders and covers a period of one year. The data comprises information related to premiums, claims, policies and policy holders. The random forest algorithm turns out to be a very effective model for forecasting customer churn, reaching an accuracy rate of 91.18%. On the other hand, survival analysis was used to model time until churn and it was concluded that approximately 90% of the policy holders survived for the first five years while the majority of the policy holders survived till the end of the policy period. These results could be used to target the identified customers in marketing campaigns aimed at reducing the rate of churn while increasing profitability.},
keywords = {pattern recognition, predictive analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Customer churn poses a significant challenge in various industries, including motor insurance. Retaining customers within insurance companies is much more challenging than in any other industry as policies are generally renewed every year. The main aim of this research is to identify the risk factors associated with churn, establish who are the churning customers and to model time until churn. The dataset used includes 72,445 policy holders and covers a period of one year. The data comprises information related to premiums, claims, policies and policy holders. The random forest algorithm turns out to be a very effective model for forecasting customer churn, reaching an accuracy rate of 91.18%. On the other hand, survival analysis was used to model time until churn and it was concluded that approximately 90% of the policy holders survived for the first five years while the majority of the policy holders survived till the end of the policy period. These results could be used to target the identified customers in marketing campaigns aimed at reducing the rate of churn while increasing profitability.
4.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel; Petkov, Nicolai
Predicting slaughter age in pigs using random forest regression Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018predictingb,
title = {Predicting slaughter age in pigs using random forest regression},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel Veerkamp and Nicolai Petkov},
doi = {10.3233/978-1-61499-929-4-1},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Applications of Intelligent Systems 2018},
pages = {1-9},
organization = {IOS Press},
abstract = {Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.
5.
Alsahaf, Ahmad; Azzopardi, George; Ducro, Bart; Veerkamp, Roel F; Petkov, Nicolai
Predicting Slaughter Weight in Pigs with Regression Tree Ensembles. Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, smart farming
@inproceedings{alsahaf2018predicting,
title = {Predicting Slaughter Weight in Pigs with Regression Tree Ensembles.},
author = {Ahmad Alsahaf and George Azzopardi and Bart Ducro and Roel F Veerkamp and Nicolai Petkov},
doi = {10.3233/978-1-61499-929-4-1},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Frontiers in Artificial Intelligence and Applications},
volume = {310},
pages = {1--9},
abstract = {Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.},
keywords = {predictive analysis, smart farming},
pubstate = {published},
tppubtype = {inproceedings}
}
Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.
2014
6.
de Vries, Harm; Azzopardi, George; Knobbe, Arno; Koelewijn, Andre
Parametric nonlinear regression models for dike monitoring systems Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, time-series
@inproceedings{deVries2014,
title = {Parametric nonlinear regression models for dike monitoring systems},
author = {Harm de Vries and George Azzopardi and Arno Knobbe and Andre Koelewijn},
doi = {https://doi.org/10.1007/978-3-319-12571-8_30},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Advances in Intelligent Data Analysis, LNCS
},
volume = {8819},
number = {345-355},
publisher = {Springer},
abstract = {Dike monitoring is crucial for protection against flooding disasters, an especially important topic in low countries, such as the Netherlands where many regions are below sea level. Recently, there has been growing interest in extending traditional dike monitoring by means of a sensor network. This paper presents a case study of a set of pore pressure sensors installed in a sea dike in Boston (UK), and which are continuously affected by water levels, the foremost influencing environmental factor. We estimate one-to-one relationships between a water height sensor and individual pore pressure sensors by parametric nonlinear regression models that are based on domain knowledge. We demonstrate the effectiveness of the proposed method by the high goodness of fits we obtain on real test data. Furthermore, we show how the proposed models can be used for the detection of anomalies.},
keywords = {predictive analysis, time-series},
pubstate = {published},
tppubtype = {inproceedings}
}
Dike monitoring is crucial for protection against flooding disasters, an especially important topic in low countries, such as the Netherlands where many regions are below sea level. Recently, there has been growing interest in extending traditional dike monitoring by means of a sensor network. This paper presents a case study of a set of pore pressure sensors installed in a sea dike in Boston (UK), and which are continuously affected by water levels, the foremost influencing environmental factor. We estimate one-to-one relationships between a water height sensor and individual pore pressure sensors by parametric nonlinear regression models that are based on domain knowledge. We demonstrate the effectiveness of the proposed method by the high goodness of fits we obtain on real test data. Furthermore, we show how the proposed models can be used for the detection of anomalies.