Publications
2023 |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado Springer, 749 , 2023, ISBN: 978-3-031-42529-5. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{SOCO2023a, title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 1}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-42529-5}, doi = {https://doi.org/10.1007/978-3-031-42529-5}, isbn = {978-3-031-42529-5}, year = {2023}, date = {2023-09-05}, volume = {749}, publisher = {Springer}, series = {Lecture Notes in Networks and Systems}, keywords = {big data, clustering, deep learning, IoT}, pubstate = {published}, tppubtype = {proceedings} } |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado Springer, 750 , 2023, ISBN: 978-3-031-42536-3. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{SOCO2023b, title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 2}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-42536-3}, doi = {10.1007/978-3-030-20055-8}, isbn = {978-3-031-42536-3}, year = {2023}, date = {2023-09-05}, volume = {750}, publisher = {Springer}, series = {Lecture Notes in Networks and Systems}, keywords = {big data, clustering, deep learning, IoT}, pubstate = {published}, tppubtype = {proceedings} } |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado Springer, 14001 , 2023, ISBN: 978-3-031-40725-3. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{HAIS2023, title = {Proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2023) Salamanca, Spain, September 5-7, 2023}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-40725-3}, doi = {https://doi.org/10.1007/978-3-031-40725-3}, isbn = {978-3-031-40725-3}, year = {2023}, date = {2023-09-05}, volume = {14001}, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, keywords = {big data, clustering, deep learning, IoT}, pubstate = {published}, tppubtype = {proceedings} } |
A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez A new Apache Spark-based framework for big data streaming forecasting in IoT networks (Journal Article) Journal of Supercomputing, 79 , pp. 11078–11100, 2023. (Abstract | Links | BibTeX | Tags: big data, IoT) @article{FERNANDEZ23, title = {A new Apache Spark-based framework for big data streaming forecasting in IoT networks}, author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/article/10.1007/s11227-023-05100-x}, doi = {https://doi.org/10.1007/s11227-023-05100-x}, year = {2023}, date = {2023-02-02}, journal = {Journal of Supercomputing}, volume = {79}, pages = {11078–11100}, abstract = {Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.}, keywords = {big data, IoT}, pubstate = {published}, tppubtype = {article} } Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules. |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A.Troncoso Identifying novelties and anomalies for incremental learning in streaming time series forecasting (Journal Article) Engineering Applications of Artificial Intelligence, 123 , pp. 106326, 2023. (Links | BibTeX | Tags: energy, IoT, time series) @article{Melgar2023b, title = {Identifying novelties and anomalies for incremental learning in streaming time series forecasting}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A.Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S0952197623005109}, doi = {https://doi.org/10.1016/j.engappai.2023.106326}, year = {2023}, date = {2023-01-01}, journal = {Engineering Applications of Artificial Intelligence}, volume = {123}, pages = {106326}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso A novel distributed forecasting method based on information fusion and incremental learning for streaming time series (Journal Article) Information Fusion, 95 , pp. 163-173, 2023. (Links | BibTeX | Tags: energy, IoT, time series) @article{Melgar2023a, title = {A novel distributed forecasting method based on information fusion and incremental learning for streaming time series}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1566253523000635}, doi = {https://doi.org/10.1016/j.inffus.2023.02.023}, year = {2023}, date = {2023-01-01}, journal = {Information Fusion}, volume = {95}, pages = {163-173}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } |
P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso Streaming big time series forecasting based on nearest similar patterns with application to energy consumption (Journal Article) Logic Journal of the IGPL, 31 (2), pp. 255-270, 2023. (Abstract | Links | BibTeX | Tags: energy, IoT, time series) @article{jimenez2023, title = {Streaming big time series forecasting based on nearest similar patterns with application to energy consumption}, author = {P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso}, url = {https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac017/6534493?redirectedFrom=fulltext}, doi = {https://doi.org/10.1093/jigpal/jzac017}, year = {2023}, date = {2023-01-01}, journal = {Logic Journal of the IGPL}, volume = {31}, number = {2}, pages = {255-270}, abstract = {This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbors algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbors algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbors is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbors model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of 10 minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy.}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbors algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbors algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbors is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbors model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of 10 minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. |
L. Melgar-García, M. Hosseini and A. Troncoso Identification of anomalies in urban sound data with Autoencoders (Conference) HAIS 18th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2023. (BibTeX | Tags: deep learning, IoT, time series) @conference{HAIS23_Laura, title = {Identification of anomalies in urban sound data with Autoencoders}, author = {L. Melgar-García, M. Hosseini and A. Troncoso}, year = {2023}, date = {2023-01-01}, booktitle = {HAIS 18th International Conference on Hybrid Artificial Intelligence Systems}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, IoT, time series}, pubstate = {published}, tppubtype = {conference} } |
2022 |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado Springer, 13469 , 2022, ISBN: 978-3-031-15470-6. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{HAIS2022, title = {Proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2022) Salamanca, Spain, September 5-7, 2022}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-15471-3}, doi = {https://doi.org/10.1007/978-3-031-15471-3}, isbn = {978-3-031-15470-6}, year = {2022}, date = {2022-09-05}, volume = {13469}, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, keywords = {big data, clustering, deep learning, IoT}, pubstate = {published}, tppubtype = {proceedings} } |
P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado Springer, 531 , 2022, ISBN: 978-3-031-18050-7. (Links | BibTeX | Tags: big data, clustering, deep learning, IoT) @proceedings{SOCO2022, title = {Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) Salamanca, Spain, September 5-7, 2022}, author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado}, url = {https://link.springer.com/book/10.1007/978-3-031-18050-7}, doi = {https://doi.org/10.1007/978-3-031-18050-7}, isbn = {978-3-031-18050-7}, year = {2022}, date = {2022-09-05}, volume = {531}, publisher = {Springer}, series = {Lecture Notes in Networks and Systems}, keywords = {big data, clustering, deep learning, IoT}, pubstate = {published}, tppubtype = {proceedings} } |
P. Mugariri and H. Abdullah and M. García-Torres and B.D. Parameshchari and K.N. Abdul-Sattar Promoting Information Privacy Protection Awareness for Internet of Things (IoT) (Journal Article) Mobile Information Systems, 2022 , pp. 1–11, 2022. (Abstract | Links | BibTeX | Tags: IoT) @article{mugariri2022promoting, title = {Promoting Information Privacy Protection Awareness for Internet of Things (IoT)}, author = {P. Mugariri and H. Abdullah and M. García-Torres and B.D. Parameshchari and K.N. Abdul-Sattar}, url = {https://www.hindawi.com/journals/misy/2022/4247651/}, doi = {10.1155/2022/4247651}, year = {2022}, date = {2022-01-01}, journal = {Mobile Information Systems}, volume = {2022}, pages = {1--11}, abstract = {The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.}, keywords = {IoT}, pubstate = {published}, tppubtype = {article} } The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet. |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso Nearest neighbors with incremental learning for real-time forecasting of electricity demand (Conference) IEEE International Conference on Data Mining Workshops, 2022. (Links | BibTeX | Tags: energy, IoT, time series) @conference{MelgarICDM2022, title = {Nearest neighbors with incremental learning for real-time forecasting of electricity demand}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso}, url = {https://ieeexplore.ieee.org/document/10031211}, year = {2022}, date = {2022-01-01}, booktitle = {IEEE International Conference on Data Mining Workshops}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {conference} } |
2021 |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach (Journal Article) Information Sciences, 558 , pp. 174-193, 2021. (Abstract | Links | BibTeX | Tags: big data, IoT, pattern recognition) @article{Melgar21_IS, title = {Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S0020025521000220}, doi = {10.1016/j.ins.2020.12.089}, year = {2021}, date = {2021-01-01}, journal = {Information Sciences}, volume = {558}, pages = {174-193}, abstract = {Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a new triclustering approach for data streams is introduced. It follows a streaming scheme of learning in two steps: offline and online phases. First, the offline phase provides a summary model with the components of the triclusters. Then, the second stage is the online phase to deal with data in streaming. This online phase consists in using the summary model obtained in the offline stage to update the triclusters as fast as possible with genetic operators. Results using three types of synthetic datasets and a real-world environmental sensor dataset are reported. The performance of the proposed triclustering streaming algorithm is compared to a batch triclustering algorithm, showing an accurate performance both in terms of quality and running times. }, keywords = {big data, IoT, pattern recognition}, pubstate = {published}, tppubtype = {article} } Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a new triclustering approach for data streams is introduced. It follows a streaming scheme of learning in two steps: offline and online phases. First, the offline phase provides a summary model with the components of the triclusters. Then, the second stage is the online phase to deal with data in streaming. This online phase consists in using the summary model obtained in the offline stage to update the triclusters as fast as possible with genetic operators. Results using three types of synthetic datasets and a real-world environmental sensor dataset are reported. The performance of the proposed triclustering streaming algorithm is compared to a batch triclustering algorithm, showing an accurate performance both in terms of quality and running times. |
A. R. Troncoso-García and J. A. Ortega and R. Seepold and N. Martínez-Madrid Non-invasive devices for respiratory sound monitoring (Conference) KES International Conference on Knowledge Based and Intelligent information and Engineering Systems, 2021. @conference{TRONCOSO-GARCIA21, title = {Non-invasive devices for respiratory sound monitoring}, author = {A. R. Troncoso-García and J. A. Ortega and R. Seepold and N. Martínez-Madrid}, url = {https://www.sciencedirect.com/science/article/pii/S1877050921018135}, doi = {https://doi.org/10.1016/j.procs.2021.09.076}, year = {2021}, date = {2021-01-01}, booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems}, pages = {3040-3048}, keywords = {IoT}, pubstate = {published}, tppubtype = {conference} } |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso Nearest neighbours-based forecasting for electricity demand time series in streaming (Conference) CAEPIA Conference of the Spanish Association for Artificial Intelligence , Lecture Notes in Artificial Intelligence 2021. (Abstract | BibTeX | Tags: IoT, time series) @conference{CAEPIA21_Laura, title = {Nearest neighbours-based forecasting for electricity demand time series in streaming}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso }, year = {2021}, date = {2021-01-01}, booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence }, series = {Lecture Notes in Artificial Intelligence}, abstract = {This paper presents a forecasting algorithm for time series in streaming. The methodology has two well-differentiated stages: the algorithm searches for the nearest neighbors to generate an initial prediction model in the batch phase. Then, an online phase is carried out when the time series arrives in streaming. In particular, the nearest neighbor of the streaming data from the training set is computed and the nearest neighbors, previously computed in the batch phase, of this nearest neighbor are used to obtain the predictions. Results using the electricity consumption time series are reported, showing a remarkable performance of the proposed algorithm in terms of forecasting errors when compared to a nearest neighbors-based benchmark algorithm. The running times for the predictions are also remarkable.}, keywords = {IoT, time series}, pubstate = {published}, tppubtype = {conference} } This paper presents a forecasting algorithm for time series in streaming. The methodology has two well-differentiated stages: the algorithm searches for the nearest neighbors to generate an initial prediction model in the batch phase. Then, an online phase is carried out when the time series arrives in streaming. In particular, the nearest neighbor of the streaming data from the training set is computed and the nearest neighbors, previously computed in the batch phase, of this nearest neighbor are used to obtain the predictions. Results using the electricity consumption time series are reported, showing a remarkable performance of the proposed algorithm in terms of forecasting errors when compared to a nearest neighbors-based benchmark algorithm. The running times for the predictions are also remarkable. |
2020 |
P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series (Conference) HAIS 15th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2020. (Links | BibTeX | Tags: big data, energy, IoT, time series) @conference{HAIS2020, title = {A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series}, author = {P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso}, url = {https://link.springer.com/chapter/10.1007/978-3-030-61705-9_43}, year = {2020}, date = {2020-11-04}, booktitle = {HAIS 15th International Conference on Hybrid Artificial Intelligence Systems}, pages = {522-533}, series = {Lecture Notes in Computer Science}, keywords = {big data, energy, IoT, time series}, pubstate = {published}, tppubtype = {conference} } |
L. Melgar-García and M. T. Godinho and R. Espada and D. Gutiérrez-Avilés and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering (Conference) SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2020. (Links | BibTeX | Tags: IoT, pattern recognition) @conference{SOCO20, title = {Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering}, author = {L. Melgar-García and M. T. Godinho and R. Espada and D. Gutiérrez-Avilés and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero}, url = {https://link.springer.com/chapter/10.1007/978-3-030-57802-2_22}, year = {2020}, date = {2020-08-29}, booktitle = {SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications}, pages = {226-236}, series = {Advances in Intelligent Systems and Computing }, keywords = {IoT, pattern recognition}, pubstate = {published}, tppubtype = {conference} } |
2019 |
A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez Real-Time Big Data Analytics in Smart Cities from LoRa-based IoT Networks (Conference) SOCO 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2019. (Links | BibTeX | Tags: big data, IoT) @conference{SOCO2019, title = {Real-Time Big Data Analytics in Smart Cities from LoRa-based IoT Networks}, author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-030-20055-8_9}, year = {2019}, date = {2019-01-01}, booktitle = {SOCO 14th International Conference on Soft Computing Models in Industrial and Environmental Applications}, series = {Advances in Intelligent Systems and Computing}, keywords = {big data, IoT}, pubstate = {published}, tppubtype = {conference} } |
2016 |
F. Hassan and N. Iqabl and F. Martínez-Álvarez and K. M. Asim Passivity Based Control of Cyber Physical Systems Under Zero-Dynamics Attack (Conference) HAIS Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science 2016. @conference{Hassan2016, title = {Passivity Based Control of Cyber Physical Systems Under Zero-Dynamics Attack}, author = {F. Hassan and N. Iqabl and F. Martínez-Álvarez and K. M. Asim}, url = {https://link.springer.com/chapter/10.1007/978-3-319-32034-2_53}, year = {2016}, date = {2016-01-01}, booktitle = {HAIS Hybrid Artificial Intelligent Systems}, series = {Lecture Notes in Computer Science}, keywords = {IoT}, pubstate = {published}, tppubtype = {conference} } |