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11 results on '"Gabriele La Malfa"'

5. Machine Learning, Optimization, and Data Science : 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II

Author

Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, Renato Umeton, Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, and Renato Umeton

Subjects
Information technology—Management, Computer networks, Electronic digital computers—Evaluation, Computer systems, Artificial intelligence, Machine learning
Abstract

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023. The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state of the art and the latest advances in the integration of machine learning, deep learning, nonlinear optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI.

Published
2024

6. Machine Learning, Optimization, and Data Science : 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18–22, 2022, Revised Selected Papers, Part II

Author

Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Giovanni Giuffrida, Renato Umeton, Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Giovanni Giuffrida, and Renato Umeton

Subjects
Information technology—Management, Computer networks, Electronic digital computers—Evaluation, Computer systems, Artificial intelligence, Machine learning
Abstract

This two-volume set, LNCS 13810 and 13811, constitutes the refereed proceedings of the 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, together with the papers of the Second Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022. The total of 84 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 226 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Published
2023

7. Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I

Author

Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton, Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, and Renato Umeton

Subjects
Application software
Abstract

This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021.The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Published
2022

8. Unsupervised anomaly detection in time series exploiting local and global information

Author

Emanuele La Malfa and Gabriele La Malfa

Subjects
Normal distribution, Error function, Computer science, business.industry, Anomaly (natural sciences), Deep learning, Pattern recognition, Anomaly detection, Artificial intelligence, business, Autoencoder, Ensemble learning, Parametric statistics
Abstract

We introduce a novel machine learning ensemble architecture for anomaly detection, that exploits global and local information from a 1d time series. A double step validation is performed to decide if a time period is anomalous: from one side a Long Short-Term Memory is trained to be reliable at forecasting, hence a parametric test on the forecasting’s error is used spot the anomalies. Concurrently, a Variational Autoencoder is trained to compress both global and local information from the series to a low-dimensional normal distribution, raising an anomaly if a time step’s likelihood is below a threshold. While anomaly detection with deep learning techniques often comes with the assumption that forecasting error is gaussian, we prove that this is in general a wrong assumption: we show that error function is better approximated by a distribution chosen dynamically. We validate our work on some public physical datasets, outperforming the current deep learning methods in terms of precision and recall.We introduce a novel machine learning ensemble architecture for anomaly detection, that exploits global and local information from a 1d time series. A double step validation is performed to decide if a time period is anomalous: from one side a Long Short-Term Memory is trained to be reliable at forecasting, hence a parametric test on the forecasting’s error is used spot the anomalies. Concurrently, a Variational Autoencoder is trained to compress both global and local information from the series to a low-dimensional normal distribution, raising an anomaly if a time step’s likelihood is below a threshold. While anomaly detection with deep learning techniques often comes with the assumption that forecasting error is gaussian, we prove that this is in general a wrong assumption: we show that error function is better approximated by a distribution chosen dynamically. We validate our work on some public physical datasets, outperforming the current deep learning methods in terms of precision and recall.

Published
2019

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