18 results on '"Della Cioppa, Antonio"'
Search Results
2. Investigating surrogate-assisted cooperative coevolution for large-Scale global optimization
- Author
-
De Falco, Ivanoe, Della Cioppa, Antonio, and Trunfio, Giuseppe A.
- Published
- 2019
- Full Text
- View/download PDF
3. Genetic Programming-based induction of a glucose-dynamics model for telemedicine
- Author
-
De Falco, Ivanoe, Della Cioppa, Antonio, Koutny, Tomas, Krcma, Michal, Scafuri, Umberto, and Tarantino, Ernesto
- Published
- 2018
- Full Text
- View/download PDF
4. A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.
- Author
-
De Falco, Ivanoe, Della Cioppa, Antonio, Koutny, Tomas, Ubl, Martin, Krcma, Michal, Scafuri, Umberto, and Tarantino, Ernesto
- Subjects
- *
EVOLUTIONARY algorithms , *DATA privacy , *BIOLOGICALLY inspired computing , *GLUCOSE , *STATISTICS , *MACHINE learning , *STATISTICAL models - Abstract
In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F 1 , and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment.
- Author
-
Ubl, Martin, Koutny, Tomas, Della Cioppa, Antonio, De Falco, Ivanoe, Tarantino, Ernesto, and Scafuri, Umberto
- Subjects
INSULIN pumps ,BLOOD sugar ,INSULIN therapy ,SIMULATED patients ,DIABETES ,DECISION trees - Abstract
Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Proceedings Part II
- Author
-
Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Della Cioppa, Antonio, Divina, Federico, Eiben, A.E., Esparcia-Alcazar, Anna I., De Vega, Francisco Fernandez, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, Zhang, Mengjie, Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Della Cioppa, Antonio, Divina, Federico, Eiben, A.E., Esparcia-Alcazar, Anna I., De Vega, Francisco Fernandez, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, and Zhang, Mengjie
- Abstract
The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
- Published
- 2016
7. Applications of Evolutionary Computation: 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
- Author
-
Mora, Antonio, Squillero, Giovanni, Agapitos, Alexandros, Burelli, Paolo, Bush, William, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Della Cioppa, Antonio, Divina, Federico, Eiben, A.E., Esparcia-Alcazar, Anna I., Fernandez de Vega, Francisco, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, Zhang, Mengjie, Mora, Antonio, Squillero, Giovanni, Agapitos, Alexandros, Burelli, Paolo, Bush, William, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Della Cioppa, Antonio, Divina, Federico, Eiben, A.E., Esparcia-Alcazar, Anna I., Fernandez de Vega, Francisco, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, and Zhang, Mengjie
- Abstract
This book constitutes the refereed conference proceedings of the 18th International Conference on the Applications of Evolutionary Computation, EvoApplications 2015, held in Copenhagen, Spain, in April 2015, colocated with the Evo 2015 events EuroGP, EvoCOP, and EvoMUSART. The 72 revised full papers presented were carefully reviewed and selected from 125 submissions. EvoApplications 2015 consisted of the following 13 tracks: EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary computation in robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
- Published
- 2015
8. Novel initialisation and updating mechanisms in PSO for feature selection in classification
- Author
-
Esparcia-Alcázar, Anna I., Silva, Sara, Sim, Kevin, Cagnoni, Stefano, Agapitos, Alexandros, Simões, Anabela, Urquhart, Neil, Cotta, Carlos, De Falco, Ivanoe, Haasdijk, Evert, Merelo, JJ, Della Cioppa, Antonio, Squillero, Giovanni, de Vega, Francisco Fernández, Tarantino, Ernesto, Diwold, Konrad, Zhang, Mengjie, Ekárt, Anikó, Tettamanzi, Andrea, Eiben, A.E., Burelli, Paolo, Rohlfshagen, Philipp, Schaefer, Robert, Glette, Kyrre, Xue, Bing, Browne, Will N., Esparcia-Alcázar, Anna I., Silva, Sara, Sim, Kevin, Cagnoni, Stefano, Agapitos, Alexandros, Simões, Anabela, Urquhart, Neil, Cotta, Carlos, De Falco, Ivanoe, Haasdijk, Evert, Merelo, JJ, Della Cioppa, Antonio, Squillero, Giovanni, de Vega, Francisco Fernández, Tarantino, Ernesto, Diwold, Konrad, Zhang, Mengjie, Ekárt, Anikó, Tettamanzi, Andrea, Eiben, A.E., Burelli, Paolo, Rohlfshagen, Philipp, Schaefer, Robert, Glette, Kyrre, Xue, Bing, and Browne, Will N.
- Abstract
In classification, feature selection is an important, but difficult problem. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes a new initialisation strategy and a new personal best and global best updating mechanism in PSO to develop a novel feature selection algorithm with the goals of minimising the number of features, maximising the classification performance and simultaneously reducing the computational time. The proposed algorithm is compared with two traditional feature selection methods, a PSO based method with the goal of only maximising the classification performance, and a PSO based two-stage algorithm considering both the number of features and the classification performance. Experiments on eight benchmark datasets show that the proposed algorithm can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. The proposed algorithm achieves significantly better classification performance than the two traditional methods. The proposed algorithm also outperforms the two PSO based feature selection algorithms in terms of the classification performance, the number of features and the computational cost.
- Published
- 2013
9. DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS:: PERFORMANCE COMPARISON.
- Author
-
DJIOUA, MOUSSA, PLAMONDON, RÉJEAN, DELLA CIOPPA, ANTONIO, and MARCELLI, ANGELO
- Subjects
HUMAN mechanics ,NONLINEAR statistical models ,COMPUTER systems ,ARTIFICIAL intelligence ,ALGORITHMS ,DATABASES - Abstract
A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model has been very useful for proposing original solutions to various pattern recognition problems (signature segmentation and verification, handwriting analysis and synthesis, etc.). Most of these applications rely on the use of an efficient algorithm to extract the delta-lognormal parameters from real data with the best possible fit. In this paper, we compare two such algorithms: a deterministic one, based on nonlinear regression, and a Breeder Genetic algorithm. The performance of these two algorithms and of their combinations are compared using the same artificial database, composed of analytical delta-lognormal profiles and their noisy versions (20 dB SNR). In the free-noise case, the analysis of the experimental results shows that the deterministic approach leads to better results than the evolutionary one, while under the extremely noisy conditions selected, the evolutionary approach seems to be less sensitive to noise, but is nevertheless less successful than the deterministic search. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
10. Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem.
- Author
-
Senatore, Rosa, Della Cioppa, Antonio, and Marcelli, Angelo
- Subjects
- *
ARTIFICIAL intelligence , *GENETIC programming , *NEURODEGENERATION - Abstract
Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not clearly state the classification rules adopted. Methods: In this framework we propose a classification method based on a Cartesian Genetic Programming (CGP) approach, which allows for the automatic identification of the presence of the disease, and concurrently, provides the explicit classification model used by the system. Results: The proposed approach has been evaluated on the publicly available HandPD dataset, which contains handwriting samples drawn by Parkinson's disease patients and healthy controls. We show that our approach compares favorably with state-of-the-art methods, and more importantly, allows the physician to identify an explicit model relevant for the diagnosis based on the most informative subset of features. Conclusion: The obtained results suggest that the proposed approach is particularly appealing in that, starting from the explicit model, it allows the physicians to derive a set of guidelines for defining novel testing protocols and intervention strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.