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146 results on '"Della Cioppa, Antonio"'

1. Investigating One-Class Classifiers to Diagnose Alzheimer’s Disease from Handwriting

Author

Parziale, Antonio, Della Cioppa, Antonio, Marcelli, Angelo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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2. Negative Selection Algorithm for Alzheimer’s Diagnosis: Design and Performance Evaluation

Author

De Gregorio, Giuseppe, Della Cioppa, Antonio, Marcelli, Angelo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jiménez Laredo, Juan Luis, editor, Hidalgo, J. Ignacio, editor, and Babaagba, Kehinde Oluwatoyin, editor

Published
2022
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3. Can Different Impacts of Covid-19 on Different Countries Be Explained? a Preliminary Machine Learning Experiment

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Scafuri, Umberto, Tarantino, Ernesto, Chatterjee, Prasenjit, Series Editor, Awasthi, Anjali, Series Editor, Tiwari, Manoj Kumar, Series Editor, Chakraborty, Shankar, Series Editor, Yazdani, Morteza, Series Editor, Kose, Utku, editor, Watada, Junzo, editor, Deperlioglu, Omer, editor, and Marmolejo Saucedo, Jose Antonio, editor

Published
2022
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4. A Decision Tree for Automatic Diagnosis of Parkinson’s Disease from Offline Drawing Samples: Experiments and Findings

Author

Parziale, Antonio, Della Cioppa, Antonio, Senatore, Rosa, Marcelli, Angelo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
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8. Impact of the Topology on the Performance of Distributed Differential Evolution

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Esparcia-Alcázar, Anna I., editor, and Mora, Antonio M., editor

Published
2014
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9. Adding Chaos to Differential Evolution for Range Image Registration

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Esparcia-Alcázar, Anna I., editor

Published
2013
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10. A Model Based on Biological Invasions for Island Evolutionary Algorithms

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hao, Jin-Kao, editor, Legrand, Pierrick, editor, Collet, Pierre, editor, Monmarché, Nicolas, editor, Lutton, Evelyne, editor, and Schoenauer, Marc, editor

Published
2012
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14. Extremal Optimization as a Viable Means for Mapping in Grids

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Giacobini, Mario, editor, Brabazon, Anthony, editor, Cagnoni, Stefano, editor, Di Caro, Gianni A., editor, Ekárt, Anikó, editor, Esparcia-Alcázar, Anna Isabel, editor, Farooq, Muddassar, editor, Fink, Andreas, editor, and Machado, Penousal, editor

Published
2009
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15. A Multiobjective Evolutionary Approach for Multisite Mapping on Grids

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wyrzykowski, Roman, editor, Dongarra, Jack, editor, Karczewski, Konrad, editor, and Wasniewski, Jerzy, editor

Published
2008
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16. Satellite Image Registration by Distributed Differential Evolution

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Giacobini, Mario, editor

Published
2007
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17. Parsimony Doesn’t Mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ebner, Marc, editor, O’Neill, Michael, editor, Ekárt, Anikó, editor, Vanneschi, Leonardo, editor, and Esparcia-Alcázar, Anna Isabel, editor

Published
2007
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18. Multiobjective Differential Evolution for Mapping in a Grid Environment

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Scafuri, Umberto, Tarantino, Ernesto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Perrott, Ronald, editor, Chapman, Barbara M., editor, Subhlok, Jaspal, editor, de Mello, Rodrigo Fernandes, editor, and Yang, Laurence T., editor

Published
2007
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19. A Genetic Programming Approach to Solomonoff’s Probabilistic Induction

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Maisto, Domenico, Tarantino, Ernesto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Collet, Pierre, editor, Tomassini, Marco, editor, Ebner, Marc, editor, Gustafson, Steven, editor, and Ekárt, Anikó, editor

Published
2006
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21. 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
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22. 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
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23. 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

24. 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

28. CTRNN parameter learning using Differential Evolution

Author

De Falco, Ivanoe, Della Cioppa, Antonio, Donnarumma, Francesco, Maisto, Domenico, Prevete, Roberto, Tarantino, Ernesto, Ghallab, M, Spyropoulos, CD, Fakotakis, N, et al, DE FALCO, Ivan, DELLA CIOPPA, Andrea, Donnarumma, Francesco, Maisto, D, Prevete, Roberto, and Tarantino, E.

Subjects
Genetic Algorithms, Dynamical System, Differential Evolution, Ctrnn, Dynamical Systems
Abstract

Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems.

Published
2008

32. Automatic Classification of Handsegmented Image Parts using Differential Evolution

Author

De Falco Ivanoe, Tarantino Ernesto, and Della Cioppa Antonio

Subjects
centroids, classification, multi-class database, recognition of image parts, Differential evolution
Abstract

Differential Evolution, a version of an Evolutionary Algorithm, is used to perform automatic classification of handsegmented image parts collected in a seven–class database. Our idea is to exploit it to find the positions of the class centroids in the search space such that for any class the average distance of instances belonging to that class from the relative class centroid is minimized. The performance of the resulting best individual is computed in terms of error rate on the testing set. Then, such a performance is compared against those of other ten classification techniques well known in literature. Results show the effectiveness of the approach in solving the classification task.

Published
2006

34. Inductive Inference on Noisy Data by Genetic Programming

Author

De Falco Ivanoe, Della Cioppa Antonio, Maisto Domenico, and Tarantino Ernesto

Subjects
Solomonoff's induction theory, Genetic Programming, inductive inference, symbolic regression
Abstract

In this paper a Genetic Programming algorithm based on Solomonoff probabilistic induction concepts is designed and used to face an Inductive Inference task, i.e. symbolic regression. To this aim, Schwefel function is dressed with increasing levels of additive noise and the algorithm is employed to denoise the resulting function and recover the starting one. The proposed algorithm is compared against a classical parsimony-based GP. The earliest results seem to show a superiority of the Solomonoff-based approach.

Published
2006

43. 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

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