Pubblications

Last Pubblications

1. De Santis, E., Martino, A., & Rizzi, A. (2026). Beyond Perplexity: A Multi-Faceted Analysis of a Novel Densely Connected Transformer. Applied Sciences (Switzerland), 16(6), Article 2721. https://doi.org/10.3390/app16062721

2. Taghdisi Rastkar, S., Jamili, S., De Santis, E., & Rizzi, A. (2026). A Universal Urban Electricity -Demand Simulator for Developing and Evaluating Load-Scheduling and Forecasting Systems. Communications in Computer and Information Science, 2829 CCIS, 570–579. https://doi.org/10.1007/978-3-032-15638-9_33

3. Zendehdel, D., Ferro, G., De Santis, E., & Rizzi, A. (2026). Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework. Communications in Computer and Information Science, 2829 CCIS, 538–557. https://doi.org/10.1007/978-3-032-15638-9_31

4. Borrini, E., De Santis, E., & Rizzi, A. (2025). A Class Incremental Learning Framework for DDoS Detection. 2025 IEEE Symposium on Computational Intelligence in Security, Defence and Biometrics, CISDB 2025. https://doi.org/10.1109/CISDB64969.2025.11010305

5. Capillo, A., De Santis, E., & Rizzi, A. (2025). On a Fast and Explainable REC HEMS Based on Kolmogorov-Arnold Networks. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11228309

6. Capillo, A., De Santis, E., Frattale Mascioli, F. M., & Rizzi, A. (2025). On the Performance of Multi-Objective Evolutionary Algorithms for Energy Management in Microgrids. Studies in Computational Intelligence, 1196 SCI, 3–15. https://doi.org/10.1007/978-3-031-85252-7_1

7. De Santis, E., Ferro, G., & Rizzi, A. (2025). A KAN-SHAP Framework for Fault Detection and Analysis in Smart Grids. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227922

8. De Santis, E., Martino, A., Bruno, E., & Rizzi, A. (2025). 2025: A GPT Odyssey. Deconstructing Intelligence by Gradual Dissolution of a Transformer. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227797

9. De Santis, E., Martino, A., Ronci, F., & Rizzi, A. (2025). From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media. IEEE Transactions on Emerging Topics in Computational Intelligence, 9(1), 1063–1077. https://doi.org/10.1109/TETCI.2024.3423444

10. De Santis, E., Martino, A., Ronci, F., & Rizzi, A. (2025). LSTM in Recursive Feedback Loops: A Study on Textual Evolution and Complexity. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227357

11. Enrico, D. S., Vanessa, P., Massimiliano, L., & Antonello, R. (2025). Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview. Renewable and Sustainable Energy Reviews, 211, Article 115349. https://doi.org/10.1016/j.rser.2025.115349

12. Ferro, G., De Santis, E., Capillo, A., & Rizzi, A. (2025). Decision Focused Forecasting for Smart Grid Energy Management Systems. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227222

13. Rastkar, S. T., Jamili, S., De Santis, E., & Rizzi, A. (2025). Graph-Aug LSTM with Weighted Loss for Enhanced Energy Forecasting. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227264

14. Rastkar, S. T., Zendehdel, D., Capillo, A., De Santis, E., & Rizzi, A. (2025). Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids. Studies in Computational Intelligence, 1196 SCI, 211–223. https://doi.org/10.1007/978-3-031-85252-7_12

15. Verdone, A., Panella, M., De Santis, E., & Rizzi, A. (2025). A review of solar and wind energy forecasting: From single-site to multi-site paradigm. Applied Energy, 392, Article 126016. https://doi.org/10.1016/j.apenergy.2025.126016

16. Zendehdel, D., De Santis, E., Capillo, A., Odonkor, P., & Rizzi, A. (2025). Multi-Objective Battery Dispatching using an Enhanced SAC Algorithm. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN64981.2025.11227829

17. De Santis, E., & Rizzi, A. (2024). Modeling failures in smart grids by a bilinear logistic regression approach. Neural Networks, 174, Article 106245. https://doi.org/10.1016/j.neunet.2024.106245

18. De Santis, E., Martino, A., & Rizzi, A. (2024). Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7), 4812–4829. https://doi.org/10.1109/TPAMI.2024.3358168

19. Grignaffini, F., De Santis, E., Frezza, F., & Rizzi, A. (2024). An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection. Information (Switzerland), 15(12), Article 783. https://doi.org/10.3390/info15120783

20. Rastkar, S. T., Jamili, S., De Santis, E., & Rizzi, A. (2024). Improving Prediction Performances by Integrating Second Derivative in Microgrids Energy Load Forecasting. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN60899.2024.10650507

21. Zendehdel, D., Capillo, A., De Santis, E., & Rizzi, A. (2024). An Extended Battery Equivalent Circuit Model for an Energy Community Real Time EMS. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN60899.2024.10650667

22. De Santis, E., & Rizzi, A. (2023). Prototype Theory Meets Word Embedding: A Novel Approach for Text Categorization via Granular Computing. Cognitive Computation, 15(3), 976–997. https://doi.org/10.1007/s12559-023-10132-9

23. De Santis, E., Capillo, A., Ferrandino, E., Mascioli, F. M. F., & Rizzi, A. (2023). An Information Granulation Approach Through m-Grams for Text Classification. Studies in Computational Intelligence, 1119, 73–89. https://doi.org/10.1007/978-3-031-46221-4_4

24. De Santis, E., Granato, G., & Rizzi, A. (2023). Facing Graph Classification Problems by a Multi-agent Information Granulation Approach. Studies in Computational Intelligence, 1119, 185–204. https://doi.org/10.1007/978-3-031-46221-4_9

25. De Santis, E., Martino, A., Ronci, F., & Rizzi, A. (2023). A Comparison of Neural Word Embedding Language Models for Classifying Social Media Users in the Healthcare Context. Proceedings of the International Joint Conference on Neural Networks, 2023-June. https://doi.org/10.1109/IJCNN54540.2023.10191583

26. De Santis, E., Martino, A., Ronci, F., & Rizzi, A. (2023). An Unsupervised Graph-Based Approach for Detecting Relevant Topics: A Case Study on the Italian Twitter Cohort during the Russia–Ukraine Conflict. Information (Switzerland), 14(6), Article 330. https://doi.org/10.3390/info14060330

27. Ferrandino, E., Capillo, A., De Santis, E., Mascioli, F. M. F., & Rizzi, A. (2023). Improving Simulation Realism in Developing a Fuzzy Modular Autonomous Driving System for Electric Boats. Studies in Computational Intelligence, 1119, 163–184. https://doi.org/10.1007/978-3-031-46221-4_8

28. Marchisio, A., Teodonio, F., Rizzi, A., & Shafique, M. (2023). ISMatch: A real-time hardware accelerator for inexact string matching of DNA sequences on FPGA. Microprocessors and Microsystems, 97, Article 104763. https://doi.org/10.1016/j.micpro.2023.104763

29. Martino, A., De Santis, E., & Rizzi, A. (2023). On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study. SN Computer Science, 4(3), Article 314. https://doi.org/10.1007/s42979-023-01716-1

30. Rastkar, S. T., Zendehdel, D., De Santis, E., & Rizzi, A. (2023). A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids. International Joint Conference on Computational Intelligence, 459–467. https://doi.org/10.5220/0012265900003595

31. Santis, E. D., De Santis, G., & Rizzi, A. (2023). Multifractal Characterization of Texts for Pattern Recognition: On the Complexity of Morphological Structures in Modern and Ancient Languages. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8), 10143–10160. https://doi.org/10.1109/TPAMI.2023.3245886

32. Baldini, L., Martino, A., & Rizzi, A. (2022). A class-specific metric learning approach for graph embedding by information granulation. Applied Soft Computing, 115, Article 108199. https://doi.org/10.1016/j.asoc.2021.108199

33. Baldini, L., Martino, A., & Rizzi, A. (2022). A Multi-objective Optimization Approach for the Synthesis of Granular Computing-Based Classification Systems in the Graph Domain. SN Computer Science, 3(6), Article 436. https://doi.org/10.1007/s42979-022-01260-4

34. Capillo, A., De Santis, E., Mascioli, F. M. F., & Rizzi, A. (2022). Synthesis of an Evolutionary Fuzzy Multi-objective Energy Management System for an Electric Boat. International Joint Conference on Computational Intelligence, 1, 199–208. https://doi.org/10.5220/0011527800003332

35. De Santis, E., Arnò, F., & Rizzi, A. (2022). Estimation of fault probability in medium voltage feeders through calibration techniques in classification models. Soft Computing, 26(15), 7175–7193. https://doi.org/10.1007/s00500-022-07194-6

36. De Santis, E., Martino, A., & Rizzi, A. (2022). On component-wise dissimilarity measures and metric properties in pattern recognition. PeerJ Computer Science, 8, Article e1106. https://doi.org/10.7717/PEERJ-CS.1106

37. De Santis, E., Naraei, P., Martino, A., Sadeghian, A., & Rizzi, A. (2022). Multifractal Characterization and Modeling of Blood Pressure Signals. Algorithms, 15(8), Article 259. https://doi.org/10.3390/a15080259

38. Ferrandino, E., Capillo, A., De Santis, E., Frattale Mascioli, F. M., & Rizzi, A. (2022). A Comparison between Crisp and Fuzzy Logic in an Autonomous Driving System for Boats. IEEE International Conference on Fuzzy Systems, 2022-July. https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882868

39. Granato, G., Martino, A., & Rizzi, A. (2022). A Granular Computing Approach for Multi-Labelled Sequences Classification in IEEE 802.11 Networks. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN55064.2022.9892473

40. Granato, G., Martino, A., Baiocchi, A., & Rizzi, A. (2022). Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis. Applied Sciences (Switzerland), 12(21), Article 11303. https://doi.org/10.3390/app122111303

41. Granato, G., Martino, A., Baldini, L., & Rizzi, A. (2022). Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers: An Extended Analysis. SN Computer Science, 3(4), Article 310. https://doi.org/10.1007/s42979-022-01191-0

42. Martino, A., Baldini, L., & Rizzi, A. (2022). On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study. Algorithms, 15(5), Article 148. https://doi.org/10.3390/a15050148

43. Munir, K., Frezza, F., & Rizzi, A. (2022). Deep Learning Hybrid Techniques for Brain Tumor Segmentation. Sensors, 22(21), Article 8201. https://doi.org/10.3390/s22218201

44. Santis, E. D., Arno, F., Martino, A., & Rizzi, A. (2022). A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN55064.2022.9892880

45. Baldini, L., & Rizzi, A. (2021). A Multi-agent Approach for Graph Classification. International Joint Conference on Computational Intelligence, 1, 334–343. https://doi.org/10.5220/0010677300003063

46. Baldini, L., Martino, A., & Rizzi, A. (2021). Relaxed Dissimilarity-based Symbolic Histogram Variants for Granular Graph Embedding. International Joint Conference on Computational Intelligence, 1, 221–235. https://doi.org/10.5220/0010652500003063

47. Baldini, L., Martino, A., & Rizzi, A. (2021). Towards a Class-Aware Information Granulation for Graph Embedding and Classification. Studies in Computational Intelligence, 922, 263–290. https://doi.org/10.1007/978-3-030-70594-7_11

48. Ferrandino, E., Capillo, A., De Santis, E., Mascioli, F. M. F., & Rizzi, A. (2021). A Modular Autonomous Driving System for Electric Boats based on Fuzzy Controllers and Q-Learning. International Joint Conference on Computational Intelligence, 1, 185–195. https://doi.org/10.5220/0010678100003063

49. Leonori, S., Baldini, L., Rizzi, A., & Mascioli, F. M. F. (2021). A physically inspired equivalent neural network circuit model for soc estimation of electrochemical cells. Energies, 14(21), Article 7386. https://doi.org/10.3390/en14217386

50. Leonori, S., Rizzoni, G., Frattale Mascioli, F. M., & Rizzi, A. (2021). Intelligent energy flow management of a nanogrid fast charging station equipped with second life batteries. International Journal of Electrical Power and Energy Systems, 127, Article 106602. https://doi.org/10.1016/j.ijepes.2020.106602

51. Martino, A., & Rizzi, A. (2021). An enhanced filtering-based information granulation procedure for graph embedding and classification. IEEE Access, 9, 15426–15440. https://doi.org/10.1109/ACCESS.2021.3053085

52. Munir, K., Elahi, H., Farooq, M. U., Ahmed, S., Frezza, F., & Rizzi, A. (2021). Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks. Data Science for COVID-19 Volume 1: Computational Perspectives, 63–73. https://doi.org/10.1016/B978-0-12-824536-1.00039-3

53. Munir, K., Frezza, F., & Rizzi, A. (2021). Brain tumor segmentation using 2d-unet convolutional neural network. Studies in Computational Intelligence, 908, 239–248. https://doi.org/10.1007/978-981-15-6321-8_14

54. Munir, K., Frezza, F., & Rizzi, A. (2021). Deep learning for brain tumor segmentation. Studies in Computational Intelligence, 908, 189–201. https://doi.org/10.1007/978-981-15-6321-8_11

55. Baldini, L., Martino, A., & Rizzi, A. (2020). Complexity vs. Performance in granular embedding spaces for graph classification. IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence, 338–349. https://doi.org/10.5220/0010109503380349

56. Baldini, L., Martino, A., & Rizzi, A. (2020). Exploiting Cliques for Granular Computing-based Graph Classification. Proceedings of the International Joint Conference on Neural Networks, Article 9206690. https://doi.org/10.1109/IJCNN48605.2020.9206690

57. Capillo, A., De Santis, E., Mascioli, F. M. F., & Rizzi, A. (2020). Mining M-Grams by a Granular Computing Approach for Text Classification. International Joint Conference on Computational Intelligence, 1, 350–360. https://doi.org/10.5220/0010109803500360

58. Cinti, A., Bianchi, F. M., Martino, A., & Rizzi, A. (2020). A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation. Cognitive Computation, 12(2), 369–387. https://doi.org/10.1007/s12559-019-09646-y

59. De Santis, E., Capillo, A., Mascioli, F. M. F., & Rizzi, A. (2020). Classification and Calibration Techniques in Predictive Maintenance: A Comparison between GMM and a Custom One-Class Classifier. International Joint Conference on Computational Intelligence, 1, 503–511. https://doi.org/10.5220/0010109905030511

60. De Santis, E., Martino, A., & Rizzi, A. (2020). An Infoveillance System for Detecting and Tracking Relevant Topics from Italian Tweets during the COVID-19 Event. IEEE Access, 8, 132527–132538. https://doi.org/10.1109/ACCESS.2020.3010033

61. Di Noia, A., Martino, A., Montanari, P., & Rizzi, A. (2020). Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction. Soft Computing, 24(6), 4393–4406. https://doi.org/10.1007/s00500-019-04200-2

62. Ferrandino, E., Capillo, A., Mascioli, F. M. F., & Rizzi, A. (2020). Nanogrids: A Smart Way to Integrate Public Transportation Electric Vehicles into Smart Grids. International Joint Conference on Computational Intelligence, 1, 512–520. https://doi.org/10.5220/0010110005120520

63. Giampieri, M., Baldini, L., De Santis, E., & Rizzi, A. (2020). Facing Big Data by an Agent-Based Multimodal Evolutionary Approach to Classification. Proceedings of the International Joint Conference on Neural Networks, Article 9206966. https://doi.org/10.1109/IJCNN48605.2020.9206966

64. Granato, G., Martino, A., Baldini, L., & Rizzi, A. (2020). Intrusion detection in Wi-Fi networks by modular and optimized ensemble of classifiers. IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence, 412–422.

65. Leonori, S., Martino, A., Frattale Mascioli, F. M., & Rizzi, A. (2020). Microgrid Energy Management Systems Design by Computational Intelligence Techniques. Applied Energy, 277, Article 115524. https://doi.org/10.1016/j.apenergy.2020.115524

66. Leonori, S., Martino, A., Luzi, M., Frattale Mascioli, F. M., & Rizzi, A. (2020). A generalized framework for ANFIS synthesis procedures by clustering techniques. Applied Soft Computing Journal, 96, Article 106622. https://doi.org/10.1016/j.asoc.2020.106622

67. Leonori, S., Paschero, M., Frattale Mascioli, F. M., & Rizzi, A. (2020). Optimization strategies for Microgrid energy management systems by Genetic Algorithms. Applied Soft Computing Journal, 86, Article 105903. https://doi.org/10.1016/j.asoc.2019.105903

68. Luzi, M., Frattale Mascioli, F. M., Paschero, M., & Rizzi, A. (2020). A White-Box Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells. IEEE Transactions on Neural Networks and Learning Systems, 31(2), 371–382. https://doi.org/10.1109/TNNLS.2019.2901062

69. Martino, A., & Rizzi, A. (2020). (Hyper)graph kernels over simplicial complexes. Entropy, 22(10), 1–20. https://doi.org/10.3390/e22101155

70. Martino, A., De Santis, E., & Rizzi, A. (2020). An Ecology-based Index for Text Embedding and Classification. Proceedings of the International Joint Conference on Neural Networks, Article 9207299. https://doi.org/10.1109/IJCNN48605.2020.9207299

71. Martino, A., De Santis, E., Giuliani, A., & Rizzi, A. (2020). Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations. Entropy, 22(7), Article 794. https://doi.org/10.3390/e22070794

72. Martino, A., Frattale Mascioli, F. M., & Rizzi, A. (2020). On the Optimization of Embedding Spaces via Information Granulation for Pattern Recognition. Proceedings of the International Joint Conference on Neural Networks, Article 9206830. https://doi.org/10.1109/IJCNN48605.2020.9206830

73. Martino, A., Giuliani, A., Todde, V., Bizzarri, M., & Rizzi, A. (2020). Metabolic networks classification and knowledge discovery by information granulation. Computational Biology and Chemistry, 84, Article 107187. https://doi.org/10.1016/j.compbiolchem.2019.107187

74. Rizzi, A., Granato, G., & Baiocchi, A. (2020). Frame-by-frame Wi-Fi attack detection algorithm with scalable and modular machine-learning design. Applied Soft Computing Journal, 91, Article 106188. https://doi.org/10.1016/j.asoc.2020.106188

75. Baldini, L., Martino, A., & Rizzi, A. (2019). Stochastic information granules extraction for graph embedding and classification. IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence, 391–402. https://doi.org/10.5220/0008149403910402

76. Leonori, S., Martino, A., Mascioli, F. M. F., & Rizzi, A. (2019). ANFIS Microgrid Energy Management System Synthesis by Hyperplane Clustering Supported by Neurofuzzy Min-Max Classifier. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(3), 193–204. https://doi.org/10.1109/TETCI.2018.2880815

77. Leonori, S., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2019). FIS synthesis by clustering for microgrid energy management systems. Smart Innovation, Systems and Technologies, 102, 61–71. https://doi.org/10.1007/978-3-319-95098-3_6

78. Luzi, M., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2019). An improved PSO for flexible parameters identification of lithium cells equivalent circuit models. Smart Innovation, Systems and Technologies, 102, 229–238. https://doi.org/10.1007/978-3-319-95098-3_21

79. Luzi, M., Paschero, M., Rizzi, A., Maiorino, E., & Frattale Mascioli, F. M. (2019). A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells. IEEE Transactions on Neural Networks and Learning Systems, 30(2), 343–354. https://doi.org/10.1109/TNNLS.2018.2827307

80. Martino, A., De Santis, E., Baldini, L., & Rizzi, A. (2019). Calibration techniques for binary classification problems: A comparative analysis. IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence, 487–495. https://doi.org/10.5220/0008165504870495

81. Martino, A., Giampieri, M., Luzi, M., & Rizzi, A. (2019). Data mining by evolving agents for clusters discovery and metric learning. Smart Innovation, Systems and Technologies, 102, 23–35. https://doi.org/10.1007/978-3-319-95098-3_3

82. Martino, A., Giuliani, A., & Rizzi, A. (2019). (Hyper)graph embedding and classification via simplicial complexes. Algorithms, 12(11), Article 223. https://doi.org/10.3390/a12110223

83. Martino, A., Rizzi, A., & Frattale Mascioli, F. M. (2019). Efficient approaches for solving the large-scale k-medoids problem: Towards structured data. Studies in Computational Intelligence, 829, 199–219. https://doi.org/10.1007/978-3-030-16469-0_11

84. Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). Cancer diagnosis using deep learning: A bibliographic review. Cancers, 11(9), Article 1235. https://doi.org/10.3390/cancers11091235

85. Capillo, A., Luzi, M., Pasc, M., Rizzi, A., & Mascioli, F. M. F. (2018). Energy Transduction Optimization of a Wave Energy Converter by Evolutionary Algorithms. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489129. https://doi.org/10.1109/IJCNN.2018.8489129

86. De Santis, E., Martino, A., Rizzi, A., & Mascioli, F. M. F. (2018). Dissimilarity Space Representations and Automatic Feature Selection for Protein Function Prediction. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489115. https://doi.org/10.1109/IJCNN.2018.8489115

87. De Santis, E., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2018). Evolutionary Optimization of an Affine Model for Vulnerability Characterization in Smart Grids. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489749. https://doi.org/10.1109/IJCNN.2018.8489749

88. De Santis, E., Rizzi, A., & Sadeghian, A. (2018). A cluster-based dissimilarity learning approach for localized fault classification in Smart Grids. Swarm and Evolutionary Computation, 39, 267–278. https://doi.org/10.1016/j.swevo.2017.10.007

89. Giampieri, M., & Rizzi, A. (2018). An evolutionary agents based system for data mining and local metric learning. Proceedings of the IEEE International Conference on Industrial Technology, 2018-February, 1461–1466. https://doi.org/10.1109/ICIT.2018.8352396

90. Giampieri, M., De Santis, E., Rizzi, A., & Mascioli, F. M. F. (2018). A Supervised Classification System based on Evolutive Multi-Agent Clustering for Smart Grids Faults Prediction. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489145. https://doi.org/10.1109/IJCNN.2018.8489145

91. Leonori, S., Rizzi, A., Paschero, M., & Mascioli, F. M. F. (2018). Microgrid Energy Management by ANFIS Supported by an ESN Based Prediction Algorithm. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489018. https://doi.org/10.1109/IJCNN.2018.8489018

92. Luzi, M., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2018). A Binary PSO Approach for Real Time Optimal Balancing of Electrochemical Cells. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489699. https://doi.org/10.1109/IJCNN.2018.8489699

93. Luzi, M., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2018). An ANFIS Based System Identification Procedure for Modeling Electrochemical Cells. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489250. https://doi.org/10.1109/IJCNN.2018.8489250

94. Martino, A., Giuliani, A., & Rizzi, A. (2018). Granular computing techniques for bioinformatics pattern recognition problems in non-metric spaces. Studies in Computational Intelligence, 777, 53–81. https://doi.org/10.1007/978-3-319-89629-8_3

95. Martino, A., Rizzi, A., & Mascioli, F. M. F. (2018). Distance Matrix Pre-Caching and Distributed Computation of Internal Validation Indices in k-medoids Clustering. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489101. https://doi.org/10.1109/IJCNN.2018.8489101

96. Martino, A., Rizzi, A., & Mascioli, F. M. F. (2018). Supervised Approaches for Protein Function Prediction by Topological Data Analysis. Proceedings of the International Joint Conference on Neural Networks, 2018-July, Article 8489307. https://doi.org/10.1109/IJCNN.2018.8489307

97. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Conclusions. SpringerBriefs in Computer Science, 0(9783319703374), 71–72. https://doi.org/10.1007/978-3-319-70338-1_8

98. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Experiments. SpringerBriefs in Computer Science, 0(9783319703374), 57–69. https://doi.org/10.1007/978-3-319-70338-1_7

99. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Introduction. SpringerBriefs in Computer Science, 0(9783319703374), 1–7. https://doi.org/10.1007/978-3-319-70338-1_1

100. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Other recurrent neural networks models. SpringerBriefs in Computer Science, 0(9783319703374), 31–39. https://doi.org/10.1007/978-3-319-70338-1_4

101. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Properties and training in recurrent neural networks. SpringerBriefs in Computer Science, 0(9783319703374), 9–21. https://doi.org/10.1007/978-3-319-70338-1_2

102. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Real-world load time series. SpringerBriefs in Computer Science, 0(9783319703374), 45–55. https://doi.org/10.1007/978-3-319-70338-1_6

103. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Recurrent neural network architectures. SpringerBriefs in Computer Science, 0(9783319703374), 23–29. https://doi.org/10.1007/978-3-319-70338-1_3

104. Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). Synthetic time series. SpringerBriefs in Computer Science, 0(9783319703374), 41–43. https://doi.org/10.1007/978-3-319-70338-1_5

105. Bianchi, F. M., Maiorino, E., Livi, L., Rizzi, A., & Sadeghian, A. (2017). An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery. Soft Computing, 21(5), 1347–1369. https://doi.org/10.1007/s00500-015-1876-1

106. De Santis, E., Rizzi, A., & Sadeghian, A. (2017). A learning intelligent System for classification and characterization of localized faults in Smart Grids. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 2669–2676. https://doi.org/10.1109/CEC.2017.7969631

107. De Santis, E., Rizzi, A., & Sadeghian, A. (2017). Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids. Applied Soft Computing Journal, 60, 135–149. https://doi.org/10.1016/j.asoc.2017.05.059

108. De Santis, E., Sadeghian, A., & Rizzi, A. (2017). A Smoothing Technique for the Multifractal Analysis of a Medium Voltage Feeders Electric Current. International Journal of Bifurcation and Chaos, 27(14), Article 1750211. https://doi.org/10.1142/S021812741750211X

109. Leonori, S., Martino, A., Rizzi, A., & Mascioli, F. M. F. (2017). ANFIS synthesis by clustering for microgrids EMS design. IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence, 328–337. https://doi.org/10.5220/0006514903280337

110. Leonori, S., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2017). An optimized microgrid energy management system based on FIS-MO-GA paradigm. IEEE International Conference on Fuzzy Systems, Article 8015438. https://doi.org/10.1109/FUZZ-IEEE.2017.8015438

111. Maiorino, E., Bianchi, F. M., Livi, L., Rizzi, A., & Sadeghian, A. (2017). Data-driven detrending of nonstationary fractal time series with echo state networks. Information Sciences, 382-383, 359–373. https://doi.org/10.1016/j.ins.2016.12.015

112. Maiorino, E., Rizzi, A., Sadeghian, A., & Giuliani, A. (2017). Spectral reconstruction of protein contact networks. Physica A: Statistical Mechanics and its Applications, 471, 804–817. https://doi.org/10.1016/j.physa.2016.12.046

113. Martino, A., Maiorino, E., Giuliani, A., Giampieri, M., & Rizzi, A. (2017). Supervised approaches for function prediction of proteins contact networks from topological structure information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10269 LNCS, 285–296. https://doi.org/10.1007/978-3-319-59126-1_24

114. Martino, A., Rizzi, A., & Mascioli, F. M. F. (2017). Efficient approaches for solving the large-scale k-medoids problem. IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence, 338–347. https://doi.org/10.5220/0006515003380347

115. Paschero, M., Pinto, R., Marchionne, E., Rizzi, A., & Mascioli, F. M. F. (2017). Design and validation of a contactless charging system for electric bicycles. RTSI 2017 - IEEE 3rd International Forum on Research and Technologies for Society and Industry, Conference Proceedings, Article 8065893. https://doi.org/10.1109/RTSI.2017.8065893

116. Rizzi, A., Antonelli, M., & Luzi, M. (2017). Instrument learning and sparse NMD for automatic polyphonic music transcription. IEEE Transactions on Multimedia, 19(7), 1405–1415. https://doi.org/10.1109/TMM.2017.2674603

117. Bianchi, F. M., Livi, L., & Rizzi, A. (2016). Two density-based k-means initialization algorithms for non-metric data clustering. Pattern Analysis and Applications, 19(3), 745–763. https://doi.org/10.1007/s10044-014-0440-4

118. Bianchi, F. M., Rizzi, A., Sadeghian, A., & Moiso, C. (2016). Identifying user habits through data mining on call data records. Engineering Applications of Artificial Intelligence, 54, 49–61. https://doi.org/10.1016/j.engappai.2016.05.007

119. Bianchi, F. M., Scardapane, S., Rizzi, A., Uncini, A., & Sadeghian, A. (2016). Granular Computing Techniques for Classification and Semantic Characterization of Structured Data. Cognitive Computation, 8(3), 442–461. https://doi.org/10.1007/s12559-015-9369-1

120. De Santis, E., Mascioli, F. M. F., Sadeghian, A., & Rizzi, A. (2016). A dissimilarity learning approach by evolutionary computation for faults recognition in smart grids. Studies in Computational Intelligence, 620, 113–130. https://doi.org/10.1007/978-3-319-26393-9_8

121. Di Noia, A., Montanari, P., & Rizzi, A. (2016). Occupational diseases risk prediction by genetic optimization: Towards a non-exclusive classification approach. Studies in Computational Intelligence, 620, 63–77. https://doi.org/10.1007/978-3-319-26393-9_5

122. Leonori, S., De Santis, E., Rizzi, A., & Mascioli, F. M. F. (2016). Multi objective optimization of a fuzzy logic controller for energy management in microgrids. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 319–326. https://doi.org/10.1109/CEC.2016.7743811

123. Leonori, S., De Santis, E., Rizzi, A., & Mascioli, F. M. F. (2016). Optimization of a microgrid energy management system based on a Fuzzy Logic Controller. IECON Proceedings (Industrial Electronics Conference), 6615–6620. https://doi.org/10.1109/IECON.2016.7793965

124. Lisena, V., Paschero, M., Gentile, V., Amicucci, P., Rizzi, A., & Mascioli, F. M. F. (2016). A new method to restore the water quality level through the use of electric boats. IEEE 2nd International Smart Cities Conference: Improving the Citizens Quality of Life, ISC2 2016 - Proceedings, Article 7580870. https://doi.org/10.1109/ISC2.2016.7580870

125. Livi, L., Giuliani, A., & Rizzi, A. (2016). Toward a multilevel representation of protein molecules: Comparative approaches to the aggregation/folding propensity problem. Information Sciences, 326, 134–145. https://doi.org/10.1016/j.ins.2015.07.043

126. Livi, L., Maiorino, E., Giuliani, A., Rizzi, A., & Sadeghian, A. (2016). A generative model for protein contact networks. Journal of Biomolecular Structure and Dynamics, 34(7), 1441–1454. https://doi.org/10.1080/07391102.2015.1077736

127. Livi, L., Maiorino, E., Pinna, A., Sadeghian, A., Rizzi, A., & Giuliani, A. (2016). Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins. Physica A: Statistical Mechanics and its Applications, 441, 199–214. https://doi.org/10.1016/j.physa.2015.08.059

128. Livi, L., Maiorino, E., Rizzi, A., & Sadeghian, A. (2016). On the Long-Term Correlations and Multifractal Properties of Electric Arc Furnace Time Series. International Journal of Bifurcation and Chaos, 26(1), Article 1650007. https://doi.org/10.1142/S0218127416500073

129. Livi, L., Tahayori, H., Rizzi, A., Sadeghian, A., & Pedrycz, W. (2016). Classification of type-2 fuzzy sets represented as sequences of vertical slices. IEEE Transactions on Fuzzy Systems, 24(5), 1022–1034. https://doi.org/10.1109/TFUZZ.2015.2500274

130. Luzi, M., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2016). A PSO algorithm for transient dynamic modeling of lithium cells through a nonlinear RC filter. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 279–286. https://doi.org/10.1109/CEC.2016.7743806

131. Luzi, M., Paschero, M., Rossini, A., Rizzi, A., & Mascioli, F. M. F. (2016). Comparison between two nonlinear Kalman Filters for reliable SoC estimation on a prototypal BMS. IECON Proceedings (Industrial Electronics Conference), 5501–5506. https://doi.org/10.1109/IECON.2016.7794054

132. Maiorino, E., Possemato, F., Modugno, V., & Rizzi, A. (2016). Noise sensitivity of an information granules filtering procedure by genetic optimization for inexact sequential pattern mining. Studies in Computational Intelligence, 620, 131–150. https://doi.org/10.1007/978-3-319-26393-9_9

133. Paschero, M., Storti, G. L., Rizzi, A., Mascioli, F. M. F., & Rizzoni, G. (2016). A novel mechanical analogy-based battery model for SoC estimation using a multicell EKF. IEEE Transactions on Sustainable Energy, 7(4), 1695–1702. https://doi.org/10.1109/TSTE.2016.2574755

134. Possemato, F., Paschero, M., Livi, L., Rizzi, A., & Sadeghian, A. (2016). On the impact of topological properties of smart grids in power losses optimization problems. International Journal of Electrical Power and Energy Systems, 78, 755–764. https://doi.org/10.1016/j.ijepes.2015.12.022

135. Bianchi, F. M., De Santis, E., Rizzi, A., & Sadeghian, A. (2015). Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition. IEEE Access, 3, 1931–1943. https://doi.org/10.1109/ACCESS.2015.2485943

136. Bianchi, F. M., Scardapane, S., Uncini, A., Rizzi, A., & Sadeghian, A. (2015). Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks, 71, 204–213. https://doi.org/10.1016/j.neunet.2015.08.010

137. De Santis, E., Livi, L., Sadeghian, A., & Rizzi, A. (2015). Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification. Neurocomputing, 170, 368–383. https://doi.org/10.1016/j.neucom.2015.05.112

138. De Santis, E., Rizzi, A., Sadeghian, A., & Mascioli, F. M. F. (2015). A learning intelligent system for fault detection in Smart Grid by a One-Class Classification approach. Proceedings of the International Joint Conference on Neural Networks, 2015-September, Article 7280756. https://doi.org/10.1109/IJCNN.2015.7280756

139. Livi, L., & Rizzi, A. (2015). Modeling the uncertainty of a set of graphs using higher-order fuzzy sets. Frontiers of Higher Order Fuzzy Sets, 131–146. https://doi.org/10.1007/978-1-4614-3442-9_7

140. Livi, L., Rizzi, A., & Sadeghian, A. (2015). Classifying sequences by the optimized dissimilarity space embedding approach: A case study on the solubility analysis of the E. coli proteome. Journal of Intelligent and Fuzzy Systems, 28(6), 2725–2733. https://doi.org/10.3233/IFS-151550

141. Livi, L., Rizzi, A., & Sadeghian, A. (2015). Granular modeling and computing approaches for intelligent analysis of non-geometric data. Applied Soft Computing, 27, 567–574. https://doi.org/10.1016/j.asoc.2014.08.072

142. Maiorino, E., Livi, L., Giuliani, A., Sadeghian, A., & Rizzi, A. (2015). Multifractal characterization of protein contact networks. Physica A: Statistical Mechanics and its Applications, 428, 302–313. https://doi.org/10.1016/j.physa.2015.02.026

143. Moharrer, M., Tahayori, H., Livi, L., Sadeghian, A., & Rizzi, A. (2015). Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction. Soft Computing, 19(1), 237–250. https://doi.org/10.1007/s00500-014-1246-4

144. Rizzi, A., Iacovazzi, A., Baiocchi, A., & Colabrese, S. (2015). A low complexity real-time Internet traffic flows neuro-fuzzy classifier. Computer Networks, 91, 752–771. https://doi.org/10.1016/j.comnet.2015.09.011

145. Storti, G. L., Paschero, M., Rizzi, A., & Frattale Mascioli, F. M. (2015). Comparison between time-constrained and time-unconstrained optimization for power losses minimization in Smart Grids using genetic algorithms. Neurocomputing, 170, 353–367. https://doi.org/10.1016/j.neucom.2015.02.088

146. Tahayori, H., Livi, L., Sadeghian, A., & Rizzi, A. (2015). Interval Type-2 Fuzzy Set Reconstruction Based on Fuzzy Information-Theoretic Kernels. IEEE Transactions on Fuzzy Systems, 23(4), 1014–1029. https://doi.org/10.1109/TFUZZ.2014.2336673

147. Bianchi, F. M., Livi, L., Rizzi, A., & Sadeghian, A. (2014). A Granular Computing approach to the design of optimized graph classification systems. Soft Computing, 18(2), 393–412. https://doi.org/10.1007/s00500-013-1065-z

148. Bianchi, F. M., Scardapane, S., Livi, L., Uncini, A., & Rizzi, A. (2014). An interpretable graph-based image classifier. Proceedings of the International Joint Conference on Neural Networks, 2339–2346. https://doi.org/10.1109/IJCNN.2014.6889601

149. De Santis, E., Distante, G., Mascioli, F. M. F., Sadeghian, A., & Rizzi, A. (2014). Evolutionary Optimization of a One-Class Classification System for Faults Recognition in Smart Grids. International Joint Conference on Computational Intelligence, 1, 95–103. https://doi.org/10.5220/0005124800950103

150. De Santis, E., Livi, L., Mascioli, F. M. F., Sadeghian, A., & Rizzi, A. (2014). Fault recognition in smart grids by a one-class classification approach. Proceedings of the International Joint Conference on Neural Networks, 1949–1956. https://doi.org/10.1109/IJCNN.2014.6889668

151. Di Noia, A., Montanari, P., & Rizzi, A. (2014). Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization. International Joint Conference on Computational Intelligence, 1, 68–75. https://doi.org/10.5220/0005077800680075

152. Livi, L., Rizzi, A., & Sadeghian, A. (2014). Optimized dissimilarity space embedding for labeled graphs. Information Sciences, 266, 47–64. https://doi.org/10.1016/j.ins.2014.01.005

153. Livi, L., Tahayori, H., Sadeghian, A., & Rizzi, A. (2014). Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions. Applied Soft Computing Journal, 17, 79–89. https://doi.org/10.1016/j.asoc.2013.12.020

154. Maiorino, E., Possemato, F., Modugno, V., & Rizzi, A. (2014). Information Granules Filtering for Inexact Sequential Pattern Mining by Evolutionary Computation. International Joint Conference on Computational Intelligence, 1, 104–111. https://doi.org/10.5220/0005124901040111

155. Modugno, V., Possemato, F., & Rizzi, A. (2014). Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification. International Joint Conference on Computational Intelligence, 1, 281–288. https://doi.org/10.5220/0005127402810288

156. Rizzi, A., Possemato, F., Caschera, S., Paschero, M., & Frattale Mascioli, F. M. (2014). An Ordering Procedure for Admissible Network Configurations to Regularize DFR Optimization Problems in Smart Grids. International Joint Conference on Computational Intelligence, 1, 273–280. https://doi.org/10.5220/0005127302730280

157. Storti, G. L., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2014). A Radial configurations search algorithm for joint PFC and DFR optimization in Smart Grids. IEEE International Symposium on Industrial Electronics, 944–949. https://doi.org/10.1109/ISIE.2014.6864739

158. Bianchi, F. M., Livi, L., & Rizzi, A. (2013). Matching of time-varying labeled graphs. Proceedings of the International Joint Conference on Neural Networks, Article 6706939. https://doi.org/10.1109/IJCNN.2013.6706939

159. Cinti, A., & Rizzi, A. (2013). FPGA targeted implementation of a neurofuzzy system for real time TCP/IP traffic classification. 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings, 312–317. https://doi.org/10.1109/ICACI.2013.6748522

160. Cinti, A., & Rizzi, A. (2013). Graph coverage: An FPGA-targeted implementation. Conference Proceedings - 9th Conference on Ph. D. Research in Microelectronics and Electronics, PRIME 2013, 129–132. https://doi.org/10.1109/PRIME.2013.6603103

161. De Santis, E., Rizzi, A., Sadeghiany, A., & Mascioli, F. M. F. (2013). Genetic optimization of a fuzzy control system for energy flow management in micro-grids. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 418–423. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608437

162. Livi, L., & Rizzi, A. (2013). Graph ambiguity. Fuzzy Sets and Systems, 221, 24–47. https://doi.org/10.1016/j.fss.2013.01.001

163. Livi, L., & Rizzi, A. (2013). The graph matching problem. Pattern Analysis and Applications, 16(3), 253–283. https://doi.org/10.1007/s10044-012-0284-8

164. Livi, L., Bianchi, F. M., Rizzi, A., & Sadeghian, A. (2013). Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure. Proceedings of the International Joint Conference on Neural Networks, Article 6706937. https://doi.org/10.1109/IJCNN.2013.6706937

165. Livi, L., Tahayori, H., Sadeghian, A., & Rizzi, A. (2013). Aggregating α-planes for Type-2 fuzzy set matching. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 860–865. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608513

166. Possemato, F., & Rizzi, A. (2013). Automatic text categorization by a Granular Computing approach: Facing unbalanced data sets. Proceedings of the International Joint Conference on Neural Networks, Article 6707082. https://doi.org/10.1109/IJCNN.2013.6707082

167. Possemato, F., Storti, G. L., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2013). Two evolutionary computational approaches for active power losses minimization in Smart Grids. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 401–406. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608434

168. Rizzi, A., Colabrese, S., & Baiocchi, A. (2013). Low complexity, high performance neuro-fuzzy system for Internet traffic flows early classification. 2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013, 77–82. https://doi.org/10.1109/IWCMC.2013.6583538

169. Rizzi, A., Livi, L., Tahayori, H., & Sadeghian, A. (2013). Matching general type-2 fuzzy sets by comparing the vertical slices. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 866–871. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608514

170. Rizzi, A., Possemato, F., Livi, L., Sebastiani, A., Giuliani, A., & Mascioli, F. M. F. (2013). A dissimilarity-based classifier for generalized sequences by a granular computing approach. Proceedings of the International Joint Conference on Neural Networks, Article 6707041. https://doi.org/10.1109/IJCNN.2013.6707041

171. Storti, G. L., Possemato, F., Paschero, M., Alessandroni, S., Rizzi, A., & Mascioli, F. M. F. (2013). Active Power Losses Constrained Optimization in Smart Grids by Genetic Algorithms. Smart Innovation, Systems and Technologies, 19, 279–288. https://doi.org/10.1007/978-3-642-35467-0_28

172. Storti, G. L., Possemato, F., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2013). Optimal distribution feeders configuration for active power losses minimization by genetic algorithms. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 407–412. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608435

173. Del Vescovo, G., Paschero, M., Rizzi, A., & Mascioli, F. M. F. (2012). An open software system for signal routing and processing in hybrid vehicles. IEEE International Symposium on Industrial Electronics, 1702–1707. https://doi.org/10.1109/ISIE.2012.6237347

174. Fabbri, G., Paschero, M., Del Vescovo, G., Chiacchiarini, H., Rizzi, A., & Mascioli, F. M. F. (2012). A simulation tool for the management of energy flows in Hybrid-Electric Vehicles. IEEE International Symposium on Industrial Electronics, 1696–1701. https://doi.org/10.1109/ISIE.2012.6237346

175. Livi, L., & Rizzi, A. (2012). Parallel algorithms for tensor product-based inexact graph matching. Proceedings of the International Joint Conference on Neural Networks, Article 6252681. https://doi.org/10.1109/IJCNN.2012.6252681

176. Livi, L., Del Vescovo, G., & Rizzi, A. (2012). Graph recognition by seriation and frequent substructures mining. ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 1, 186–191.

177. Livi, L., Del Vescovo, G., & Rizzi, A. (2012). Inexact graph matching through graph coverage. ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 1, 269–272.

178. Paschero, M., Del Vescovo, G., Benucci, L., Rizzi, A., Santello, M., Fabbri, G., & Mascioli, F. M. F. (2012). A real time classifier for emotion and stress recognition in a vehicle driver. IEEE International Symposium on Industrial Electronics, 1690–1695. https://doi.org/10.1109/ISIE.2012.6237345

179. Rizzi, A., Del Vescovo, G., Livi, L., & Mascioli, F. M. F. (2012). A new Granular Computing approach for sequences representation and classification. Proceedings of the International Joint Conference on Neural Networks, Article 6252680. https://doi.org/10.1109/IJCNN.2012.6252680

180. Cinti, A., & Rizzi, A. (2011). NEUROFUZZY MIN-MAX NETWORKS IMPLEMENTATION ON FPGA. International Joint Conference on Computational Intelligence, 1, 51–57. https://doi.org/10.5220/0003680700510057

181. Antonelli, M., Rizzi, A., & Del Vescovo, G. (2010). A query by humming system for music information retrieval. Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10, 586–591. https://doi.org/10.1109/ISDA.2010.5687200

182. Del Vescovo, G., Paschero, M., Rizzi, A., Di Salvo, R., & Frattale Mascioli, F. M. (2010). Multi-fault diagnosis of rolling-element bearings in electric machines. 19th International Conference on Electrical Machines, ICEM 2010, Article 5608123. https://doi.org/10.1109/ICELMACH.2010.5608123

183. Maiolini, G., Baiocchi, A., Rizzi, A., & Di Lollo, C. (2010). Statistical classification of services tunneled into SSH connections by a k-means based learning algorithm. IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, 742–746. https://doi.org/10.1145/1815396.1815567

184. Paschero, M., Di Giacomo, V., Del Vescovo, G., Rizzi, A., & Frattale Mascioli, F. M. (2010). Estimation of lithium polymer cell characteristic parameters through genetic algorithms. 19th International Conference on Electrical Machines, ICEM 2010, Article 5608060. https://doi.org/10.1109/ICELMACH.2010.5608060

185. Baiocchi, A., Maiolini, G., Molina, G., & Rizzi, A. (2009). On-the-fly statistical classification of internet traffic at application layer based on cluster analysis. Advances in Soft Computing, 53, 178–185. https://doi.org/10.1007/978-3-540-88181-0_23

186. Maiolini, G., Baiocchi, A., Iacovazzi, A., & Rizzi, A. (2009). Real time identification of SSH encrypted application flows by using cluster analysis techniques. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5550 LNCS, 182–194. https://doi.org/10.1007/978-3-642-01399-7_15

187. Maiolini, G., Baiocchi, A., Rizzi, A., Ferri, S., & Gabbrielli, L. (2009). On the fly encoded application flows recognition by relying on statistical features of IP traffic. CEUR Workshop Proceedings, 582, 89–96.

188. Paschero, M., Del Vescovo, G., Rizzi, A., & Frattale Mascioli, F. M. (2009). An embedded computer based system for monitoring, diagnostics and communication in hybrid and electric vehicles. 24th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition 2009, EVS 24, 3, 1869–1876.

189. Rizzi, A., Frattale Mascioli, F. M., Baldini, F., Mazzetti, C., & Bartnikas, R. (2009). Genetic optimization of a PD diagnostic system for cable accessories. IEEE Transactions on Power Delivery, 24(3), 1728–1738. https://doi.org/10.1109/TPWRD.2009.2016826

190. Antonelli, M., & Rizzi, A. (2008). A correntropy-based voice to MIDI transcription algorithm. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 978–983. https://doi.org/10.1109/MMSP.2008.4665216

191. Rizzi, A., Buccino, N. M., Panella, M., & Uncini, A. (2008). Genre classification of compressed audio data. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 654–659. https://doi.org/10.1109/MMSP.2008.4665157

192. Antonelli, M., & Rizzi, A. (2007). A non-monotone optimization algorithm for IIR filter design. Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP, 372–377. https://doi.org/10.1109/MLSP.2007.4414335

193. Del Vescovo, G., & Rizzi, A. (2007). Automatic classification of graphs by symbolic histograms. Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007, 410–416. https://doi.org/10.1109/GRC.2007.4403133

194. Del Vescovo, G., & Rizzi, A. (2007). Online handwriting recognition by the symbolic histograms approach. Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007, 686–690. https://doi.org/10.1109/GRC.2007.4403187

195. Mascioli, F. M. F., Rizzi, A., Panella, M., & Bettiol, C. (2007). Optimization of hybrid electric cars by neuro-fuzzy networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4578 LNAI, 253–260.

196. Panella, M., & Rizzi, A. (2006). Baseband filter banks for neural prediction. CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ..., Article 4052836. https://doi.org/10.1109/CIMCA.2006.57

197. Panella, M., Rizzi, A., Mascioli, F. M. F., & Martinelli, G. (2006). A neuro-fuzzy system for the prediction of the vehicle traffic flow. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2955 LNAI, 110–118. https://doi.org/10.1007/10983652_15

198. Rizzi, A., & Del Vescovo, G. (2006). Automatic image classification by a granular computing approach. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006, 33–38. https://doi.org/10.1109/MLSP.2006.275517

199. Rizzi, A., Buccino, M., Panella, M., & Uncini, A. (2006). Optimal short-time features for music/speech classification of compressed audio data. CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ..., Article 4052826. https://doi.org/10.1109/CIMCA.2006.160

200. Rizzi, A., & Del Vescovo, G. (2005). A symbolic approach to the solution of F-classification problems. Proceedings of the International Joint Conference on Neural Networks, 3, 1953–1958. https://doi.org/10.1109/IJCNN.2005.1556179

201. Frattale Mascioli, F. M., Panella, M., & Rizzi, A. (2004). A neural prediction of multi-sensor systems. Soft Computing with Industrial Applications - Proceedings of the Sixth Biannual World Automation Congress, 1–6.

202. Panella, M., Rizzi, A., Mascioli, F. M. F., & Martinelli, G. (2004). From circuits to neurofuzzy networks: Synthesis by numerical and linguistic information. Journal of Circuits, Systems and Computers, 13(1), 205–236. https://doi.org/10.1142/S0218126604001258

203. Rizzi, A., Panella, M., Paschero, M., & Mascioli, F. M. F. (2004). Estimation of bone mineral density data using MoG neural networks. IEEE International Conference on Neural Networks - Conference Proceedings, 4, 3241–3246. https://doi.org/10.1109/IJCNN.2004.1381198

204. Costantini, G., Rizzi, A., & Casali, D. (2003). Recognition of musical instruments by generalized min - Max classifiers. Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, 2003-January, 555–564. https://doi.org/10.1109/NNSP.2003.1318055

205. Panella, M., Frattale Mascioli, F. M., Rizzi, A., & Martinelli, G. (2003). ANFIS synthesis by hyperplane clustering for time series prediction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2859, 77–84. https://doi.org/10.1007/978-3-540-45216-4_8

206. Panella, M., Rizzi, A., & Martinelli, G. (2003). Refining accuracy of environmental data prediction by MoG neural networks. Neurocomputing, 55(3-4), 521–549. https://doi.org/10.1016/S0925-2312(03)00392-8

207. Martinelli, G., Frattale Mascioli, F. M., Panella, M., & Rizzi, A. (2002). Extended random neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2486 LNCS, 75–82. https://doi.org/10.1007/3-540-45808-5_7

208. Panella, M., Rizzi, A., Frattale Mascioli, F. M., & Martinelli, G. (2002). Constructive MoG neural networks for pollution data forecasting. Proceedings of the International Joint Conference on Neural Networks, 1, 417–422.

209. Rizzi, A., Panella, M., & Mascioli, F. M. F. (2002). Adaptive resolution min-max classifiers. IEEE Transactions on Neural Networks, 13(2), 402–414. https://doi.org/10.1109/72.991426

210. Rizzi, A., Panella, M., Frattale Mascioli, F. M., & Martinelli, G. (2002). Automatic feature selection for adaptive resolution classifiers. IEEE International Conference on Fuzzy Systems, 1, 384–389.

211. Panella, M., Rizzi, A., Frattale Mascioli, F. M., & Martinelli, G. (2001). A constructive EM approach to density estimation for learning. Proceedings of the International Joint Conference on Neural Networks, 4, 2608–2613.

212. Panella, M., Rizzi, A., Frattale, F. M., & Martinelli, M. G. (2001). ANFIS synthesis by hyperplane clustering. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 1, 340–345.

213. Rizzi, A., Panella, M., Frattale Mascioli, F. M., & Martinelli, G. (2001). Automatic training of generalized Min-Max classifiers. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 5, 3070–3075.

214. Mascioli, F. M. F., Panella, M., Rizzi, A., & Martinelli, G. (2000). Scale-based clustering with latent variables. European Signal Processing Conference, 2015-March(March), Article 7075635.

215. Mascioli, F. M. F., Rizzi, A., Panella, M., & Martinelli, G. (2000). Scale-based approach to hierarchical fuzzy clustering. Signal Processing, 80(6), 1001–1016. https://doi.org/10.1016/S0165-1684(00)00016-5

216. Rizzi, A., Frattale Mascioli, F. M., & Martinelli, G. (2000). Generalized Min-Max classifier. IEEE International Conference on Fuzzy Systems, 1, 36–41. https://doi.org/10.1109/FUZZY.2000.838630

217. Rizzi, A., Panella, M., Mascioli, F. M. F., & Martinelli, G. (2000). A Recursive Algorithm for Fuzzy Min-Max Networks. Proceedings of the International Joint Conference on Neural Networks, 6, 541–546.

218. Mancini, A., Frattale Mascioli, F. M., Rizzi, A., & Martinelli, G. (1999). Improving FBF neurofuzzy approximates by optimised input space covering. Electronics Letters, 35(4), 312–313. https://doi.org/10.1049/el:19990149

219. Mascioli, F. M. F., Rizzi, A., Panella, M., & Martinelli, G. (1999). Clustering with unconstrained hyperboxes. IEEE International Conference on Fuzzy Systems, 2, II–1075.

220. Rizzi, A., Mascioli, F. M. F., & Martinelli, G. (1999). Automatic training of ANFIS networks. IEEE International Conference on Fuzzy Systems, 3, III–1655. https://doi.org/10.1109/fuzzy.1999.790153

221. Fracassi, G. L., Mascioli, F. M. F., Lamedica, R., Martinelli, G., Prudenzi, A., Regoli, M., & Rizzi, A. (1998). Neuro-fuzzy approach to the planning of electric distribution networks. IEEE International Conference on Neural Networks - Conference Proceedings, 1, 79–83.

222. Mascioli, F. M. F., Risi, G., Rizzi, A., & Martinelli, G. (1998). A nonexclusive classification system based on co-operative fuzzy clustering. European Signal Processing Conference, 1998-January.

223. Rizzi, A., Frattale Mascioli, F. M., & Martinelli, G. (1998). Adaptive resolution min-max classifier. 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence, 2, 1435–1440. https://doi.org/10.1109/FUZZY.1998.686330

224. Mascioli, F. M. F., Martinelli, G., & Rizzi, A. (1997). Constructive algorithm for fuzzy neural networks. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4, 3193–3196.

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