KORE PlatformLaboratorio STMLab - Laboratory for Speech Technology research and Machine Learning

RESEARCH COUNCIL OF NORWAY 2015
Aulus – Atomic units for language universal representation of speech – 2015-2019. Id: 240282/O70.
Total budget: NOK 12 milion (€ 1.34 million).
Partners: Norwegian University of Science and Technology – NTNU (Norvegia), Eurecom (Francia) e Università di Enna “Kore” (Italia)

MIUR PRIN – Progetti di Rilevante Interesse Nazionale – 2019-2022
Phonetic analysis of dysarthric speech by speakers of different varieties of Italian to develop clinical tools: objective, quantitative assessment for severity measurement, early diagnosis and rehabilitation planning, taking into account sociophonetic variation. ID: 2017JNKCYZ.
Partners: Università del Sannio, Università di Bari, Università di Enna Kore

RESEARCH COUNCIL OF NORWAY 2021:
SCRIBE: Machine transcription of Norwegian conversational speech– 2021-2026.
Id: 322964.
Total budget: NOK 27 milion (€ 2.65 million).
Partners: Norwegian University of Science and Technology – NTNU (Norvegia), Telenor Research (Noway), Nasjonalbiblioteket (Norway)

ITAMA_CAP
Finanziato nell’ambito del programma INTERREG ITALIA-MALTA, ha come obiettivo generale quello di capitalizzare i risultati del progetto ITAMA attraverso l’uso da parte dei servizi sanitari della Sicilia e delle scuole di Malta delle best practices per la diagnosi precoce della celiachia.
Durata: 9 mesi
Partners: 3
Budget: 1 Mln €uro

1. A. S. Shahrebabaki, G. Salvi, T. Svendsen and S. M. SINISCALCHI, “Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 135-147, 2022, doi: 10.1109/TASLP.2021.3133218.
2. S.M. SINISCALCHI (2021). Vector-to-Vector Regression via Distributional Loss for Speech Enhancement. IEEE Signal Processing Letters, 28, pp. 254-258, 0.1109/LSP.2021.3050386
3. J Qi, J Du, S. M. SINISCALCHI, X Ma, C.-H. Lee (2020). On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters, 27, pp. 1485-1489, doi: 10.1109/LSP.2020.3016837.
4. J. Qi, J. Du, S. M. SINISCALCHI, X. Ma, and C.-H. Lee (2020). Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression. IEEE TRANSACTIONS on SIGNAL PROCESSING, vol. 68, pp. 3411-3422, doi: 10.1109/TSP.2020.2993164.
5. I. Kukanov, S.M. SINISCALSHI, and et al. (2020). Maximal Figure-of-Merit Framework to Detect Multi-label Phonetic Features for Spoken Language Recognition,  IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 28, pp. 682-695, doi: 10.1109/TASLP.2020.2964953
6. J. Qi, J. Du, S.M. SINISCALCHI, and C.-H. Lee, (2019). A Theory on Deep Neural Network based Vector-to-Vector Regression with an Illustration of Its Expressive Power in Speech Enhancement.  IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 27 (12), p. 1932-1943, doi: 10.1109/TASLP.2019.2935891
7. R. Hussain, S.M. SINISCALCHI, and et al (2020). Ensemble Hierarchical Extreme Learning Machine for Speech Dereverberation. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, vol. 12(4), pp. 744-758, 10.1109/TCDS.2019.2953620
8. W. Li, N.F. Chen, S.M. SINISCALCHI, and C.-H. Lee, (2019). Improving Mispronunciation Detection of Mandarin Tones for Non-native Learners with Soft-target Tone Labels and BLSTM-based Deep Tone models.  IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 27 (12), pp. 2012-2024, doi:10.1109/TASLP.2019.2936755
9. J. Lin, W. Li, Y Gao, Y. Xie, N.F. Chen, SINISCALCHI S.M., J. Zhang, C.H. Lee (2018). Improving Mandarin Tone Recognition Based on DNN by Combining Acoustic and Articulatory Features Using Extended Recognition Networks. Journal of Signal Processing Systems 90 (7), 1077-1087
10. Wu, B., Li, K, Ge, F., Huang, Z., Yang, M., SINISCALCHI S. M., and Lee, C.-H. (2017). An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and  Acoustic Modeling for Robust Speech Recognition. IEEE J-STSP, 2017, doi: 10.1109/JSTSP.2017.2756439
11. Huang Z., SINISCALCHI S. M., and Lee C.-H. (2017). Hierarchical Bayesian Combination of Plug-in Maximum A Posteriori Decoders in Deep Neural Networks-based Speech Recognition and Speaker Adaptation. PATTERN RECOGNITION LETTERS, vol. 98, p. 1-7, ISSN: 0167-8655, doi: 10.1016/j.patrec.2017.08.001
12. Huang Z., SINISCALCHI S. M., and Lee C.-H. (2017). Bayesian Unsupervised Batch and Online Speaker Adaptation of Activation Function Parameters in Deep Models for Automatic Speech Recognition. IEEE/ACM TRANS. ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 25 (1); p. 64-75, doi: 10.1109/TASLP.2016.2621669
13. Wu, B., Yang, M., Li, K., SINISCALCHI S.M. et al. (2017). A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation. EURASIP J. Adv. Signal Process. 2017, 81 (2017). https://doi.org/10.1186/s13634-017-0516-6
14. SINISCALCHI S. M., and V. M. Salerno (2017). Adaptation to New Microphones using Artificial Neural Networks with Trainable Activation Functions. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, doi:10.1109/TNNLS.2016.2550532
15. Z. Huang, SINISCALCHI S. M., and C.-H. Lee (2016). A Unified Approach to Transfer Learning of Deep Neural Networks with Applications to Speaker Adaptation in Automatic Speech Recognition. NEUROCOMPUTING, vol. 218; p. 448-459, doi:10.1016/j.neucom.2016.09.018
16. Behravan H., Hautamaki V., SINISCALCHI S. M., Kinnunen T., and Lee C.-H. (2016). i-Vector Modeling of Speech Attributes for Automatic Foreign Accent Recognition. IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 24 (1); p. 29-41, ISSN: 2329-9290, doi: 10.1109/TASLP.2015.2489558
17. SINISCALCHI S. M., Svendsen T., and C.-H. Lee (2014). An Artificial Neural Network Approach to Automatic Speech Processing. NEUROCOMUTING, vol. 140; p. 326-338, ISSN: 0925-2312, doi: 10.1016/j.neucom.2014.03.005
18. R. E. Barone, T. Giuffrè, SINISCALCHI S. M., Morgana M. A., and Tesoriere G. (2014). Architecture for parking management in smart cities. IET INTELLIGENT TRANSPORT SYSTEMS, vol. 8; p. 445-452, ISSN: 1751-956X, doi: 10.1049/iet-its.2013.0045
19. Lee C.-H., and SINISCALCHI S. M. (2013). An Information-Extraction Approach to Speech Processing: Analysis, Detection, Verification, and Recognition. PROCEEDINGS OF THE IEEE, vol. 101; p. 1089-1115, ISSN: 0018-9219, doi: 10.1109/JPROC.2013.2238591
20. SINISCALCHI S. M., Svendsen T., and Lee C.-H. (2013). A bottom-up modular search approach to large vocabulary continuous speech recognition. IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, vol. 21; p. 786-797, ISSN: 1558-7916, doi: 10.1109/TASL.2012.2234115
21. SINISCALCHI S. M., Yu D., Deng L., and Lee C.-H. (2013). Speech recognition using long-span temporal patterns in a deep network model. IEEE SIGNAL PROCESSING LETTERS, vol. 20; p. 201-204, ISSN: 1070-9908, doi: 10.1109/LSP.2013.2237901
22. SINISCALCHI S. M., Li J., and Lee C.-H. (2013). Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems. IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, vol. 21; p. 2152-2161, ISSN: 1558-7916, doi: 10.1109/TASL.2013.2270370
23. SINISCALCHI S. M., Li J., and Lee C-H. (2013). Model-based margin estimation for hidden Markov model learning and generalisation. IET SIGNAL PROCESSING, vol. 7; p. 704-709, ISSN: 1751-9675, doi: 10.1049/iet-spr.2013.0036
24. SINISCALCHI S. M., Reed J., Svendsen T., and Lee C.-H. (2013). Universal attribute characterization of spoken languages for automatic spoken language recognition. COMPUTER SPEECH AND LANGUAGE, vol. 27; p. 209-227, ISSN: 0885-2308, doi: 10.1016/j.csl.2012.05.001
25. SINISCALCHI S. M., Yu D., Deng L., and Lee C.-H. (2013). Exploiting Deep Neural Networks for Detection-Based Speech Recognition. NEUROCOMPUTING, vol. 106; p. 148-157, ISSN: 0925-2312, doi: 10.1016/j.neucom.2012.11.008
26. SINISCALCHI S. M., Lyu D.-C., Svendsen T., Lee C.-H. (2012). Experiments on cross-language attribute detection and phone recognition with minimal target-specific training data. IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, vol. 20; p. 875-887, ISSN: 1558-7916, doi: 10.1109/TASL.2011.216761
27. SINISCALCHI S. M. (2012). Combining Speech Attribute Detection and Penalized Logistic Regression for Phoneme Recognition. NEUROCOMPUTING, vol. 93; p. 10-18, ISSN: 0925-2312, doi: 10.1016/j.neucom.2012.02.037
28. Birkenes O., Matsui T., Tanabe K., SINISCALCHI S. M., Myrvoll T.A., Johnsen M.H. (2010). Penalized logistic regression with HMM log-likelihood regressors for speech recognition. IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, vol. 18; p. 1440-1454, ISSN: 1558-7916, doi: 10.1109/TASL.2009.2035151
29. SINISCALCHI S. M., Lee C.-H. (2009). A study on integrating acoustic-phonetic information into lattice rescoring for automatic speech recognition. SPEECH COMMUNICATION, vol. 51; p. 1139-1153, ISSN: 0167-6393, doi: 10.1016/j.specom.2009.05.004
30. V. Conti, S.S. Ruffo, A. Merlo, M. Migliardi, S. Vitabile (2018). “A bio-inspired approach to attack graphs analysis”, proc. of 10th International Symposium on Cyberspace Safety and Security, CSS 2018, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11161 LNCS, 2018, Pages 63-76.
31. V. Conti, A. Ziggiotto, M. Migliardi, S. Vitabile, “Bio-inspired Security Analysis for IoT Scenarios”, International Journal of Embedded Systems, Vol. 13, No. 2, pp. 221-235, 2020.
32. Militello, C., Rundo, L., Toia, P., Conti, V., Russo, G., Filorizzo, C., Maffei, E., Cademartiri, F., La Grutta, L., Midiri, M., Vitabile, S., “A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans”, Computers in Biology and Medicine, Vol. 114, November 2019, Article number 103424
33. Orazio Gambino, Vincenzo Conti, Sergio Galdino, Cesare Fabio Valenti, and Wellington Pinheiro dos Santos, Editorial: Image Segmentation Techniques for Healthcare Systems, Journal of Healthcare Engineering, Volume 2019, Article ID 2723419, 2 pages, DOI: 10.1155/2019/2723419
34. V. Conti, S. S. Ruffo, S. Vitabile, L. Barolli, “BIAM: A New Bio-Inspired Analysis Methodology for Digital Ecosystems based on a Scale-free Architecture”, Soft Computing, Vol. 23, issue 4, 2019 (First Online: 14 September 2017), pp. 1133-1150 ISSN: 1432-7643 (Print) 1433-7479 (Online), Springer Editor, DOI: 10.1007/s00500-017-2832-z
35. Vincenzo Conti, Leonardo Rundo, Giuseppe Dario Billeci, Carmelo Militello and Salvatore Vitabile, “Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays: An Experimental Case Study”, Energies, 2018, 11(4), 739, doi:10.3390/en11040739
36. A. Alongi, V. Conti, G. Vitello, S. Vitabile, “An Empirical Set of Metrics for Embedded Systems Testing”, IEEE Design & Test, Volume 35, Issue 5, October 2018, Article number 8240747, pp. 45-53, ISSN: 2168-2356, DOI: 10.1109/MDAT.2017.2787678
37. C. Militello, L. Rundo, V. Conti, L. Minafra, F. P. Cammarata, S. Vitabile, G. Mauri, M. C. Gilardi, N. Porcino, “Area-Based Cell Colony Surviving Fraction Evaluation: A Novel Fully Automatic Approach Using General-Purpose Acquisition Hardware”, Computers in Biology and Medicine, 2017, Vol. 89, pp. 454-465, ISSN: 0010-4825, Elsevier Editor, DOI: https://doi.org/10.1016/j.compbiomed.2017.08.005
38. G. Vitello, A. Alongi, V. Conti, S. Vitabile, “A Bio-Inspired Cognitive Agent for Autonomous Urban Vehicles Routing Optimization”, IEEE Transactions on Cognitive and Developmental Systems, (first online publication on 12 September 2016), Vol. 9, Issue 1, March 2017, pp. 5-15, ISSN 2379-8920, DOI: 10.1109/TCDS.2016.2608500.
39. G. Vitello, V. Conti, S. Vitabile, and F. Sorbello, “Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users”, Mobile Information Systems, Vol. 2015 (2015), 13 pages, Hindawi Publishing Corporation, Article ID 401975, DOI: http://dx.doi.org/10.1155/2015/401975
40. V. Conti, C. Militello, F. Sorbello and S. Vitabile, “Biometric Sensors Rapid Prototyping on FPGA”, The Knowledge Engineering Review, 2015, Vol. 30, N° 2, pp. 201-219, ISSN: 0269-8889, EISSN: 1469-8005, DOI: 10.1017/S0269888914000307
41. C. Militello, V. Conti, F. Sorbello, and S. Vitabile, “A Fast Fusion Technique for Fingerprint and Iris Spatial Descriptors in Multimodal Biometric Systems”, International Journal of Computer Systems Science and Engineering (IJCSSE), Vol. 29, n° 3, pp. 205-217, ISSN 0267-6192, © 2014 CRL Publishing Ltd.
42. S. Vitabile, V. Conti, M. Collotta, G. Scatà, S. Andolina, A. Gentile, F. Sorbello, “A Real-Time Network Architecture for Biometric Data Delivery in Ambient Intelligent”, Journal of Ambient Intelligence and Humanized Computing (AIHC), © Springer-Verlag Editor, June 2013, Vol. 4, Issue 3, pp. 303-321, Print ISSN 1868-5137, Online ISSN 1868-5145, DOI: 10.1007/s12652-011-0104-9
43. C. Militello, V. Conti, S. Vitabile and F. Sorbello, “Embedded Access Points for Trusted Data and Resources Access in HPC Systems”, The Journal of Supercomputing – An international journal of High-Performance Computer Design, Analysis and Use, Springer Netherlands Publisher, 2011, ISSN 0920-8542, Vol. 55, N° 1, pp. 4 – 27, (ISSN Online 1573-0484), DOI:10.1007/s11227-009-0379-1
44. S. Vitabile, V. Conti, B. Lanza, D. Cusumano, and F. Sorbello, “Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation”, Frontiers in Artificial Intelligence and Applications, IOS Press Editor, Vol. 234: Neural Nets WIRN11, pp. 66-73, 2011, ISSN 0922-6389, ISBN 978-1-60750-971-4, DOI: 10.3233/978-1-60750-972-1-66
45. V. Conti, C. Militello, F. Sorbello, S. Vitabile. “A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems”, IEEE Transactions on Systems, Man, and Cybernetics (SMC) Part C: Applications & Reviews, Vol, 40 issue 4, pp. 384-395. 2010, ISSN 1094-6977, DOI:10.1109/TSMCC.2010.2045374
46. C. Militello, V. Conti, S. Vitabile, F. Sorbello, “An Embedded Iris Recognizer for Portable and Mobile Devices”, Special Issue on “Frontiers in Complex, Intelligent and Software Intensive Systems” of International Journal of Computer Systems Science & Engineering, Vol. 25, n° 2, pp. 119-131, ISSN 0267-6192, © 2010 CRL Publishing Ltd., ISSN: 0267-6192
47. V. Conti, C. Militello, S. Vitabile and F. Sorbello, “A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems”, International Journal on Mobile Information Systems – Vol. 5, No. 2, 2009, pp. 105-124, IOS Press Ed., ISSN: 1574-017X, DOI:10.3233/MIS-2009-0076
48. S. Vitabile, V. Conti, C. Militello, F. Sorbello, “An Extended JADE-S Based Framework for Developing Secure Multi-Agent Systems”, Computer Standard and Interfaces Journal, Elsevier Editor, Vol. 31, issue 5, 2009, pp. 913-930, ISSN: 0920-5489, DOI:10.1016/j.csi.2008.03.017
49. S. Vitabile, V. Conti, G. Lentini, F., Sorbello, “An Intelligent Sensor for Fingerprint Recognition”, Proc. of International Conference on Embedded and Ubiquitous Computing (EUC-05), Lecture Note in Computer Science (LNCS), Springer-Verlag, vol. 3824, pp. 27-36, ISBN 3-540-30807-5, 2005, DOI: 10.1007/11596356_6 (conference su scopus)
50. G. Pilato S. Vitabile, V. Conti, G. Vassallo, F. Sorbello (2003). “A Concurrent Neural Classifier for HTML Documents Retrieval“, B. Apolloni, M. Marinaro, R. Tagliaferri (Eds.): Neural Nets – WIRN 2003, Lecture Notes in Computer Science, vol. 2859, pp. 210-217, Springer-Verlag, DOI: 10.1007/978-3-540-45216-4_24
51. Vincenzo Conti, Leonardo Rundo, Carmelo Militello, Giancarlo Mauri, and Salvatore Vitabile, “Resource-Efficient Hardware Implementation of a Neural-based Node for Automatic Fingerprint Classification”, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), Vol. 8, N. 4, pp. 19-36, December 2017, DOI: 10.22667/JOWUA.2017.12.31.019
52. V. Conti, S. Vitabile, “Design Exploration of AES Accelerators on FPGAs and GPUs”, Journal on Telecommunications and Information Technology, 2017, issue 1, pp. 28-38, ISSN 1509-4553, National Institute of Telecommunications Publisher
53. G. Vitello, V. Conti, S. Vitabile, and F. Sorbello, “An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks”, in Advances in Neural Networks: Computational and Theoretical Issues, proc. of WIRN 2014, Editors: S. Bassis, A. Esposito, F.C. Morabito, pp. 217-227, Series: Smart Innovation, Systems and Technologies, Springer International Publishing, Vol. 37, 2015, ISSN: 2190-3018, DOI: 10.1007/978-3-319-18164-6
54. V. Conti, M. Collotta, G. Pau, and S. Vitabile, “Usability Analysis of a Novel Biometric Authentication Approach for Android-based Mobile Devices”, Journal on Telecomunications and Information Technology, 2014, vol. 2014, issue 4, pp. 34-43, ISSN 1509-4553, Publisher: National Institute of Telecommunications
55. M. Collotta, V. Conti, G. Scatà, G. Pau, S. Vitabile, “Smart Wireless Sensor Networks and Biometric Authentication for Real Time Traffic Light Junctions Management“, International Journal of Intelligent Information and Database Systems, Copyright © 2013 Inderscience Enterprises Ltd., ISSN online: 1751-5866, ISSN print: 1751-5858, Vol. 7, No. 5, 2013, pp. 454-478, DOI 10.1504/IJIIDS.2013.056392
56. V. Conti, S. Vitabile, L. Agnello, F. Sorbello, “Fingerprint and Iris based Authentication in Inter-cooperative Emerging e-Infrastructures”, Studies in Computational Intelligence (Internet of things and inter-cooperative computational technologies for collective intelligence), ISBN 978-3-642-34951-5, ISSN 1860-949X, Vol. 460, 2013, pp. 433-462, Springer Berlin Heidelberg Editor
57. M. Collotta, V. Conti, G. Pau, G. Scatà, S. Vitabile, “Fuzzy Techniques for Access and Data Management in Home Automation Environments”, Journal of Mobile Multimedia, Vol. 8, No. 3 (2012), pp. 181-203, © Rinton Press (Princeton, New Jersey) Publisher, ISSN: 1550-4646
58. S. Vitabile, V. Conti, B. Lanza, D. Cusumano, and F. Sorbello, “Metabolic Networks Robustness: Theory, Simulations and Results”, Journal of Interconnection Networks (JOIN), Vol. 12, Issue No. 3, pp 221-240, © World Scientific Publishing Company, 2011, ISSN: 0219-2659 (print), 1793-6713 (online), DOI: 10.1142/S0219265911002964
59. G. Pilato, S. Vitabile, G. Vassallo, V. Conti, F. Sorbello, “Web Directories as a Knowledge Based to Build a Multi-Agent System for Information Sharing”, An International Journal on Web Intelligence and Agent Systems, 2004, Vol. 2, No. 4, pp. 265-277, ISSN: 1570-1263 (print) – 1875-9289 (Online), IOS Press (IF 0.865)
60. Pingfei Jiang, Mark Atherton, Salvatore Sorce, David Harrison & Alessio Malizia (2018) “Design for invention: a framework for identifying emerging design–prior art conflict”, Journal of Engineering Design, 29:10, 596-615, DOI: 10.1080/09544828.2018.1520204
61. F. Alonge, F.D’Ippolito, G. Garraffa. A. Sferlazza, “A hybrid observer for localization of mobile vehicles with asynchronous measurements”, Asian Journal of Control, 2019, Vol.21, No.4, pp. 1506-1521, pub. Wiley.
62. G. Garraffa, A. Sferlazza, F. D’Ipplito, F. Alonge, “Localization based on Parallel Robots Kinematics As an alternative to trilateration”, IEEE Transactions on Industrial Electronics, 2021, Vol. 69, No. 1, pp. 999-1010, IEEE.
63. F. Alonge, F. D’Ippolito, A. Fagiolini, G. Garraffa, A. Sferlazza, “Trajectory robust control of autonomous quadcopters based on model decoupling and disturbances estimation”, International Journal of Advanced Robotic Systems, 2021, Vol. 18, No. 2, SAGE Publications UK, London, England.
64. Cilia N.D., D’Alessandro T., De Stefano C., Fontanella F., Molinara M. ( 2021). From online handwriting to synthetic images for Alzheimer’s disease detection using a deep transfer learning approach, IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2021.3101982.
65. Cilia N.D., De Stefano C., Fontanella F, Scotto di Freca A. (2021). Feature selection as a tool to support the diagnosis of cognitive impairments through handwriting analysis, IEEE Access 9: 78226-78240.
66. Cilia N.D., Tiziana D’Alessandro, De Stefano C., Fontanella F. (2021). Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction, Machine Vision and Application, in press.
67. Cilia N.D., De Stefano C., Fontanella F., Marrocco C., Molinara M., Scotto di Freca A (2021) Deep Transfer Learningfor Alzheimer’s disease detection. IEEE Xplore, 25th International Conference on Pattern Recognition (ICPR), pp. 9904-9911, doi: 10.1109/ICPR48806.2021.9412603.
68. Cilia N.D., De Stefano C., Fontanella F., Marrocco C., Molinara M., Scotto di Freca A. (2020). An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents. J. Imaging 6(9): 89.
69.  Cilia N.D., De Stefano C., Fontanella F., Molinara M., Scotto di Freca A. (2020), What is the minimum training data size to reliably identify writers in medieval manuscripts? Pattern Recognition Letters, 129, pp. 198-204.
70.  Cilia N.D., De Stefano C., Fontanella F., Marrocco M., Molinara M., Scotto di Freca A. (2020). An end-to-end deep learning system for medieval writer identification, Pattern Recognition Letters, 129, pp. 137-143.
71.  Cilia N.D., De Stefano C., Fontanella F., Scotto di Freca A. (2019). An Experimental Comparison of Feature-Selection and Classification Methods for Microarray Datasets, Information, 10(3): 109.
72.  Scarpato N. Cilia N.D, Romano M. (2019). Reachability Matrix Ontology: A Cybersecurity Ontology, Applied Artificial Intelligence, 33(7), pp. 643-655.
73. Cilia N.D., De Stefano C., Fontanella F., Scotto di Freca A. (2019). A ranking-based feature selection approach for handwritten character recognition. Pattern Recognition Letters, 121, pp. 77-86.