• V.Randazzo, G.Cirrincione, A.Paviglianiti, E.Pasero, F.C.Morabito, “Neural feature extraction for the analysis of Parkinsonian patient handwriting”, Springer, ISBN: 978-981-15-5092-8
  • V.Randazzo, G.Cirrincione, E.Pasero, “A new unsupervised neural approach to stationary and non-stationary data”, Springer, ISBN: 978-3-030-51870-7
  • J.Ferretti, V.Randazzo, G.Cirrincione, E.Pasero, “1-D Convolutional Neural Network for ECG Arrhythmia Classification”, Springer Singapore, ISBN: 978-981-15-5092-8
  • R.R.Kumar, V.Randazzo, G.Cirrincione, M.Cirrincione, E.Pasero, A.Tortella, M.Andriollo, “Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis”, IEEE Access, DOI:
  • Paredes Q.; Miryam E.; Abdunabiev, S.; Allegretti, M.; Merlone, A.; Musacchio, C.; Pasero, E.; Tordella, D.; Canavero, F., “Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations within Warm Clouds: Electronic Design”, Sensors, DOI:
  • Alfieri, F.; Ancona, A.; Tripepi, G.; Crosetto, D.; Randazzo, V.; Paviglianiti, A.; Pasero, E.; Vecchi, L.; Cauda, V.; Fagugli, R., “A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients”, Jn. Journal of Nephrology, DOI:
  • V.Randazzo, J.Ferretti, E.Pasero, “Anytime ecg monitoring through the use of a low-cost, user-friendly, wearable device”, Sensors, DOI:
  • A.Paviglianiti, V.Randazzo, S.Villata, G.Cirrincione, E.Pasero, “A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction”, Cognitive Computation, DOI:
  • P.Barbiero, G.Ciravegna, V.Randazzo, E.Pasero, G.Cirrincione, “Topological Gradient-based Competitive Learning”, 2021 International Joint Conference on Neural Networks, IJCNN 2021, ISBN: 978-1-6654-3900-8