July 10-12, 2024 Virtual Conference
Traffic classification (TC) is pivotal for network traffic management and security. Over time, TC solutions leveraging Artificial Intelligence (AI) have undergone significant advancements, primarily fueled by Machine Learning (ML). This talk analyzes the history and current state of AI-powered TC on the Internet, highlighting unresolved research questions. Indeed, despite extensive research, key desiderata goals to product-line implementations remain. AI presents untapped potential for addressing the complex and evolving challenges of TC, drawing from successful applications in other domains. Novel ML topics and solutions that address unmet TC requirements are identified, shaping a comprehensive research landscape for the TC future. Last, obstacles hindering AI-powered next-generation solutions are identified.
Domenico Ciuonzo – University of Naples Federico II, Italy – is a Tenure-Track Assistant Professor at University of Naples Federico II, Italy. He received his Ph.D. in 2013 from Univ. of Campania. He has held the following visiting appointments: NATO CMRE (IT), UCONN (US), NTNU (NOR), CTTC (ES). He is a Track-Chair for IEEE WCNC 2024, a publication chair for IEEE TMA 2023, an elected member of IEEE SPS SPCOM technical committee and a member of the “Conferences and Events Committee” of the IEEE IoT Technical Community. He has served as Associate EiC for the IEEE Comm. Letters, Tech. Editor for the IEEE Trans. on Aerospace and Electronic Systems, Lead Guest Editor for the IEEE IoT Magazine. His reviewing and editorial activities were recognized by the IEEE Comm. Letters, IEEE Trans. on Comm., IEEE Trans. on Wireless Comm., and MDPI, which nominated him Exemplary Reviewer and Editor for 14 times. He is the recipient of two Best Paper awards (IEEE ICCCS 2019 and Elsevier Computer Networks 2020), the 2019 Exceptional Service award from IEEE AESS, the 2020 Early-Career Technical Achievement award from IEEE SENSORS COUNCIL for sensor networks/systems and the 2021 Early-Career Award from IEEE AESS. His research interests fall within the areas of data fusion, network traffic analysis, statistical signal processing, IoT & wireless sensor networks, and AI. He has co-authored 135+ journal and conference publications to top-notch venues. He is co-author of the IET book “Data Fusion in Wireless Sensor Networks: A Statistical Signal Processing Perspective”. Since 2016 he is an IEEE Senior Member. D. Ciuonzo is the co-PI of the PRIN-22 “GARDEN” and serves as independent reviewer/evaluator of research and implementation projects and project proposals co-funded by many EU and non-EU parties. Web of Science ResearcherID: https://www.webofscience.com/wos/author/record/1105665 ORCID: https://orcid.org/0000-0002-6230-2958 Web: https://domenicociuonzo.wordpress.com/
Handwriting analysis is emerging as a powerful, non-invasive tool for diagnosing, monitoring, and managing neurological and cognitive health disorders. This keynote explores how handwriting dynamics can be utilized to detect early signs of diseases such as Parkinson's and Alzheimer's, assess cognitive functions, and guide personalized rehabilitation programs. The integration of advanced digital tools and machine learning models enables real-time remote monitoring and tele-rehabilitation, offering significant benefits in telemedicine and personalized healthcare. This presentation will delve into the latest research, practical applications, and future directions of handwriting analysis in the healthcare landscape, with special emphasis on the freely available tools that facilitate entry into this research field.
Marcos Faundez-Zanuy – Tecnocampus Universitat Pompeu Fabra, Spain – was born in Barcelona, Spain. He received the B.Sc. degree in Telecommunication (speciality: electronics) in 1993 and the Ph.D. degree in 1998, both from the Polytechnic University of Catalunya. He is currently a Full Professor at the Tecnocampus, Universitat Pompeu Fabra, where he leads the Signal Processing Group. His h-index WoS is 28. From 2009 to 2018, he served as the Dean of the Escola Universitària Politècnica de Mataró, which was part of the Polytechnic University of Catalonia until 2015, and subsequently, part of Pompeu Fabra University. He also held the position of Head of Research at Tecnocampus from 2010 until February 2020. His research interests lie in the fields of biometrics applied to security and health. He was the initiator and Chairman of the European COST action 277 "Nonlinear speech processing'', and the secretary of COST action 2102 “Cross-Modal Analysis of Verbal and Non-Verbal Communication”. He has authored over 200 papers indexed in the ISI Journal Citation Report, more than 100 conference papers, and around 10 books. He has also been responsible for leading 10 national and European research projects. Marcos Faundez-Zanuy has been recognized as one of the Stanford World’s top 2% scientists for the years 2019, 2020, 2021, 2022, and 2023. Specialties: Biometrics (including face, speech, hand-geometry, and online signature recognition) applied to security and health, and signal processing. Web of Science ResearcherID: https://www.webofscience.com/wos/author/record/912728 ORCID: https://orcid.org/0000-0003-0605-1282 Web: https://www.tecnocampus.cat/en/professorat/marcos-faundez-zanuy-0