This book provides an overview of the current advances in artificial intelligence and neural nets. Artificial intelligence (AI) methods have shown great capabilities in modelling, prediction and recognition tasks supporting humanmachine interaction.
At the same time, the issue of emotion has gained increasing attention due to its relevance in achieving human-like interaction with machines. The real challenge is taking advantage of the emotional characterization of humans' interactions to make computers interfacing with them emotionally and socially credible.
The book assesses how and to what extent current sophisticated computational intelligence tools might support the multidisciplinary research on the characterization of appropriate system reactions to human emotions and expressions in interactive scenarios. Discussing the latest recent research trends, innovative approaches and future challenges in AI from interdisciplinary perspectives, it is a valuable resource for researchers and practitioners in academia and industry.

Provides an overview of the recent advances in artificial intelligence and neural nets Discusses the real challenge in the context of the emotional characterization of humans Serves as a reference resource for researchers and practitioners in academia and industry

Autorentext

Anna Esposito received her Laurea degree summa cum laude in Information Technology and Computer Science from Salerno University (1989), and her Ph.D. degree in Applied Mathematics and Computer Science from Napoli University Federico II (1995) with a thesis developed at MIT, Boston, USA. She was a postdoc at IIASS, Lecturer at Salerno University Department of Physics (1996-2000), and Research Professor (2000-2002) at WSU Department of Computer Science and Engineering, Ohio, USA. She is currently an Associate Professor at Campania University L. Vanvitelli. She has published over 240 peer-reviewed papers in journals, books, and conference proceedings.
Marcos Faundez-Zanuy received his B.Sc. degree (1993) and Ph.D. (1998) from the Polytechnic University of Catalunya. He is a Full Professor at ESUP Tecnocampus Mataro, where he also heads the Signal Processing Group. His research focuses on biometrics applied to security and health. He was the initiator and Chairman of the EU COST action 277 "Nonlinear speech processing" and secretary of COST action 2102 "Cross-Modal Analysis of Verbal and Non-Verbal Communication". He is the author of over 50 papers indexed in ISI Journal citation report, over 100 conference papers and several books, and is the PI of 10 national and EU funded projects.
Francesco Carlo Morabito joined the University of Reggio Calabria, Italy, in 1989, and has been a Full Professor of Electrical Engineering there since 2001. He served as President of the Electronic Engineering Course, a member of the University's Inner Evaluation Committee, Dean of the Faculty of Engineering and Deputy Rector, and is currently Vice-Rector for Internationalization. He is a member of the Italian Society of Electrical Engineering's Steering Committee.
Eros Pasero has been a Professor of Electronics at Politecnico of Turin since 1991. He was a visiting Professor at ICSI Berkeley (1991), Tongji University Shanghai (2011, 2015), and Tashkent Politechic University, Uzbekistan. His interests include artificial neural networks and electronic sensors. He heads the Neuronica Lab, which develops wired and wireless sensors for biomedical, environmental and automotive applications, and neural networks for sensor signals processing. Prof. Pasero is President of the Italian Society for Neural Networks (SIREN) and was General Chair of IJCNN2000, SIRWEC2006, and WIRN 2015. He has received several awards and holds 5 international patents, and is the author of over 100 international publications.

Inhalt
Part I: Introduction.- Towards Socially and Emotionally Believable ICT Interfaces.- Part II: Neural Networks and Related Applications.- The Simplification Conspiracy.- Passengers' Emotions Recognition to Improve Social Acceptance of Autonomous Driving Vehicles.- Road Type Classification using Acoustic Signals: Deep Learning Models and Real-Time Implementation.- Emotional Content Comparison in Speech Signal Using Feature Embedding.