This book provides an overview on the current progresses in artificial intelligence and neural nets in data science. The book is reporting on intelligent algorithms and applications modeling, prediction, and recognition tasks and many other application areas supporting complex multimodal systems to enhance and improve human-machine or human-human interactions. This field is broadly addressed by the scientific communities and has a strong commercial impact since investigates on the theoretical frameworks supporting the implementation of sophisticated computational intelligence tools. Such tools…mehr
This book provides an overview on the current progresses in artificial intelligence and neural nets in data science. The book is reporting on intelligent algorithms and applications modeling, prediction, and recognition tasks and many other application areas supporting complex multimodal systems to enhance and improve human-machine or human-human interactions. This field is broadly addressed by the scientific communities and has a strong commercial impact since investigates on the theoretical frameworks supporting the implementation of sophisticated computational intelligence tools. Such tools will support multidisciplinary aspects of data mining and data processing characterizing appropriate system reactions to human-machine interactional exchanges in interactive scenarios. The emotional issue has recently gained increasing attention for such complex systems due to its relevance in helping in the most common human tasks (like cognitive processes, perception, learning, communication, and even "rational" decision-making) and therefore improving the quality of life of the end users.
Anna Esposito received her Laurea degree summa cum laude in Information Technology and Computer Science from Salerno University (1989) and the 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 Postdoc at IIASS, Lecturer at Salerno University in Department of Physics (1996-2000), and Research Professor (2000-2002) at WSU in Department of Computer Science and Eng., OH, USA. She is Full Professor at Campania University "L. Vanvitelli". She is author of 300+ peer-reviewed publications in journals, books, and conference proceedings and editor-coeditor of 30+ books in the Springer series SIST, ISRL, LNCS and LNAI. Marcos Faundez-Zanuy received the B.Sc. degree (1993) and the Ph.D. degree (1998), both from the Polytechnic University of Catalunya. He is Full Professor at ESUP Tecnocampus Mataro and heads the Signal Processing Group. His research interests lie in biometrics applied to security and health. He was 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 author of 50+ papers indexed in ISI Journal citation report, 100+ conference papers, 10+ books, and PI of 10 national and EU funded projects. Francesco Carlo Morabito joined the University of Reggio Calabria, Italy, in 1989 where he is a Full Professor (2001) of Electrical Engineering. He served as President of the Electronic Engineering Course, as Member of the University's Inner Evaluation Committee, as Dean of the Faculty of Engineering, as Deputy Rector, and as Vice Rector for Internationalization. He is Member of the Steering Committee of the Italian Society of Electrical Engineering. He served as President of Siren(2008-2014), as a Governor of INNS (2000-2012, 2022-) and is Vice-President of INNS. Eros G. Pasero is Professor of Electronics at Politecnico of Turin since 1991. He was Visiting Professor at ICSI Berkeley (1991), Tongji University, Shanghai (2011, 2015), and Tashkent Politechic University, Uzbekistan. His interests are in Artificial Neural Networks and Electronic Sensors. He heads the Neuronica Lab (1990) where wired and wireless sensors are developed for biomedical, environmental, automotive applications, and sensor signals that are processed by neural networks. Prof. Pasero is President of the Italian Society for Neural Networks (SIREN) and was General Chair of IJCNN2000, SIRWEC2006, and WIRN 2015. He received several awards and holds 5 international patents. He supervised 10 international Ph.D. and 100 master's theses and is Author of 100+ international publications.
Inhaltsangabe
Generating New Sounds by Vector Arithmetic in the Latent Space of the MelGAN Architecture.- Graph Neural Networks for Topological Feature Extraction in ECG Classification.- Manifold Learning by a Deep Gaussian Process Variational Autoencoder.- Analysis of Sensors for Movement Analysis.- Dual Seep Clustering.- Learning-Based Approach to Predict Fatal Events in Brugada Syndrome.- Breast Cancer Localization and Classification in Mammograms Using YoloV5.- Deep Acoustic Emission Detection Trained on Seismic Signals.- A Deep Learning Framework for the Classification of Pre-Prodromal and Prodromal Alzheimer's Disease Using Resting-State EEG signals.- Imitation Learning Through Prior Injection in Markov Decision Processes.- Vision-Based Human Activity Recognition Methods Using Pose Estimation.- Identifying Exoplanets in TESS Data by Deep Learning.- Computational Intelligence for Marine Litter Recovery.- A Synthetic Datasetfor Learning Optical Flow in Underwater Environment.- An Interpretable BERT-Based Architecture for SARS-CoV-2 Variant Identification.
Generating New Sounds by Vector Arithmetic in the Latent Space of the MelGAN Architecture.- Graph Neural Networks for Topological Feature Extraction in ECG Classification.- Manifold Learning by a Deep Gaussian Process Variational Autoencoder.- Analysis of Sensors for Movement Analysis.- Dual Seep Clustering.- Learning-Based Approach to Predict Fatal Events in Brugada Syndrome.- Breast Cancer Localization and Classification in Mammograms Using YoloV5.- Deep Acoustic Emission Detection Trained on Seismic Signals.- A Deep Learning Framework for the Classification of Pre-Prodromal and Prodromal Alzheimer’s Disease Using Resting-State EEG signals.- Imitation Learning Through Prior Injection in Markov Decision Processes.- Vision-Based Human Activity Recognition Methods Using Pose Estimation.- Identifying Exoplanets in TESS Data by Deep Learning.- Computational Intelligence for Marine Litter Recovery.- A Synthetic Datasetfor Learning Optical Flow in Underwater Environment.- An Interpretable BERT-Based Architecture for SARS-CoV-2 Variant Identification.
Generating New Sounds by Vector Arithmetic in the Latent Space of the MelGAN Architecture.- Graph Neural Networks for Topological Feature Extraction in ECG Classification.- Manifold Learning by a Deep Gaussian Process Variational Autoencoder.- Analysis of Sensors for Movement Analysis.- Dual Seep Clustering.- Learning-Based Approach to Predict Fatal Events in Brugada Syndrome.- Breast Cancer Localization and Classification in Mammograms Using YoloV5.- Deep Acoustic Emission Detection Trained on Seismic Signals.- A Deep Learning Framework for the Classification of Pre-Prodromal and Prodromal Alzheimer's Disease Using Resting-State EEG signals.- Imitation Learning Through Prior Injection in Markov Decision Processes.- Vision-Based Human Activity Recognition Methods Using Pose Estimation.- Identifying Exoplanets in TESS Data by Deep Learning.- Computational Intelligence for Marine Litter Recovery.- A Synthetic Datasetfor Learning Optical Flow in Underwater Environment.- An Interpretable BERT-Based Architecture for SARS-CoV-2 Variant Identification.
Generating New Sounds by Vector Arithmetic in the Latent Space of the MelGAN Architecture.- Graph Neural Networks for Topological Feature Extraction in ECG Classification.- Manifold Learning by a Deep Gaussian Process Variational Autoencoder.- Analysis of Sensors for Movement Analysis.- Dual Seep Clustering.- Learning-Based Approach to Predict Fatal Events in Brugada Syndrome.- Breast Cancer Localization and Classification in Mammograms Using YoloV5.- Deep Acoustic Emission Detection Trained on Seismic Signals.- A Deep Learning Framework for the Classification of Pre-Prodromal and Prodromal Alzheimer’s Disease Using Resting-State EEG signals.- Imitation Learning Through Prior Injection in Markov Decision Processes.- Vision-Based Human Activity Recognition Methods Using Pose Estimation.- Identifying Exoplanets in TESS Data by Deep Learning.- Computational Intelligence for Marine Litter Recovery.- A Synthetic Datasetfor Learning Optical Flow in Underwater Environment.- An Interpretable BERT-Based Architecture for SARS-CoV-2 Variant Identification.
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