Vincenzo Piuri – författare

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more.

In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging.

Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required.

3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related fields.

This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.

This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 45 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2021) and 16 papers from the 17th International Conference on Information Assurance and Security, which was held online, from December 14 to 16, 2021. A premier conference in the field of artificial intelligence and machine learning applications, HIS-IAS 2021 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from over 20 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of computer science and engineering.

This book constitutes the  refereed proceedings of the 17th International Conference on Information Security Practice and Experience, ISPEC 2022, held in Taipei, Taiwan, in November 2022.

The 33 full papers together with 2 invited papers included in this volume were carefully reviewed and selected from 87 submissions.

The main goal of the conference is to promote research on new information security technologies, including their applications and their integration with IT systems in various vertical sectors.

The 25 full papers presented were carefully reviewed and selected from 120 submissions. The CoCoLe conference focuses on Application of Supervised Learning in Computing; Application of Unsupervised Learning in Computing; and Computing in Communication Networks.

This book covers the Proceedings of the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023.” One of the evocative and valuable dimensions of this conference is the way it brings together researchers, scientists, academics, and engineers in the field from different countries and enables discussions and debate of relevant issues, challenges, opportunities, and research findings. The ICISAT’2023 conference provided a forum for research and developments in the field of information systems and advanced technologies and new trends in developing information systems organizational aspects of their development and intelligent aspects of the final product. The aim of the ICISAT’2023 is to report progress and development of methodologies, technologies, planning and implementation, tools, and standards in information systems, technologies, and sciences.ICISAT’2023 aims at addressing issues related to the intelligent information, data science, and decision support system, from multidisciplinary perspectives and to discuss the research, teaching, and professional practice in the field. The book of ICISAT’2023 includes selected papers from the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023,” organized during December 29–30, 2023.In this book, researchers, professional software, and systems engineers from around the world addressed intelligent information, data science, and decision support system for the conference. The ideas and practical solutions described in the book are the outcome of dedicated research by academics and practitioners aiming to advance theory and practice in this research domain. The list of topics is in all the areas of modern intelligent information systems and technologies such as neural networks, evolutionary computing, adaptive systems, pervasive system, ubiquitous system, E-learning and teaching, knowledge-based paradigms, learning paradigms, intelligent data analysis, intelligent decision making and support system, intelligent network security, web intelligence, deep learning, natural language processing, image processing, general machine learning, and unsupervised learning.

This book covers the Proceedings of the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023.” One of the evocative and valuable dimensions of this conference is the way it brings together researchers, scientists, academics, and engineers in the field from different countries and enables discussions and debate of relevant issues, challenges, opportunities, and research findings. The ICISAT’2023 conference provided a forum for research and developments in the field of information systems and advanced technologies and new trends in developing information systems organizational aspects of their development and intelligent aspects of the final product. The aim of the ICISAT’2023 is to report progress and development of methodologies, technologies, planning and implementation, tools, and standards in information systems, technologies, and sciences.ICISAT’2023 aims at addressing issues related to the intelligent information, data science, and decision support system, from multidisciplinary perspectives and to discuss the research, teaching, and professional practice in the field. The book of ICISAT’2023 includes selected papers from the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023,” organized during December 29–30, 2023.In this book, researchers, professional software, and systems engineers from around the world addressed intelligent information, data science, and decision support system for the conference. The ideas and practical solutions described in the book are the outcome of dedicated research by academics and practitioners aiming to advance theory and practice in this research domain. The list of topics is in all the areas of modern intelligent information systems and technologies such as neural networks, evolutionary computing, adaptive systems, pervasive system, ubiquitous system, E-learning and teaching, knowledge-based paradigms, learning paradigms, intelligent data analysis, intelligent decision making and support system, intelligent network security, web intelligence, deep learning, natural language processing, image processing, general machine learning, and unsupervised learning.