The proceedings of the AIAI 2020 will be published as always by the SPRINGER IFIP AICT Series and they are INDEXED BY SCOPUS, DBLP, Google Scholar, ACM Digital Library, IO-Port, MAthSciNet, CPCI, Zentralblatt MATH and EI Engineering Index
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BEST PAPER AWARDS
ONE BEST PAPER AWARD of 150 Euros cash will be given to the best student EANN/AIAI 2020 paper
by the General co-Chair PROFESSOR JOHN MACINTYRE (University of Sunderland).
T U T O R I A L S
Artificial Intelligence for Cloud Computing Management
Prof. Vincenzo Piuri
This talk will discuss a user-centric, dependability- and resilience-driven framework that considers deploying and protecting users’ applications in the Cloud infrastructure so as to minimize their exposure to the vulnerabilities in the network, as well as offering fault tolerance and resilience as a service to the users who need to deploy their applications in the Cloud.
W O R K S H O P S
9th Mining Humanistic Data Workshop
The Mining Humanistic Data Workshop (MHDW) aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing artificial intelligence, data matching, fusion and mining and knowledge discovery and management techniques to data derived from all areas of Humanistic Sciences.
5th Workshop on “5G – Putting Intelligence to the Network Edge” (5G-PINE 2020)
The 5th 5G-PINE Workshop has been established to disseminate knowledge obtained from actual EU projects as well as from any other action of EU-funded research, in the wider thematic area of “5G Innovative Activities – Putting Intelligence to the Network Edge” and with the aim of focusing on Artifical Intelligence (AI) in modern 5G telecommunications infrastructures.
Machine Learning and Computational Intelligence in multi-omics and medical image analysis (MALCI_MUOMI 2020)
There is an increasing need for the application of Machine Learning (ML) and Computational Intelligence (CI) techniques, which can effectively perform image processing operations (such as segmentation, co-registration, classification, and dimensionality reduction), in the fields of neuroimaging and oncological imaging. Although the manual approach often remains the golden standard in some tasks (e.g., segmentation), ML can be exploited to automate and facilitate the work of researchers and clinicians. Frequently used techniques include Support Vector Machines (SVMs) for classification problems, graph-based methods, and Artificial Neural Networks (ANNs).
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P O R T O C A R R A S G R A N D R E S O R T
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