Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Annalisa Appice"'
Publikováno v:
Future Internet, Vol 16, Iss 5, p 168 (2024)
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine l
Externí odkaz:
https://doaj.org/article/71db32118cf64a9fa95eb471a7646253
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10050-10066 (2023)
Recent long spells of high temperatures and drought-hit summers have fostered the conditions for an unprecedented outbreak of bark beetles in Europe. This phenomenon has ruined vast swathes of European conifer forests creating a need among forest man
Externí odkaz:
https://doaj.org/article/d38ebb1c7d544cafab608ef194d8397d
Publikováno v:
IEEE Access, Vol 8, Pp 53346-53359 (2020)
Networks had an increasing impact on modern life since network cybersecurity has become an important research field. Several machine learning techniques have been developed to build network intrusion detection systems for correctly detecting unforese
Externí odkaz:
https://doaj.org/article/db3e205e96694dc18cca8c8e02ed7166
Publikováno v:
IEEE Access, Vol 8, Pp 52713-52725 (2020)
The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal
Externí odkaz:
https://doaj.org/article/6af32c42921240169315e334e3c43b1e
Autor:
Vincenzo Pasquadibisceglie, Annalisa Appice, Giovanna Castellano, Donato Malerba, Giuseppe Modugno
Publikováno v:
IEEE Access, Vol 8, Pp 184073-184086 (2020)
The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning
Externí odkaz:
https://doaj.org/article/4d7da4dd1b4148f0908575d2ab295501
Autor:
Donato Malerba, Annalisa Appice, Paolo Buono, Giovanna Castellano, Berardina De Carolis, Marco de Gemmis, Marco Polignano, Veronica Rossano, Lynn Margaret Rudd
Publikováno v:
Je-LKS: Journal of E-Learning and Knowledge Society, Vol 15, Iss 2 (2019)
Robotics in education is a promising new area: social robots have started to move into schools as part of educational/learning technologies, playing roles in educational settings that range from tutors, teaching assistants and learners, to learning c
Externí odkaz:
https://doaj.org/article/9cbd5fafacdd47bd84541867df4288da
Publikováno v:
Journal of Spatial Information Science, Vol 2013, Iss 6, Pp 119-153 (2013)
Ubiquitous sensor stations continuously measure several geophysical fields over large zones and long (potentially unbounded) periods of time. However, observations can never cover every location nor every time. In addition, due to its huge volume, th
Externí odkaz:
https://doaj.org/article/5cefb165662948ea930352dc2af767b7
Publikováno v:
Information Sciences. 606:250-271
Autor:
Giuseppina Andresini, Annalisa Appice
Publikováno v:
Journal of Intelligent Information Systems. 60:277-279
Publikováno v:
IEEE Transactions on Services Computing. 15:2382-2395
The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces basedon the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity