Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Abdullatif, Amr R.A."'
Yes
Machine learning (ML) is increasingly employed for automating complex tasks, specifically in autonomous driving. While ML applications bring us closer to fully autonomous systems, they simultaneously introduce security and safety risks speci
Machine learning (ML) is increasingly employed for automating complex tasks, specifically in autonomous driving. While ML applications bring us closer to fully autonomous systems, they simultaneously introduce security and safety risks speci
Externí odkaz:
http://hdl.handle.net/10454/19992
Yes
The early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting
The early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting
Externí odkaz:
http://hdl.handle.net/10454/19555
Yes
Immunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by explori
Immunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by explori
Externí odkaz:
http://hdl.handle.net/10454/19308
Yes
Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition
Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition
Externí odkaz:
http://hdl.handle.net/10454/18707
Autor:
Uglanov, Alexey, Kartashev, K., Campean, Felician, Doikin, Aleksandr, Abdullatif, Amr R.A., Angiolini, E., Lin, C., Zhang, Q.
This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish sim
Externí odkaz:
http://hdl.handle.net/10454/18692
Autor:
Kartashev, K., Doikin, Aleksandr, Campean, Felician, Uglanov, Alexey, Abdullatif, Amr R.A., Zhang, Q., Angiolini, E.
This paper presents a novel approach for probabilistic clustering, motivated by a real-world problem of modelling driving behaviour. The main aim is to establish clusters of drivers with similar journey behaviour, based on a large sample of historic
Externí odkaz:
http://hdl.handle.net/10454/18694
Yes
This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifi
This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifi
Externí odkaz:
http://hdl.handle.net/10454/18591
Yes
Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift).
Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift).
Externí odkaz:
http://hdl.handle.net/10454/17599
Yes
Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier anal
Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier anal
Externí odkaz:
http://hdl.handle.net/10454/17629
Yes
Real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taki
Real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taki
Externí odkaz:
http://hdl.handle.net/10454/17627