Search results for "Data type"
showing 10 items of 1183 documents
An Efficient Traceable Attribute-Based Authentication Scheme with One-Time Attribute Trees
2015
Attribute-based authentication (ABA) is a way to authenticate signers by means of attributes and it requests proof of possessing required attributes from the one to be authenticated. To achieve the property of traceability, required attributes should be combined with the signer’s attribute private keys in order to generate a signature. In some schemes, signers’ attribute keys are related to attribute trees, so changing attribute trees will cause the regeneration of all related attribute keys. In this paper, we propose an efficient traceable ABA scheme, where the generation of signers’ attribute keys is independent from attribute trees. Thus the same set of attribute keys can be used with a …
Learning Automata-Based Solutions to the Multi-Elevator Problem
2019
In the last century, AI has been the topic of interest in many areas, where the focus was on mimicking human behaviour. It has been researched to be incorporated into different domains, such as security, diagnosis, autonomous driving, financial prediction analysis and playing games such as chess and Go. They also worked on different subfields of AI such as machine learning, deep learning, pattern recognition and other relevant subfields. Our work in a previous paper [1] focused on a problem that has not been tackled using AI before, which is the elevator-problem. In which we try to find the optimal parking floor for the elevator for the single elevator problem. In this paper, our work exten…
An Efficient Multi-Show Unlinkable Attribute Based Credential Scheme for a Collaborative E-Health Environment
2017
Modern electronic healthcare (e-health) systems constitute collaborative environments in which patients' private health data are shared across multiple domains. In such environments, patients' privacy can be violated through the linkability of different user access sessions over patient health data. Therefore, enforcing anonymous as well as multi-session unlinkable access for the users in e-health systems is of paramount importance. As a way of achieving this requirement, more emphasis has been given to anonymous attribute credentials, which allows a user to anonymously prove the ownership of a set of attributes to a verifier and thereby gain access to protected resources. Among the existin…
Global sensitivity analysis of the A-SCOPE model in support of future FLEX fluorescence retrievals
2014
In support of ESA's Earth Explorer 8 candidate mission FLEX (FLuorescence EXplorer), a Photosynthesis Study has been initiated to quantitatively link fluorescence to photosynthesis. This led to the development of A-SCOPE, a graphical user interface software package that integrates multiple biochemical models into the soil-vegetation-atmosphere-transfer model SCOPE. Its latest version (v1.53) has been successfully verified and was subsequently evaluated through a global sensitivity analysis. By using the method of Saltelli [4], the relative importance of each input variable to model outputs was quantified through first order and total effect sensitivity indices. Variations in leaf area index…
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …
A neural network approach to movement pattern analysis.
2004
Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, …
Qualitative analysis of goat and sheep production data using self-organizing maps
2009
The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map …
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
2021
DNA sequences are the basic data type that is processed to perform a generic study of biological data analysis. One key component of the biological analysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. Machine Learning (ML) methodologies have given a fundamental contribution to the solution of the problem. Among them, recently, also Deep Neural Network (DNN) models have shown strongly encouraging results. In this chapter, we deal with specific classification problems related to t…
Design of the Tube Roll Forming Process Through an Heuristic Algorithm
2002
In the paper the design of an industrial tube roll forming process is developed utilizing an heuristic algorithm, namely the simulated annealing (SA). In particular both the number and the shape of the deforming rolls are determined in order to define the forming sequence. The effectiveness of the results supplied by the SA algorithm is verified both by means of some experiments tests carried out on an industrial tube roll forming equipment and through a set of numerical simulations of the process.
Healthcare trajectory mining by combining multidimensional component and itemsets
2012
Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $ \lbrace $ Radiography, Appendectomy $\rbrace$) and…