OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment

Aslam, Sidra; Ahmed, Mansoor; Ahmed, Imran; Khan, Abid; Ahmad, Awais; Imran, Muhammad; Anjum, Adeel; Hussain, Shahid

doi:10.1007/s11042-020-08764-2

Cited by 1 publication

(1 citation statement)

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“…However, role-based access control model is static in nature and does not provide the dynamism of collaboration required in the multi-domain environment. The contribution by Aslam et al "OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment" presents an ontology-based access control (OBAC) model, which provides a solution by using an ontology-based approach [1]. In OBAC model, agents are used for the identification and classification of roles, objects and permissions (ROP) in distributed environment.…”

Section: Performance Improvementsmentioning

confidence: 99%

Artificial intelligence in deep learning algorithms for multimedia analysis

Jeon

Anisetti

Damiani

et al. 2020

Multimed Tools Appl

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Deep learning has gained a lot of research interest in artificial intelligence (AI) in many applications, such as image understanding, object detection, feature extraction, audio/ video processing, image demosaicking and denoising, overhead views in industrial applications. In addition, exploitation of deep learning in the field of data science, particularly in big data analytics focuses on high-level feature extraction and abstraction as data representation based on the hierarchical learning process. Moreover, deep learning is also designed to tackle supervised learning problems for a wide variety of tasks. How to reliably solve unsupervised tasks with a similar degree of success is an important issue to address. Such studies are investigated based on the adoption of parallel computing, i.e., GPUs and CPUs clusters.Various algorithms are already applied to achieve the desired goals. For instance, convolutional neural network has established superior performance on large-scale image and video classification. The supervised learning (semi/weakly) methods have expressively enhanced the performance when only a small amount of annotated data is available. In addition, correlation analysis, transfer learning, multi-tasking have proven

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Section: Performance Improvementsmentioning

confidence: 99%