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Information Fusion
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Published on Jan 1, 2019in Information Fusion 6.64
Francesco Cauteruccio3
Estimated H-index: 3
(University of Calabria),
Giancarlo Fortino31
Estimated H-index: 31
(University of Calabria)
+ 5 AuthorsM. Torres Vega1
Estimated H-index: 1
(Ghent University)
Heterogeneous wireless sensor networks are a source of large amount of different information representing environmental aspects such as light, temperature, and humidity. A very important research problem related to the analysis of the sensor data is the detection of relevant anomalies. In this work, we focus on the detection of unexpected sensor data resulting either from the sensor system itself or from the environment under scrutiny. We propose a novel approach for automatic anomaly detection ...
1 Citations Source Cite
Published on Mar 10, 2019
Nor Hanimah Kamis3
Estimated H-index: 3
,
Francisco Chiclana46
Estimated H-index: 46
,
Jeremy Levesley13
Estimated H-index: 13
Source Cite
Published on Aug 17, 2018in Information Fusion 6.64
Ning Chen1
Estimated H-index: 1
(East China University of Science and Technology)
Abstract Similarity networks contain important topological features and patterns critical to understanding interactions among samples in a large dataset. To create a comprehensive view of the interactions within a dataset, the Similarity Network Fusion (SNF) technique has been proposed to fuse the similarity networks based on different data types into one similarity network that represents the full spectrum of underlying data. In this paper, a modified version of SNF, which is named as Contextua...
3 Citations Source Cite
Published on Mar 14, 2019in Information Fusion 6.64
Dongxiang Zhang1
Estimated H-index: 1
(Zhejiang University),
Rui Cao1
Estimated H-index: 1
(University of Electronic Science and Technology of China),
Sai Wu1
Estimated H-index: 1
(Zhejiang University)
Abstract Visual question answering automatically answers natural language questions according to the content of an image or video. The task is challenging because it requires the understanding of semantic information in the textual and visual channels, as well as their interplay. A typical solver is composed of three components: feature extraction from singular modality, feature fusion between visual and textual channels, and answer prediction based on the learnt joint representation. Among them...
1 Citations Source Cite
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