scholarly journals Conflict resolution using relation classification: High-level data fusion in data integration

2021 ◽  
pp. 14-14
Author(s):  
Zeinab Nakhaei ◽  
Ali Ahmadi ◽  
Arash Sharifi ◽  
Kambiz Badie
Keyword(s):  
Machine Learning ◽  
Conflict Resolution ◽  
Data Integration ◽  
Data Fusion ◽  
Estimated Parameters ◽  
True Value ◽  
Level Data ◽  
Fusion Methods ◽  
Relation Classification ◽  
High Level

The aim of conflict resolution in data integration systems is to identify the true values from among different and conflicting claims about a single entity provided by different data sources. Most data fusion methods for resolving conflicts between entities are based on two estimated parameters: the truthfulness of data and the trustworthiness of sources. The relations between entities are however an additional source of information that can be used in conflict resolution. In this article, we seek to bridge the gap between two important broad areas, relation estimation and truth discovery, and to demonstrate that there is a natural synergistic relationship between machine learning and data fusion. Specifically, we use relational machine learning methods to estimate the relations between entities, and then use these relations to estimate the true value using some fusion functions. An evaluation of the results shows that our proposed approach outperforms existing conflict resolution techniques, especially where there are few reliable sources.

A Threat Assessment Method Based on Hierarchies and Modules

2014 ◽  
Vol 18 (1) ◽  
pp. 93-99
Author(s):  
Fang Deng ◽  
◽  
Xinan Liu ◽  
Zhihong Peng ◽  
Jie Chen
Keyword(s):  
Data Fusion ◽  
Threat Assessment ◽  
Assessment Method ◽  
Fusion Technology ◽  
Low Level ◽  
Information Classification ◽  
Level Data ◽  
High Level

With the development of low-level data fusion technology, threat assessment, which is a part of high-level data fusion, is recognized by an increasing numbers of people. However, the method to solve the problem of threat assessment for various kinds of targets and attacks is unknown. Hence, a threat assessment method is proposed in this paper to solve this problem. This method includes tertiary assessments: information classification, reorganization, and summary. In the tertiary assessments model, various threats with multi-class targets and attacks can be comprehensively assessed. A case study with specific algorithms and scenarios is shown to prove the validity and rationality of this method.

High Level Data Fusion on a Multimodal Interactive Applications Platform

2008 ◽  
Author(s):  
Olga Vybornova ◽  
Hildeberto Mendona ◽  
Jean-Yves Lionel Lawson ◽  
Benoit Macq
Keyword(s):  
Data Fusion ◽  
Interactive Applications ◽  
Level Data ◽  
High Level

General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences

2019 ◽  
pp. 51-79 ◽  
Author(s):  
Agnieszka Smolinska ◽  
Jasper Engel ◽  
Ewa Szymanska ◽  
Lutgarde Buydens ◽  
Lionel Blanchet
Keyword(s):  
Data Fusion ◽  
Life Sciences ◽  
Level Data ◽  
High Level

scholarly journals UV-Vis and CIELAB Based Chemometric Characterization of Manihot esculenta Carotenoid Contents

2017 ◽  
Vol 14 (4) ◽  
Author(s):  
Telma Afonso ◽  
Rodolfo Moresco ◽  
Virgilio G. Uarrota ◽  
Bruno Bachiega Navarro ◽  
Eduardo da C. Nunes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vitamin A ◽  
Data Fusion ◽  
Vitamin A Deficiency ◽  
Model Performance ◽  
Total Carotenoids ◽  
Cassava Root ◽  
Level Data ◽  
Uv Visible ◽  
Single Data

AbstractVitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples.

scholarly journals Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor

2020 ◽  
Author(s):  
Yannick Weesepoel ◽  
Martin Alewijn ◽  
Michiel Wijtten ◽  
Judith Müller-Maatsch
Keyword(s):  
Data Fusion ◽  
Olive Oil ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Food Fraud ◽  
Olive Oils ◽  
Level Data ◽  
High Level

Abstract Background Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable. Objective Prospecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs. Method EVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios. Results: By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%. Conclusions Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results. Highlights Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.

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