Artificial intelligence-assisted data fusion

Xuyin Ding; Yecheng Zhang; Hossam Haick; Min Zhang

doi:10.1016/B978-0-443-15684-7.00042-7

Artificial intelligence-assisted data fusion

Xuyin Ding, Yecheng Zhang, Hossam Haick, Min Zhang

School of Chemistry and Molecular Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

Data fusion is capable of addressing imperfect raw data by fusing reliable, valuable and accurate information. Artificial intelligence (AI) methods are significantly advantageous for analyzing and fusing large-scale datasets from multiple sources and/or modalities. In this chapter, we will discuss AI-assisted data fusion beginning with data acquisition to AI algorithms, data fusion methods, and the applications and challenges of contemporary AI for data fusion.

Original language	English
Title of host publication	Nature-Inspired Sensors
Publisher	Elsevier
Pages	549-560
Number of pages	12
ISBN (Electronic)	9780443156847
ISBN (Print)	9780443156854
DOIs	https://doi.org/10.1016/B978-0-443-15684-7.00042-7
State	Published - 1 Jan 2024

Keywords

Artificial intelligence
data acquisition
data fusion

Access to Document

10.1016/B978-0-443-15684-7.00042-7

Cite this

@inbook{76020d6e581e455cb4855d076992a3d1,

title = "Artificial intelligence-assisted data fusion",

abstract = "Data fusion is capable of addressing imperfect raw data by fusing reliable, valuable and accurate information. Artificial intelligence (AI) methods are significantly advantageous for analyzing and fusing large-scale datasets from multiple sources and/or modalities. In this chapter, we will discuss AI-assisted data fusion beginning with data acquisition to AI algorithms, data fusion methods, and the applications and challenges of contemporary AI for data fusion.",

keywords = "Artificial intelligence, data acquisition, data fusion",

author = "Xuyin Ding and Yecheng Zhang and Hossam Haick and Min Zhang",

year = "2024",

month = jan,

day = "1",

doi = "10.1016/B978-0-443-15684-7.00042-7",

language = "英语",

isbn = "9780443156854",

pages = "549--560",

booktitle = "Nature-Inspired Sensors",

publisher = "Elsevier",

address = "荷兰",

}

TY - CHAP

T1 - Artificial intelligence-assisted data fusion

AU - Ding, Xuyin

AU - Zhang, Yecheng

AU - Haick, Hossam

AU - Zhang, Min

PY - 2024/1/1

Y1 - 2024/1/1

N2 - Data fusion is capable of addressing imperfect raw data by fusing reliable, valuable and accurate information. Artificial intelligence (AI) methods are significantly advantageous for analyzing and fusing large-scale datasets from multiple sources and/or modalities. In this chapter, we will discuss AI-assisted data fusion beginning with data acquisition to AI algorithms, data fusion methods, and the applications and challenges of contemporary AI for data fusion.

AB - Data fusion is capable of addressing imperfect raw data by fusing reliable, valuable and accurate information. Artificial intelligence (AI) methods are significantly advantageous for analyzing and fusing large-scale datasets from multiple sources and/or modalities. In this chapter, we will discuss AI-assisted data fusion beginning with data acquisition to AI algorithms, data fusion methods, and the applications and challenges of contemporary AI for data fusion.

KW - Artificial intelligence

KW - data acquisition

KW - data fusion

UR - https://www.scopus.com/pages/publications/85213181292

U2 - 10.1016/B978-0-443-15684-7.00042-7

DO - 10.1016/B978-0-443-15684-7.00042-7

M3 - 章节

AN - SCOPUS:85213181292

SN - 9780443156854

SP - 549

EP - 560

BT - Nature-Inspired Sensors

PB - Elsevier

ER -

Artificial intelligence-assisted data fusion

Abstract

Keywords

Access to Document

Other files and links

Cite this