Honey dataset standard using hyperspectral imaging for machine learning problems. Noviyanto, A. & Abdullah, W. H. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 473-477, Aug, 2017.
Honey dataset standard using hyperspectral imaging for machine learning problems [pdf]Paper  doi  abstract   bibtex   
Hyperspectral imaging has been rarely investigated for honey analyses, on the contrary to the optical spectroscopy which is widely investigated. The essential missing component to kick start this research is a standard honey hyperspectral images, called hypercubes, dataset. This paper proposes a systematic procedure for the preparation of the first honey hypercube dataset using hyperspectral imaging. Moreover, a scalable and flexible dataset module is introduced to ease the interaction between raw hypercube data and machine learning software. The developed dataset greatly benefits researchers to progress on the research of honey analysis including constituents prediction and types classification using hyperspectral imaging and machine learning.
@InProceedings{8081252,
  author = {A. Noviyanto and W. H. Abdullah},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Honey dataset standard using hyperspectral imaging for machine learning problems},
  year = {2017},
  pages = {473-477},
  abstract = {Hyperspectral imaging has been rarely investigated for honey analyses, on the contrary to the optical spectroscopy which is widely investigated. The essential missing component to kick start this research is a standard honey hyperspectral images, called hypercubes, dataset. This paper proposes a systematic procedure for the preparation of the first honey hypercube dataset using hyperspectral imaging. Moreover, a scalable and flexible dataset module is introduced to ease the interaction between raw hypercube data and machine learning software. The developed dataset greatly benefits researchers to progress on the research of honey analysis including constituents prediction and types classification using hyperspectral imaging and machine learning.},
  keywords = {geophysical image processing;hyperspectral imaging;learning (artificial intelligence);honey dataset standard;machine learning problems;standard honey hyperspectral images;honey hypercube dataset;scalable dataset module;flexible dataset module;machine learning software;constituent prediction;Hypercubes;Feature extraction;Hyperspectral imaging;Standards;Databases;Metadata;Containers},
  doi = {10.23919/EUSIPCO.2017.8081252},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344824.pdf},
}
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