Planck–Shannon Classifier

A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells

Sungchul Ji, Beum Jun Park, John Stuart Reid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Planckian distribution equation (PDE), also called blackbody radiation-like equation, BRE, was derived from the Planck radiation formula by replacing its universal constants and temperature with free parameters, A, B, and C, resulting in y = A/(x + B)5/(eC/(x+B)Â −Â 1), where x is bin variable and y is frequency. PDE has been found to fit many long-tailed asymmetric histograms (LAHs) reported in various fields, including atomic physics, protein folding, single-molecule enzymology, whole-cell metabolism, brain neurophysiology, electrophysiology, decision-making psychophysics, glottometrics (quantitative study of words and texts), sociology, econometrics, and cosmology (http://www.conformon.net/wp-content/uploads/2016/09/PDE_Vienna_2015.pdf ). The apparent universality of PDE is postulated to be due to the principle of wave-particle duality embodied in PDE that applies not only to quantum mechanics but also to macrophysics regardless of scales. In this paper, the new classification method referred to as the Planck–Shannon classifier (PSC) or the Planck–Shannon plot (PSP) is formulated based on the two functions, i.e., (i) the Planckian information of the second kind, IPS, and (ii) the Shannon entropy, H, that can be computed from PDE. PSC has been shown to successfully distinguish between the digital CymaScopic images generated from the sonified Raman signals measured from normal and cancer cells in human brain tissues. PSC is a general purpose classifier and can be applied to classifying long-tailed asymmetric histograms generated by many physical, chemical, biological, physiological, psychological, and socioeconomical processes called Planckian processes, i.e., those processes that generate long-tailed asymmetric histograms fitting PDE.

Original languageEnglish (US)
Title of host publicationAdvances in Artificial Systems for Medicine and Education II
EditorsZhengbing Hu, Matthew He, Sergey V. Petoukhov
PublisherSpringer Verlag
Pages185-195
Number of pages11
ISBN (Print)9783030120818
DOIs
StatePublished - Jan 1 2020
Event2nd International Conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018 - Moscow, Russian Federation
Duration: Oct 6 2018Oct 8 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume902
ISSN (Print)2194-5357

Conference

Conference2nd International Conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018
CountryRussian Federation
CityMoscow
Period10/6/1810/8/18

Fingerprint

Classifiers
Cells
Brain
Neurophysiology
Atomic physics
Cosmology
Electrophysiology
Protein folding
Radiation
Quantum theory
Bins
Metabolism
Entropy
Decision making
Tissue
Molecules
Temperature

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Keywords

  • Digital CymaScope
  • Planckian distribution equation
  • Planckian information of the second kind
  • Planck–Shannon classifier
  • Shannon entropy
  • Sonified Raman spectral features of cancer cells

Cite this

Ji, S., Park, B. J., & Reid, J. S. (2020). Planck–Shannon Classifier: A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells. In Z. Hu, M. He, & S. V. Petoukhov (Eds.), Advances in Artificial Systems for Medicine and Education II (pp. 185-195). (Advances in Intelligent Systems and Computing; Vol. 902). Springer Verlag. https://doi.org/10.1007/978-3-030-12082-5_17
Ji, Sungchul ; Park, Beum Jun ; Reid, John Stuart. / Planck–Shannon Classifier : A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells. Advances in Artificial Systems for Medicine and Education II. editor / Zhengbing Hu ; Matthew He ; Sergey V. Petoukhov. Springer Verlag, 2020. pp. 185-195 (Advances in Intelligent Systems and Computing).
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Ji, S, Park, BJ & Reid, JS 2020, Planck–Shannon Classifier: A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells. in Z Hu, M He & SV Petoukhov (eds), Advances in Artificial Systems for Medicine and Education II. Advances in Intelligent Systems and Computing, vol. 902, Springer Verlag, pp. 185-195, 2nd International Conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018, Moscow, Russian Federation, 10/6/18. https://doi.org/10.1007/978-3-030-12082-5_17

Planck–Shannon Classifier : A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells. / Ji, Sungchul; Park, Beum Jun; Reid, John Stuart.

Advances in Artificial Systems for Medicine and Education II. ed. / Zhengbing Hu; Matthew He; Sergey V. Petoukhov. Springer Verlag, 2020. p. 185-195 (Advances in Intelligent Systems and Computing; Vol. 902).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ji S, Park BJ, Reid JS. Planck–Shannon Classifier: A Novel Method to Discriminate Between Sonified Raman Signals from Cancer and Healthy Cells. In Hu Z, He M, Petoukhov SV, editors, Advances in Artificial Systems for Medicine and Education II. Springer Verlag. 2020. p. 185-195. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-12082-5_17