Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


How Does Logical Dynamics Assist Interdisciplinary Education and Research in Addressing Cognitive Challenges?
Mengqin Ning, Jiahong Guo
(pages: 1-6)

Inter-Corrective Meta-Dialogue on Constructive Impact of Trans-disciplinary Communication in Modern Education
Vinod Kumar Verma
(pages: 7-9)

Intergenerational Learning for Older and Younger Employees: What Should Be Done and Should Not?
Gita Aulia Nurani, Ya-Hui Lee
(pages: 10-15)

On the Ontological Notion of Education
Jeremy Horne
(pages: 16-24)

Research-Based Learning in Intergenerational Dialogue and Its Relationship to Education
Sonja Ehret
(pages: 25-29)

Role-Playing in Education: An Experiential Learning Framework for Collaborative Co-design
Cristo Leon, James Lipuma, Sirimuvva Pathikonda, Rafael Arturo Llaca Reyes
(pages: 30-38)

The Emergent Role of Artificial Intelligence as Tool in Conducting Academic Research
Bilquis Ferdousi
(pages: 39-46)

The Impact of Cybernetic Relationships Between Education and Work-Based Learning
Birgit Oberer, Alptekin Erkollar
(pages: 47-51)

The Notions of Education and Research
Nagib Callaos, Jeremy Horne
(pages: 52-62)

Towards Sustainable Legal Education Reform: Interdisciplinary and Transdisciplinary Approaches in Albania's Justice System
Adrian Leka, Brunilda Haxhiu
(pages: 63-67)

Transdisciplinary Research and the Gift Economy
Teresa Henkle Langness
(pages: 68-75)


 

Abstracts

 


ABSTRACT


Real-Time, On-Site, Machine Learning Identification Methodology of Intrinsic Human Cancers Based on Infra-Red Spectral Analysis – Clinical Results

Yaniv Cohen, Arkadi Zilberman, Ben Zion Dekel, Evgenii Krouk


In this work we present a real-time (RT), on-site, machine-learning based methodology for identifying intrinsic human cancers. The presented approach is reliable, effective, cost-effective and non-invasive and based on the Fourier transform infrared (FTIR) spectroscopy - a vibrational method with the ability to detect changes as a result of molecular vibration bonds using infrared (IR) radiation in human tissues and cells.

Medical IR optical system (IROS) is a table-top device for real-time tissue diagnosis that utilizes FTIR spectroscopy and the attenuated total reflectance (ATR) principle to accurately diagnose the tissue. The ATR measurement principle is performed utilizing a radiation source and a Fourier transform (FT) spectrometer. Information acquired and analyzed in accordance with this method provides accurate details of biochemical composition and pathologic condition of the tissue.

The combined device and method were used for RT diagnosis and characterization of normal and pathological tissues ex-vivo/ in-vitro. Therefore, the presented device can be used in close conjunction with a surgical procedure

The solution methodology is to select a set of "features" that can be used to differentiate between cancer, normal and other pathologies using an appropriate classifier. These features serve as spectral signatures (intensity levels) at specific values of measured FTIR-ATR spectral responses.

Excellent results were achieved by applying the following three machine learning (ML) based classification methods to 76 wet samples: Partial least square regression (PLSR) and Principal component regression (PCR)

Both of the methods (PCR & PLSR) show a high performance to classify "Cancer" or "non-Cancer"; Correct Classification: 100 %; Incorrect Classification: 0.0 %.

Naive Bayesian classifier (NBC); Shows a high performance to classify "Cancer" or "non-Cancer" (benign); Correct Classification: 100 %; Incorrect Classification: 0.0 %.

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