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


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


An Optimal Deep Learning Approach for Classification of Age Groups in Social Network

Anil Kumar Swain, Bunil Kumar Balabantaray, Jitendra Kumar Rout, Suneeta Satpathy


There is huge amount of data in social networks, where people post their opinion on a topic, or share their information. But people often don’t provide their personal data, like gender, age and other demographics. Research can be done on this data to develop applications of sentiment analysis, but the success rate is restricted by the number of words in the dictionaries as they do not consider all the words which reflect the sentiment in our messages as most of the communication on social networks is non-standard language with small messages. Moreover, with contemporary technology it is quite easy to create profile with false age, gender and location which provides criminals an easy way to deceive. Thus we can analyze the text messages posted by the user on social network platform. As per the research done so far, age is one of the important parameter in the user profile which reveals the important information about the typical behavior among same age group users. An analysis is done with more than 4000 tuples which contains relevant parameters like number of friends, length of message, number of likes, number of hash tags and comments are considered for the classification. In this study, we use the user profile information for the prediction of age group, which we collected using Facebook API. In this paper we classified the users into two age groups teenagers and adults using different Machine learning algorithms like deep convolutional neural networks, Multilayer perceptron, Random forest , SVM and Decision trees. Among all these algorithms deep convolutional neural network stands out to be the best among all of them reaching the best performance with an accuracy of 94%.

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