Journal of
Systemics, Cybernetics and Informatics

ISSN: 1690-4524 (Online)

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Editorial Advisory Board's Chair
William Lesso

Nagib C. Callaos

Sponsored by
The International Institute of
Informatics and Systemics

Editorial Advisory Board


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No Warranty Express or Implied: Why Do We Have So Many Problems With the Computer Systems That Pervade Our Lives?
John W. Coffey
(pages: 1-6)

Can You Hear Me Now? An Innovative Approach to Assess and Build Connections with Online Learner’s
Tina M. Serafini, Risa Blair
(pages: 7-11)

End-to-end Security with Translation
Kevin E. Foltz
(pages: 12-17)

(Assistive) Technology at the Point of Instruction: Barriers and Possibilities
Lorayne Robertson
(pages: 18-24)

Supplementing Multiple Modalities and Universal Design in Learning with Goal-Setting
Russell Jay Hendel
(pages: 25-30)

Experts Informing Experts
Robert Hammond
(pages: 31-35)

Internet of Things – A New Epistemic Object
Rolf Dornberger, Terry Inglese, Safak Korkut
(pages: 36-44)

An Experiment in Interdisciplinary STEM Education: Insights from the Catholic Intellectual Tradition
Fr. Joseph R. Laracy, Thomas Marlowe, Fr. Gerald J. Buonopane
(pages: 45-53)

Big History Understanding of Complexity, Informatics and Cybernetics
John L. Motloch
(pages: 54-60)

Flourishing Organizations
Maria Jakubik
(pages: 61-72)

Pros & Cons of Smart ICT in Some Governmental Applications
Dusan Soltes
(pages: 73-75)

Information Exchange in Vehicles Ad-Hock Networks
Tomas Zelinka
(pages: 76-80)

Living in a Digital World: Improving Skills to Meet the Challenges of Digital Transformation Through Authentic and Game-Based Learning
Margit Scholl, Frauke Fuhrmann
(pages: 81-86)

Psychotherapy via the Internet as a Novel Tool for Clinical Use
Ulrich Sprick
(pages: 87-94)

Technology Intercepts for Cyber Security Applied to Critical Infrastructures
Mario La Manna
(pages: 95-100)

“And Then a Miracle Occurs …” – Engaging the Challenge of Operationalizing Theories of Success in Digital Transformation
Michael Von Kutzschenbach
(pages: 101-105)

Multidisciplinary Learning Extends Communication Skill, and Helps Cross Cultural Understandings: Biomedical Engineering
Shigehiro Hashimoto
(pages: 106-112)

Integrating Teaching, Research and Problem Solving: An Experience in Progress in the Mucuri Valley Region (Brazil)
Leônidas Conceição Barroso
(pages: 113-118)

Meeting Learning Challenges in Product Design Education with and through Additive Manufacturing
William Lavatelli Kempton, Steinar Killi, Andrew Morrison
(pages: 119-129)

Creating and Using Symbolic Mental Structures via Piaget’s Constructivism and Popper’s Three Worlds View with Falsifiability to Achieve Critical Thinking by Students in the Physical Sciences
Matthew E. Edwards
(pages: 130-134)

Creativity in Higher Education: Comparative Genetic Analyses on the Dopaminergic System in Relation to Creativity, Addiction, Schizophrenia in Humans and Non-Human Primates
Bernard Wallner, Sonja Windhager, Katrin Schaefer, Martin Fieder
(pages: 135-142)





Parallel Prediction of Stock Volatility

Priscilla Jenq, John Jenq

Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows over time and if these highs and lows fluctuate wildly, then it is considered a high volatile stock. Such a stock is considered riskier than a stock whose volatility is low. Although highly volatile stocks are riskier, the returns that they generate for investors can be quite high. Of course, with a riskier stock also comes the chance of losing money and yielding negative returns. In this project, we will use historic stock data to help us forecast volatility. Since the financial industry usually uses S&P 500 as the indicator of the market, we will use S&P 500 as a benchmark to compute the risk. We will also use artificial neural networks as a tool to predict volatilities for a specific time frame that will be set when we configure this neural network. There have been reports that neural networks with different numbers of layers and different numbers of hidden nodes may generate varying results. In fact, we may be able to find the best configuration of a neural network to compute volatilities. We will implement this system using the parallel approach. The system can be used as a tool for investors to allocating and hedging assets.

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