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Advances in Science, Technology & Innovation
IEREK Interdisciplinary Series for Sustainable Development

Miroslav Mateev
Panikkos Poutziouris Editors

Creative Business and

Social Innovations
for a Sustainable Future
Proceedings of the 1st American University in the Emirates
International Research Conference—Dubai, UAE 2017
Advances in Science, Technology & Innovation
IEREK Interdisciplinary Series for Sustainable
Development

Series editor
Mourad Amer

Editorial Board Members

Hassan Abdalla
Md. Abdul Mannan
Chaham Alalouch
Sahar Attia
Sofia Natalia Boemi
Hocine Bougdah
Emmanuel Bozonnet
Luciano De Bonis
Dean Hawkes
Stella Kostopoulou
Yasser Mahgoub
Saleh Mesbah Elkaffas
Nabil Mohareb
Iman O. Gawad
Mieke Oostra
Gloria Pignatta
Anna Laura Pisello
Federica Rosso
Biswajeet Pradhan
Advances in Science, Technology & Innovation (ASTI) is a series of peer-reviewed books
based on the best studies on emerging research that redefines existing disciplinary boundaries
in science, technology and innovation (STI) in order to develop integrated concepts for
sustainable development. The series is mainly based on the best research papers from various
IEREK and other international conferences, and is intended to promote the creation and
development of viable solutions for a sustainable future and a positive societal transformation
with the help of integrated and innovative science-based approaches. Offering interdisciplinary
coverage, the series presents innovative approaches and highlights how they can best support
both the economic and sustainable development for the welfare of all societies. In particular,
the series includes conceptual and empirical contributions from different interrelated fields of
science, technology and innovation that focus on providing practical solutions to ensure food,
water and energy security. It also presents new case studies offering concrete examples of how
to resolve sustainable urbanization and environmental issues. The series is addressed to
professionals in research and teaching, consultancies and industry, and government and
international organizations. Published in collaboration with IEREK, the ASTI series will
acquaint readers with essential new studies in STI for sustainable development.

More information about this series at http://www.springer.com/series/15883

Miroslav Mateev
Panikkos Poutziouris
Editors

Creative Business and Social

Innovations for a Sustainable
Future
Proceedings of the 1st American University
in the Emirates International Research
Conference—Dubai, UAE 2017

123
Editors
Miroslav Mateev Panikkos Poutziouris
American University in the Emirates UCLan Cyprus
Dubai, United Arab Emirates Larnaka, Cyprus

ISSN 2522-8714 ISSN 2522-8722 (electronic)

Advances in Science, Technology & Innovation
IEREK Interdisciplinary Series for Sustainable Development
ISBN 978-3-030-01661-6 ISBN 978-3-030-01662-3 (eBook)
https://doi.org/10.1007/978-3-030-01662-3

Library of Congress Control Number: 2018957633

© Springer Nature Switzerland AG 2019

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not
imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and
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The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed
to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty,
express or implied, with respect to the material contained herein or for any errors or omissions that may have been
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The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface

The 1st American University in the Emirates International Research Conference (AUEIRC)
held on November 15–16, 2017, has become one of the major events of the year in the field of
creative business and social innovation. AUEIRC 2017 was organized under the patronage
of His Excellency Sultan bin Saeed Al Mansouri, Cabinet Member and Minister of Economy
of UAE. This conference aims to provide a vibrant platform for academicians, researchers,
scientists, and industrial practitioners from all over the world to meet, exchange ideas and
network, and discuss the challenges and opportunities for businesses in the contemporary
world. The proceedings will be published in the Advances in Science, Technology & Inno-
vation book series of Springer.
The general aim of AUEIRC 2017 is to promote international collaboration in education
and research in all fields of creative business and innovation that can scale for the benefit of
communities. The International Scientific Committee comprises over 80 international experts
in various fields as per the themes of the conference. The conference keynote speakers include
Suzanne Trager Ortega, President of the Council of Graduate Schools (USA), Prof. Galal
Hassan Galal-Edeen (University of London, UK), and Prof. Ali Emrouznejad (Aston
University, UK).
The scope of AUEIRC 2017 includes a broad range of topics on areas that contemporary
society. We have received 231 abstracts and 108 full papers from 42 countries in six different
tracks, namely Creative Business and Social Innovation, Education and Social Innovation,
Media for Smart Cities, Creative Industries and Social Innovation, Governance and Legisla-
tion, and Smart Technologies and Innovation. The panels are divided based on tracks and all
papers are presented in 24 sessions. Each session has a chairperson, while each paper has a
discussant. A double-blind peer review process enabled 106 papers to be accepted for pub-
lication (in two edited volumes) in Springer. On behalf of the AUEIRC 2017 Standing
Committee, we would like to thank all the referees, track chairs, discussions, and paper
authors.
AUEIRC has been planned to enable postgraduate students from the American University
in the Emirates and other academic institutions to participate in the conference and share their
research experience with an international community of academics and industry experts. The
best paper (in terms of quality and practicality) presented at the conference was awarded
US $1000. The recipient of the Best Paper Award was the paper titled “Optimizing Turbo
Codes for Secret Key Generation in Vehicular Ad Hoc Networks.” This paper’s
authors/co-authors are Dr. Dhouha Kbaier Ben Ismail (University of Bedfordshire), Dr. Petros
Karadimas (University of Glasgow), Dr. Gregory Epiphaniou (University of Bedfordshire),
and Dr. Haider Al-Khateeb (University of Bedfordshire).
On behalf of the AUEIRC 2017 Standing Committee, we would like to thank everyone
who made the conference a reality. In particular, we would like to thank Prof. Muthanna
(Conference Chairperson), who contributed all resources at his disposal to ensure the high
standard of the conference. We also thank our Provost Prof. Abhilasha for her contributions
and for attending meetings to guarantee that the conference is on track. We likewise extend our
gratitude to the entire AUE Board of Trustees, who were present on campus during the

v
vi Preface

conference, for their support. We also thank the members of the conference steering committee
for their hard work, dedication, and continuous support throughout the preparation and
implementation of the conference’s activities. Moreover, we are grateful to the event man-
agement, IT, auxiliary services, media, security, PR, and protocol teams. We likewise extend
our thanks to all faculty and staff members from different committees for their support in
organizing the conference and ensuring its success.
Overall, we express our heartfelt thanks to the keynote speakers, who managed their busy
schedules and graciously agreed to deliver inspiring messages. We believe that AUEITC 2017
was a high-quality event with remarkable success, although you must judge this matter for
yourselves. We are optimistic that you will enjoy reading the collection of papers included in
this book.

Dubai, United Arab Emirates Prof. Miroslav Mateev

Larnaka, Cyprus Prof. Panikkos Poutziouris
Conference Steering Committee Members

Mahir Albanna
Arafat Al Naim
Konrad Gunesch
Anthony Ijeh
Golam Mustafa
Zaire Ruanto
Asma Salman
Assem Tharwat

vii
Contents

A MRI View of Brain Tumor Outcome Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Cristiana Neto, Inês Dias, Maria Santos, Victor Alves, Filipa Ferraz,
João Neves, Henrique Vicente, and José Neves
Achieving Cognitive Skills in Multimedia Through Revised
Bloom Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Reshmy Krishnan
An Estimation for Bitcoin Price Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Murat Akbalık, Melis Zeren, and Ömer Sarıgül
An Optimization Model for Solving Stochastic Cooperative Games . . . . . . . . . . . . 33
Assem Tharwat, Marwa Sabry, and Ihab El-Khodary
Banking Sector Performance and Economic Growth: An Empirical
Evidence of UAE Islamic Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Mosab I. Tabash
Challenges of Business Schools to Implement Accreditation
Standards: Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Doaa Wafik and Assem Tharwat
Control Environment, Risk Assessment and Monitoring
in United Arab Emirates Businesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Hikmat AlRawi, Narcisa Roxana Mosteanu, and Ibaa H. Alrawi
Creative Contributions and Solutions to Dubai’s Challenges
and Commitments as a Smart City: Transnationalizing and Personalizing
Global Standards of Excellence in International Education . . . . . . . . . . . . . . . . . . . 67
Konrad Gunesch
Credit Risk Assessment Using Decision Tree and Support Vector
Machine Based Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Abhijeet Guha Roy and Siddhaling Urolagin
Death and Factors of Adjustment Among Bereaved Parents . . . . . . . . . . . . . . . . . . 85
Mais Al-Nasa’h and Kimberly Asner-Self
Determinants of Entry Modes Choice for MNEs: Exploring Major
Challenges and Implications for Saudi Arabia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Young-Eun Park, Alawiya Allui, and Raneem AlSelaimi
Developing an Input Oriented Data Envelopment Analysis Model
with Fuzzy Uncertainty in Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Assem Tharwat, Basma El-Demerdash, and Ihab El-Khodary
Digital Currencies and the Power Shift in the Economy . . . . . . . . . . . . . . . . . . . . . 123
Asma Salman

ix
x Contents

Evaluating Inclination of Youth to Start Enterprise: A Study in Oman . . . . . . . . . 133

Bilal Ahmad Pandow and Ali Salem Omar
Evidence-Based Tobacco Control Mobile Apps Based on Federally Funded
Research Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Alexander V. Prokhorov, Salma K. Marani, and Mario R. Luca
Family Mediation in England and Wales: A Focus on Children . . . . . . . . . . . . . . . 149
Amel Ketani
Forecasting of the Internet Usage in the World Regions . . . . . . . . . . . . . . . . . . . . . 155
Ksenija Dumičić and Berislav Žmuk
Impact of FDI on GDP and Unemployment in Macedonia Compared
to Albania and Bosnia and Herzegovina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Agim Mamuti and Mehmed Ganic
Insurance Coverage for IPRs: Possible Solution for Businesses
in the GCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Hanan Almawla
Integration of Applied Linguistics and Business Communication:
A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Janet Madhu
Mapping Creative Pedagogies in Fashion Design Education—Fashion
Design Program at American University in the Emirates as a Case Study . . . . . . . 189
Shlagha Agarwal
Modern Versus Traditional Assets Allocation Models on Imperfect
Markets—Which One Is More Convenient for Investors? . . . . . . . . . . . . . . . . . . . . 199
Jelena Janjusevic
Online Summative Assessment and Its Impact on Students’ Academic
Performance, Perception and Attitude Towards Online Exams:
University of Sharjah Study Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Hussein M. Elmehdi and Al-Mehdi Ibrahem
Risk Exposure, Liquidity and Bank Performance: New Evidence
from the Recent Financial Crisis of 2007–2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Miroslav Mateev and Petko Bachvarov
Significance of Education, Emotional Intelligence, Experience
and Mediating Role of Ethical Values in Exceptional
Executive Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Tahir Masood Qureshi
Smart Art for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Heather Shipman
Social Identification, Brand Image and Customer Satisfaction
as Determinants of Brand Love . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Abdullah Al-Haddad
State Responsibility in Combating Dangerous Climate Change:
The Critical Role of Domestic and International Justice . . . . . . . . . . . . . . . . . . . . . 263
Mahir Al Banna
The First Superbrand in Knowledge Economy in the UAE:
Morison Menon in a Mission of Building Better Business Globally. . . . . . . . . . . . . 275
Nikola Perovic
Contents xi

The Relationship Between EFL Teachers’ Attitudes Towards CLT

and Perceived Difficulties of Implementing CLT in Language Classes
in Omani Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
M. Al Kalbani, J. Solabarrieta, and A. Bin Touq
The Role of Sustainable Management in Small and Medium Enterprises,
with Reference to Interior Design Firms in the Kingdom of Bahrain . . . . . . . . . . . 291
Hisham Al-Azzawi and Amna Hasan
The Way to Impartial Justice: Carving an Approach to Effective Decision
Making Process, the Role of Individual Judges and Problems Faced
by the International Court of Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
Adit Sharma and Rajshree Acharya
 A MRI View of Brain Tumor Outcome
Prediction

Cristiana Neto , Inês Dias , Maria Santos , Victor Alves ,

Filipa Ferraz , João Neves , Henrique Vicente , and José Neves

Abstract based on Magnetic Resonance Imaging (MRI) data or

On the one hand, cancer and tumor are one of the most knowledge (if the data is presented in context) that allows
feared terms in today’s society. It refers to an unstable for brain tumor outcome prediction. It describes an
growth of cells that potentially invade the surrounding innovative approach to cater for brain illness where Logic
tissues and may eventually lead to edema or even death. Programming comes in support of a computational
On the other hand, the term tumor is often misleading approach based on Case Based Reasoning. An attempt
since people assume that it is the same as cancer, but this is made to predict whether a patient will die or survive
is not necessarily true. A cancer is a particularly with or without a tumor, where the data or knowledge
threatening type of tumor. The word tumor simply refers may be of type unknown, incomplete or even self-
to a mass, and in particular a brain tumor is a mass contradictory.
located in the patient’s brain that may seriously threaten

his/her life. Thus, it is crucial to study which factors may Keywords
influence the outcome of a brain tumor to improve the Brain tumor Feature extraction Brain tumor outcome

given treatment or even make the patient more contented. prediction Logic programming Knowledge repre-

Therefore, this study presents a decision support system sentation and reasoning Case-based reasoning
3D slicer Magnetic resonance imaging
C. Neto
I. Dias
M. Santos
Departamento de Informática, Universidade do Minho, Braga,
Portugal
e-mail: crisneto95@gmail.com
I. Dias 1 Introduction
e-mail: ines3dias@gmail.com
M. Santos A brain tumor is characterized by the abnormal multiplica-
e-mail: mjmsantos95@gmail.com tion of some brain cells, resulting in a mass that hereafter
V. Alves
F. Ferraz
H. Vicente
J. Neves (&) will start to compress and injure the normal cells of the
Centro Algoritmi, Universidade do Minho, Braga, Portugal brain.
e-mail: jneves@di.uminho.pt According to tumor classification, it can be either pri-
V. Alves mary, a case where tumors start their formation in the brain
e-mail: valves@di.uminho.pt itself, or secondary, where tumors begin in another part of
F. Ferraz the body and spread till the brain through metastasis. The
e-mail: filipatferraz@gmail.com former ones can be considered benign or malignant, whereas
H. Vicente the newest can only be described as malignant, where the
e-mail: hvicente@uevora.pt word cancer is only used when the tumor was caused by
J. Neves malignant cells [1, 2].
Mediclinic Arabian Ranches, Dubai, 282602 United Arab Besides that, tumor localization is also crucial, as it
Emirates
appears to be related to prognosis. Bearing in mind that
e-mail: joaocpneves@gmail.com
every tumor type manifests different symptoms that vary
H. Vicente
according to the affected brain zone, either from headaches,
Departamento de Química, Escola de Ciências e Tecnologia,
Centro de Química de Évora, Universidade de Évora, Évora, vision problems, mental illnesses or vomiting, the treatment
Portugal may include a combination of surgery, radiotherapy or

© Springer Nature Switzerland AG 2019 1

M. Mateev and P. Poutziouris (eds.), Creative Business and Social Innovations for a Sustainable Future,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-01662-3_1
2 C. Neto et al.

chemotherapy. Also, a recurring process for the prognosis is was not declared explicitly. Under this formalism, every
to measure the patient’s performance status. This is an program is associated with a set of abducibles [5, 6], given
attempt to quantify cancer patients’ general well-being and here in the form of exceptions to the extensions of the
activities of daily life, using evaluation systems entitled as predicates that make the program, i.e., clauses of the form:
Karnofsky score, that runs from 100 to 0, where 100 denotes
perfect health and 0 means death. exceptionp1 ; . . .; exceptionpj ð0  j  kÞ;
Despite their diverse histological types, most brain being k an integer number
tumors cause brain edema (excess accumulation of fluid in
the intracellular or extracellular spaces of the brain), which is that stand for data, information or knowledge that cannot be
a significant cause of patient morbidity and mortality [3]. ruled out. On the other hand, clauses of the type:
Indeed, due to the large amount of tumor-related deaths, it is
?ðp1 ; . . .; pn ; not q1 ; . . .; not qm Þ ðn; m  0Þ
crucial to invest in auxiliary diagnostic studies such as
Computed Tomography (CT) or Magnetic Resonance also named invariants, allows one to set the context under
Imaging (MRI), as a consequence of a large variance in some which the universe of discourse has to be understood. The
tumor characteristics, namely in its size, shape, location, term scoringvalue stands for the relative weight of the
edema or intensities [4]. extension of a specific predicate with respect to the exten-
In image processing, feature selection and extraction sions of peers ones that make the inclusive or global
methods are related to dimensionality reduction, where the program.
selected features contain the relevant information avoiding In order to evaluate the data, information or knowledge’s
redundant and irrelevant ones. quality that may be associated to a logic program, an
assessment of it, denote as QoI, is given by a truth-value
ranging between 0 and 1 [9, 10]. Thus, QoIi = 1 when the
2 Knowledge Representation and Reasoning information is known (positive) or false (negative), and
QoIi = 0 if the information is unknown. Finally, for situa-
Many approaches to Knowledge Representation and Rea- tions where the extension of a given predicatei is taken from
soning have been proposed using the Logic Programming a set of terms, QoIi 2 ]0, 1[, i.e.:
(LP) epitome, namely in the area of Model Theory [5, 6] and
Proof Theory [7, 8]. In the present work, the Proof Theo- QoIi ¼ 1=Card ð1Þ
retical approach in terms of an extension to the LP language
is followed. An Extended Logic Program is, therefore, given where Card denotes the cardinality of the abducibles set for
by a finite set of clauses, in the form: i, if the abducibles set is disjoint. If the abducibles set is not

where the first clause stand for predicate’s closure, “,” disjoint, the clause’s set is given by C1Card þ


þ CCard
Card
,
denotes “logical and”, while “?” is a domain atom denoting under which the QoI’s evaluation takes the form:
falsity, the pi, qj, and p are classical ground literals, i.e.,
either positive atoms or atoms preceded by the classical QoIi1  i  Card ¼ 1=C1Card ; . . .; 1=CCard
Card
ð2Þ
negation sign ¬ [8]. Indeed, ¬ stands for a strong declaration
Card
that speaks for itself, and not denotes negation-by-failure, or where CCard is a card-combination subset, with Card ele-
in other words, a flop in proving a given statement, once it ments. For example, the logic program depicted below:
A MRI View of Brain Tumor Outcome Prediction 3

where ⊥ denotes a null value of the type unknown, stands where [ , m and for example [Axj, Bxj] stand for, respec-
for a logic program that denotes a particular universe of tively, set union, the cardinality of predicatei extension and
discourse in its initial form. Then, it is now possible to split the extremes of the interval where attribute attributej may, in
the abducible or exception set into the admissible clauses or principle, be situated.
terms and evaluate their QoIs. A pictorial view of this pro- In the present study both qualitative and quantitative
cess, in general terms, is given below as a pie chart (Fig. 1). data/information/knowledge are presented. Aiming at the
Under this setting, a new evaluation factor has to be quantification of the qualitative part and in order to make
considered, which will be denoted as DoC, that stands for easy the understanding of the process, it will be presented in
one’s confidence that the argument values or attributes of the a graphical form. Taking as an example a set of n issues
terms that make the extension of a given predicate, having regarding a particular subject, where there are k possible
into consideration their domains, fit into a given interval choices (e.g., absence, low, …, high and very high), an
[11]. The DoC is evaluated as shown in Fig. 2 and computed unitary area circle, split into n slices, was itemized. The
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi marks in the axis correspond to each of the possible options.
using DoC ¼ 1  Dl2 , where Dl stands for the argument
interval length, which was set in the interval [0, 1]. Thus, the Thus, if the answer to issue 1 is high the area correspondent
universe of discourse is engendered according to the infor- is ðk  1Þ=ðk  nÞ (Fig. 3a). Assuming that in the issue 2 are
mation presented in the extensions of such predicates, chosen the alternatives high and very high, the correspondent
according to productions of the type: area ranges between ½ðk  1Þ=ðk  nÞ; 1=n (Fig. 3b).
Finally, in issue n if no alternative is ticked, all the
[ hypotheses should be considered and the area varies in
predicatei  clausej ðð½Ax1 ; Bx1 ðQoIx1 ; DoCx1 ÞÞ; . . .;
1jm the range ½0; 1=n (Fig. 3c). The total area is the sum of the
partial ones (Fig. 3d). In some cases, similar responses to
ð½Axn ; Bxn ðQoIxm ; DoCxm ÞÞÞ :: QoIj :: DoCj
different issues have opposing impact in the subject in
ð3Þ consideration. Thus, the contribution of the items with

Fig. 1 QoI’s values for the

abducible set of clauses referred
to above. The clauses cardinality
set, K, is given by the expression
C Card þ C Card þ


þ C Card
Card ,
1 P2
where ni¼1 ðQoIi  pi Þ=n
denotes the attributes QoI’s
average of each clause or term. pi
stands for the relative weight of
attribute
Pi with respect to its peers,
being ni¼1 pi ¼ 1
4 C. Neto et al.

Fig. 2 Evaluation of the 1 mentioned above (3D Slicer is an open source

attributes’ Degree of Confidence DoC multi-platform software package for visualization and med-
ical image computing, and three-dimensional visualization,
widely used in tumor detection) [13, 14]. Therefore, from the
time when the dataset is loaded, we were able to work on the
axial, sagittal and coronal 2D views to construct a model of
the Region Of Interest (ROI) using its Editor module. On the
0 1 other hand, since the most detailed view regarding tumors is
Interval
Lenght
the axial (AX FLAIR), it was select in order to construct the
∆l brain model. For the ROI’s selection, the main used tool was
the LevelTracingEffect (Fig. 4), which allowed for the
negative impact on the subject in analysis is set as 1=n minus selection of previously labeled structures.
the correspondent area, i.e., ð1=n  ðk  1Þ=ðk  nÞÞ ¼ Following the selection mentioned above, the Surface
1=ðk  nÞ for issue 1, ½0; 1=ðk  nÞ for issue 2 and ½0; 1=n Models/Model Maker module was selected in order to create
for issue 3. 3D surface models from the segmented data. A new Model
Maker Parameter Set was created and an Input Volume that
is a segmented label map volume was itemized. Besides that,
3 Methods a new Models hierarchy was created to provide a structure to
contain the return models created from the input volume.
For this study we used The Cancer Genome Atlas Regarding the Model Maker Parameters, it was essential to
GlioBlastoma Multiforme (TCGA-GBM) data collection control the type of smoothing on the models, and in this case
[12].This image repository is a very large one and, as might the Laplacian Filter set to the value of 30 was used. Finally,
be expected, led to the extreme heterogeneity of the image after applying all these changes, it was possible to build the
data sets in terms of scanner modalities, manufacturers and 3D model visible in the Models module as well as the vol-
acquisition protocols. Thus, one of the difficulties in this ume and area’s estimations, presented in Fig. 5.
project was to find several records on different patients that
did not differ much in terms of image, using the same MR
modality whenever possible. On the other hand an advantage 4 Case Study
of using this data collection was the presence of documents
that contained very useful information about patients (e.g., The case study considers the knowledge base given in terms
age, gender, race) and their process (e.g., treatment, follow of the extensions of the relations (or tables) depicted in
ups). Fig. 6, which stand for a situation where one has to manage
The choice of features to be extracted was rather impor- information in order to evaluate brain tumor outcome pre-
tant in a way the patient’s outcome may be predicted. diction of patients with brain tumors. The Measurements
Therefore, the starting point of the process consisted in MRI table is populated with information from the data set con-
brain tumor images in which we could analyze the tumors taining features extracted from MRI brain images, as
volume and surface area, as well as edemas’ areas and explained before. The features involved in the Measure-
volumes. Besides that, some patient’s information was also ments table are Tumor Volume, Tumor Area, Edema Volume
available such as age, gender, Karnofsky score, medications and Edema Area. It is a two column table given in terms of
and treatments, as well as the outcomes that will serve as General Attributes of Brain Tumor Images, Tumor Mea-
label for the training dataset. surements and Edema Measurements. The table Patient
Primarily, the image dataset collected was transferred to Information has as attributes Age, Gender (Male—M—1,
3D Slicer software in order to proceed to the analysis Female—F—0) and the Karnofsky score; table Treatments

Fig. 3 A view of the qualitative

data/information/knowledge
processing
A MRI View of Brain Tumor Outcome Prediction 5

Fig. 4 The outcome of ROI’s computation using Editor Module of 3D Slicer

has as attributes Medication and Therapy. Although the quantitative data, whereas the table Treatments contains only
majority of the data present in the tables is known, there is qualitative one. Each one of the two attributes from the
also some incomplete, default and/or unknown (depicted Treatment table may fit into two classes, i.e., with regard to
through the symbol ⊥) data. It must be noted that, in terms the Medication attribute, the scale consists of Temodar,
of known data, the Measurements table contains solely Temozolomide, Avastin, Bevacizumab, Gleevac, whereas for

Fig. 5 Volume and Surface area computation using Models Module on 3D Slicer software
6 C. Neto et al.

Fig. 6 A knowledge base fragment for Brain Tumor Outcome Prediction for patients diagnosed with brain tumors

the the Therapy attribute one has Chemotherapy, In order to quantify the information present in the
Immunotherapy and Target Molecular Therapy. Such scales Treatments table the procedures already described above
are given in Fig. 7. were followed. Applying the algorithm presented in [11] to
A MRI View of Brain Tumor Outcome Prediction 7

little work is done offline. Undeniably, in almost all the

situations the work is performed at query time. The main
difference between this approach and the typical CBR one
relies on the fact that the cases have their arguments set in
the range [0, 1], a situation that is complemented with the
Fig. 7 Qualitative classes on Medication (a) and Therapies prospect of handling incomplete, unknown, or even
(b) attributes self-contradictory data, information or knowledge. Thus, the
classic CBR cycle was changed (Fig. 8), being the Case
the table or relation’s fields that make the knowledge base Base records given in terms of the pattern:
for brain tumor outcome prediction for patients with brain
tumor (Fig. 6), and looking to the DoCs values obtained, it is Case Records ¼ fRawdata ; Normalizeddata ; Descriptiondata g
possible to set the arguments of the predicate patients brain ð4Þ
tumor (pbt), that also describes the objective function with
The Descriptiondata field will not be object of attention in
respect to the problem being studied:
this study.
pbt: Age; Gender ; Karnofsky Score ; Treatment ; Tumor Measures ; Now, the algorithm given in [11] may be now applied to a
Edema Measures ! f0; 1g new case, that in this study denotes a possible clinical situ-
ation of a given patient, here assumed to have the feature
where 0 (zero) and 1 (one) denote, respectively, the truth vector Age = 45, Gender = 0, Karnofsky Score = 73, Treat-
values false and true. ment = ⊥, Tumor Measures = [0.17, 0.25], Edema Mea-
Applying the algorithm presented in [11] to a term sures = 0.73. One may get:
(clause) that presents feature vector Age = 38, Gender = 1,
Karnofsky Score = ⊥, Treatment = 0.14, Tumor Measures = 0.21, pbtnew case ððð0:38; 0:38Þð1; 1ÞÞ; . . .; ðð0:73; 0:73Þð1; 1ÞÞÞ :: 1
Edema Measures = [0.7, 1], one may get: :: 0:80

We are now in a position to use our approach to com- The new case, in the present form, is now compared with
puting grounded on Case Based Reasoning and proceed every retrieved case from the Case Base working a simi-
further with our study. larity function sim, given in terms of the average of the
modulus of the arithmetic difference between the arguments
of each case selected and those of the new case, with the
5 Case Based Reasoning results [18]:

Case Based Reasoning (CBR) stands for a computing retrievedcase1 ðððð0:77; 0:77Þð1; 1ÞÞ; . . .; ðð0:25; 0:75Þð1; 0:87ÞÞÞ :: 1 :: 0:83
methodology where the act of solving a given problem is ..
.
based on the consideration of the solutions of similar past
retrievedcasej ðððð0:69; 0:69Þð1; 1ÞÞ; . . .; ðð0:75; 0:82Þð1; 0:99ÞÞÞ :: 1 :: 0:81
ones [15, 16]. Therefore, cases are stored in a cases’ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
normalized cases that make the retrieved cluster
repository, and those that are similar (or close) to a new one
are used in the problem-solving process (Fig. 8).
Contrasting with other problem-solving strategies (e.g., Assuming that every attribute has equal weight for the
Decision Trees or Artificial Neural Networks), relatively sake of presentation, the dis(similarity), in terms of DoC,
8 C. Neto et al.

Fig. 8 The extended view of the CBR cycle [17]

new case!1 ;
between newcase and the retrievedcase 1 , i.e., disDoC The proposed model was tested on a real data set with
may be computed as follows: 262 cases, and its performance evaluated using the k-folds
cross validation technique [19]. In the implementation of the
k1  1k þ . . . þ k1  0:87k respective dividing procedures, ten executions were per-
new case!1 ¼
disDoC ¼ 0:14
6 formed for each one of them. Table 1 presents the confusion
Therefore, the sim(ilarity), i.e., simDoC matrix of the CBR model, where the values presented denote
newcase!1 is set as
1 − 0.14 = 0.86. Regarding QoI the procedure is similar, the average of 25 (twenty-five) experiments. A perusal to
Table 1 shows that the model accuracy was 82.8% (i.e., 217
returning simQoI
new case!1 ¼ 1. Thus, one may have: instances correctly classified in 262). Thus, from clinical
simQoI;DoC practice perspective, the predictions made by the CBR model
new case!1 ¼ 1  0:86 ¼ 0:86
are satisfactory, attaining accuracies higher than 80%.
These procedures may be extended to all retrieved cases Based on the confusion matrix it is possible to compute
leading to the most similar ones, i.e., the potential solutions to different metrics in order to evaluate the performance of the
the problem in study. This approach allows users to define the model, namely sensitivity (83.6%) and specificity (81.9%)
most appropriate similarity methods to address the problem [20, 21]. It denotes that the one’s model exhibits an
(i.e., it gives the user the possibility to narrow the number of acceptable performance in the assessment of Brain Tumor
selected cases with the increase of the similarity threshold). Outcome Prediction.
A MRI View of Brain Tumor Outcome Prediction 9

Table 1 The CBR’s model confusion matrix

Target Predictive
True (1) False (0)
True (1) 122 24
False (0) 21 95

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 Achieving Cognitive Skills in Multimedia
Through Revised Bloom Taxonomy

Reshmy Krishnan

Abstract 1 Introduction and Background

Students in middle east are having innovative skills in
multimedia and Human Computer Interaction (HCI) and An educational model known as Blooms Taxonomy [1] was
hence self-learning and critical thinking can be enhanced defined in 1956 to categorize objectives in education. In this
through the teaching of this subject through proper design model the criterions such as knowledge, application, analy-
of the course. To achieve cognitive learning objectives in sis, synthesis and evaluation were considered. In 2001 a
students, revised bloom taxonomy can be embedded modified version of this model was released which focuses
while designing the curriculum. Teaching activities dynamic conception of classification rather than the static
should be aligned to achieve self-learning and critical view of educational objectives.
thinking in students according to the revised bloom
taxonomy. Through a Project Based Learning
(PBL) method in multimedia and HCI course, the above 1.1 Revised Bloom Taxonomy
cognitive learning objectives can be developed in class-
room. In this paper the design and practice of a As per the revised version, categories are considered such as
multimedia and HCI curriculum which is taught in remember, understand, apply, analyze, evaluate and create.
Muscat College, Sultanate of Oman is described and When the learner does the processing of tasks such as
demonstrates how cognitive skills are achieved through choose, define, describe, give example, group, know, locate
the embedding of revised bloom taxonomy. The design of etc.… The remember category will be fulfilled. In assess-
the course took the students to the various concepts of ment, he can write definition, write facts, put label, attend
graphics techniques and practicals give them the exposure quiz as result of remember. workbook, worksheet, test etc.
towards realization of those techniques. The final project can be done through the reproduction due to remember. The
is developed through the integration of all these practical concepts or ideas can be described is known as under-
and tutorial experiences. The observation shows students standing. To understand the assessments, questions like” can
have applied their creativity with revised bloom taxon- you write in your own words, what could happen next, what
omy to obtain the goal in a wonderful way. was the main idea etc. can be passed to get the answers. The
queries for definition, explanation, example, summary etc.

Keywords are used to understand the idea of assessments. The next

Revised bloom taxonomy Project based learning(PBL) category which is applying the idea can be communicated

Multimedia Graphics Human computer interaction through the words like practice, produce, solve, show, apply
(HCI) Self-learning Critical thinking etc. Analyzing is possible with the help of entities such as
Cognitive learning chart, database, checklist, outline, questionnaire, report,
summary etc. in assessments. The following level is the
evaluation which can be achieved through the process of
checking, experimenting and hypothesizing.
Evaluation level will be followed after analyzing level. In
this level a decision or an action is justified through
R. Krishnan (&) the process checking, experimenting and hypothesizing.
Department of Computing, Muscat College, Muscat, Oman The session entitled like conclusion, report, survey,
e-mail: reshmy_krishnan@yahoo.co.in

© Springer Nature Switzerland AG 2019 11

M. Mateev and P. Poutziouris (eds.), Creative Business and Social Innovations for a Sustainable Future,
Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-01662-3_2
12 R. Krishnan

recommendation etc. will define evaluation level. This ses- • Human computer Interaction concepts and cognitive
sion can be developed through the questions “is there a psychology issues in HCI.
better solution to…? how would you feel if…? what are the • Requirement of user interface for real time systems.
pros and cons of…? how effective are…? etc. The verbs of • Design of user interface with a proper emphasis on the
this session consists of choose, compare, conclude, debate, visual aspects of presentation and interaction.
decide, justify, recommend tell why etc. • User testing of an interface.
All elements are combined in a new method and com- • Knowledge on standards for representing audio files in
piled the information in a different way in the creation level computers.
which is the final level of taxonomy. In this level an alter- • Knowledge on standards and issues during the imple-
native solution can be proposed or new ideas can be gen- mentation of static/dynamic visual input/output on
erated, new invention or design can be made. The concepts computers.
like blueprint, formula, invention, story board etc. provides • Practical experience in multimedia design and
the creation level in assessments. The enquiries can be “how implementation.
would you device your own way…? how many ways can
you…? can you see a possible solution to…? can you design User Interface
a…?, what would happen if…?” etc. The verbs which can
constitute the creation level comprised arrange, compose, • Motivations for human factors in design: safety-critical
design, device, formulate, construct etc. systems, industrial and commercial uses, office, home,
Multimedia and Human computer interaction (HCI) is a and entertainment applications.
vital domain for computer experts and software specialists. • The place of human factors, usability and interface
The approaches to integrate revised bloom taxonomy with design in the software life cycle.
the curriculum of Multimedia and HCI to bridge the gap • Adjusting the computing environment to the user (ac-
between the CS specialists and CS educators are presented in commodation of human diversity): cognition, perception
this paper. Here various implementations of multimedia and and physiology.
HCI which are there in curriculum and how they are tar- • Mechanisms of interaction with machines (I/O devices
geting CS educators are discussed [2]. These implementa- [mouse, keyboard, displays, …], interaction styles
tions include module outlines, reference books, tutorial and [command line, menus, GUIs, VR]).
practical which lead to project ideas. Paper explores how • Usability, completeness, consistency: the design of the
each outline integrates revised bloom taxonomy to recognize user interface.
Deepness of knowledge to be masters by learners. • Evaluating the user interface.
In the current approach of multimedia and HCI, psy- • Usability testing.
chological guidelines which should be followed in the • Use of interface design tools.
design of a user interface, technical details in the design,
testing details to be covered for evaluation etc. are discussed. Graphics
The paper points out how the structure of multimedia and
HCI curriculum supports computer science (CS) educators to • Colour and the production of colour on graphical output
teach a consistent design, evaluation and implementation of devices.
both software technology and user interface. • Graphical representation and techniques.
The remainder of this paper is as follows: in the next • File formats of static and dynamic images: standards,
section we will cover curriculum design of multimedia and uses, data compression, quality.
HCI courses, in Sect. 2, all aspects of multimedia and HCI • Principles of animation: model design, animation design,
teaching using the project based output; this includes edu- production.
cational as well as technical details. In Sect. 3, the effec-
tiveness of our approach is discussed before the conclusion Sound
and the future directions in Sect. 4.
• Auditory input and output: standards and techniques.
• Quality of service and usability in sound.
1.2 Curriculum Design of Multimedia and HCI
Course Multimedia authoring

Course structure of the course multimedia and HCI con- • Project design: setting up, requirements, navigation,
sists of the following objectives storage, delivery.
Achieving Cognitive Skills in Multimedia … 13

• Authoring tools: history, comparison of different to all segments of society. It become shows and displays in a
approaches, functionality and principles. lot of TV channels, even in social media. now there are lots
• Web-based authoring. of institutes and colleges that teach student the art of optical
• Applications (e.g. kiosks, distance learning, web-based). illusions. This program is designed for all segments of
society, but there are three categories will take care of this
subject they are “teenagers, teachers, doctors”.
2 Method The main objective of the optical illusions program is to
attract lightness fun, and joy to the users, and also to help the
Students are required to do individual project for the teacher to make the classroom more fun and entertaining and
assignment. The title of the project should be of real time to attract students’ attention to the lesson and participate to
nature. Various phases of projects should be recorded in the solving the questions in new entertaining way. The doctor
report and should be submitted along with the project sub- will use this program to create a new atmosphere and fun for
mission. Report consists of the following contents. the Patients to help them gets rid of the pain and to attract
them to have some fun. This design will meet the needs of
• introduction the user, it containing a lot of information, such as “intro-
• design aspects duction of Optical illusions, how Optical illusions work,
– story boards used in medicine, used in classroom, used in fashion,
– navigational maps advantages of Optical illusions, picture, video”. These
– hierarchical task analysis diagram Optical illusions will be presented by multimedia program.
– prototype
• usability testing
– test plan 3 Design
– test plan2
– application usability tests 3.1 Story Board
• references.
Storyboards a sketches tells consecutive events in the form
In this paper the case study of optical Illusion is descri- of illustrations, an important element to explain ideas in
bed. Optical illusion is an illusion and tricks resulting from simple outline, a useful property used by the students to
the visual confusion and lack of discrimination pictures and clarify their thoughts within the limits of resources and time
be different from the images of nature. Optical illusion visual available where consists of a combination of text, images,
hallucination happens and affects the visual stimulation sound and graphics. Storyboards grew up in the film industry
causes hallucinations and visual disturbances, cognitive and to help directors, camera operator to visual film scenes in
cause eye diseases and headaches. sequence. Some of the sample story board sketches are
Optical illusion known as the interference happens to the shown in Figs. 1, 2, 3 and 4.
headmaster of the image never sees the reality of the image
and cannot distinguish them Most of the visual tricks is a
fitting pictures next to each other a measured way so as to 3.2 Navigation Maps
give the expected results. it automates Optical illusions
based on changes colors and lights that interfere with the A navigation map delineate the structure of the whole web
brain’s just a matter of the image where the brain is trying to project showing the greater part of the html pages and the
interpret what the eye sees but gets jammed he cannot associations from one page to others. It is helpful for
describe or refer it’s just an illusion. Multiple types of optical arranging and plainly perceiving how material ought to be
illusions with regard to the mismatch in color or geometric associated. The Navigation Map is like a storyboard, aside
shapes and tricks related to moving images and be from that it doesn’t give detail with regards to the substance
three-dimensional images and who relate to refrain mea- of the individual pages. The following are a few cases of
surements called deceived “Millar billion.” navigation map (see Fig. 5):
The optical illusions are one kind of delusion that urges
distortions and jamming the five senses. And are hearing and • Linear Structure: The web user explores successively,
sight and smell and taste and touch, where the five senses are moving starting with one page then onto the next.
human and sensors that can collect the necessary information • Hierarchical Structure: Closely resembling the branches
as a result of its interaction with its surroundings to help him of a tree. To move through and through it most move in
in various decision-making process. This topic is attracting one time.
14 R. Krishnan

Fig. 1 Story board 1

Fig. 2 Story board 2

Fig. 3 Story board 3

Achieving Cognitive Skills in Multimedia … 15

Fig. 4 Story board 4

Fig. 5 Navigation map

• Non-Linear Structure: Web clients can explore unre- 3.3 Hierarchical Task
servedly through the substance of the web extend,
unbound by foreordained courses. The hierarchical task analysis diagram is shown on Fig. 6.
• Composite Structure: Generally user can explore unre- Prototype. Optical illusions design is characterized by
servedly (as in the non-linear structure), however are simplicity design and clarity of the data, that easy design
sporadically compelled to a direct or progressive struc- enables the user to access the information which they want.
ture for a portion of the material. This design is characterized by “Special colors, photos,
16 R. Krishnan

Fig. 5 (continued)
Achieving Cognitive Skills in Multimedia … 17

Fig. 5 (continued)
18 R. Krishnan

Fig. 5 (continued)

videos, txt and buttons”, that buttons contain the threads that plans that the designer will be followed and adds to the
have been developed to this page design. all that Buttons are design. Prototype making the designer focuses more on the
easy to use by the user; by the way the user can press any design and arrangement of his thoughts, before starting work
button which he want. This design contains buttons which for easy access to the design that the user wishes, with high
enable the user to go back to the main page it call “back” quality. In my opinion there is no negative point for
button. The main objective of this design is to attract the user prototype.
to using this design. So we taking all the wishes of the user
in terms of design, colors and information displayed.
4 Usability Testing
There are a simple difference between prototype and
storyboard. In storyboard we have add new buttons, photos The following questions were asked to fill the questionnaire
and properties, which was not found in the prototype. The to study the success and the quality of the program. The
prototype contains plans and ideas that we will be Add to the questions of questionnaire are:
design of the program but when we start planning there are
few changes happened to the design format. One of the most 1. do you like the subject of the program?
positive aspects of the prototype is clarifying the ideas and 2. does the colors and background are good for that subject?
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