Table Of ContentStudies in Computational Intelligence 751
Yaxin Bi
Supriya Kapoor
Editors
Rahul Bhatia
Intelligent
Systems and
Applications
Extended and Selected Results from
the SAI Intelligent Systems Conference
(IntelliSys) 2016
Studies in Computational Intelligence
Volume 751
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Yaxin Bi Supriya Kapoor
(cid:129)
Rahul Bhatia
Editors
Intelligent Systems
and Applications
Extended and Selected Results from
the SAI Intelligent Systems Conference
(IntelliSys) 2016
123
Editors
YaxinBi RahulBhatia
Schoolof Computing TheScience andInformation
Ulster University at Jordanstown (SAI)Organization
Newtownabbey, CountyAntrim Bradford
UK UK
SupriyaKapoor
TheScience andInformation
(SAI)Organization
Bradford
UK
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-319-69265-4 ISBN978-3-319-69266-1 (eBook)
https://doi.org/10.1007/978-3-319-69266-1
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’
Editor s Preface
The SAI Intelligent Systems Conference (IntelliSys) 2016 was held on 21–22
September2016inLondon,UK.Thisconferenceisaprestigiousannualconference
onareasofintelligentsystemsandartificialintelligenceandtheirapplicationstothe
realworld,whichbuildsonthesuccessofpreviousIntelliSysconferencesalsoheld
at London.
This conference not only presented state of the art methods and valuable
experience from researchers in the related research areas, but also provided the
audiencewithavisionoffurtherdevelopmentinthefield.Theeventwasatwoday
program comprised of twenty six presentation sessions (paper and poster presen-
tations). The themes of the contributions and scientific sessions ranged from the-
ories to applications, reflecting a wide spectrum of artificial intelligence.
Out of 168 papers published in the proceedings, 22 papers, which received
highly recommended feedback, were selected, and the extended versions are pub-
lishedaschaptersinthisbook.Webelievethiseditionwillincreasethevisibilityof
research results presented in the conference, and certainly help further disseminate
new ideas and inspire more international collaborations.
It has been a great honor to serve as the Program Chair for the SAI Intelligent
Systems Conference (IntelliSys) 2016 and to work with the conference team. The
conferencewouldtrulynotfunctionwithoutthecontributionsandsupportreceived
fromauthors,participants,keynotespeakers,programcommitteemembers,session
chairs,organizingcommitteemembers,steeringcommitteemembers,andothersin
theirvariousroles.Theirvaluablesupport,suggestions,dedicatedcommitmentand
hard work have made the IntelliSys 2016 successful. Finally, we would like to
thank the conference’s sponsors and partners: HPCC Systems, IEEE and IBM
Watson AI XPrize.
Newtownabbey, UK Yaxin Bi
v
Contents
Pattern Sets for Financial Prediction: A Follow-Up . . . . . . . . . . . . . . . . 1
Mattias Wahde
Optimum Wells Placement in Oil Fields Using Cellular Genetic
Algorithms and Space Efficient Chromosomes . . . . . . . . . . . . . . . . . . . . 15
Alexandre Ashade L. Cunha, Giulia Duncan, Alan Bontempo,
and Marco Aurélio C. Pacheco
Transient Stability Enhancement Using Sliding Mode Based
NeuroFuzzy Control for SSSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Rabiah Badar and Jan Shair
A Multi-objective Genetic Algorithm for Path Planning
with Micro Aerial Vehicle Test Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
H. David Mathias and Vincent R. Ragusa
Mining Process Model Descriptions of Daily Life Through
Event Abstraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
N. Tax, N. Sidorova, R. Haakma, and W. van der Aalst
A Novel Approach for Time Series Forecasting with Multiobjective
Clonal Selection Optimization and Modeling . . . . . . . . . . . . . . . . . . . . . 105
N. N. Astakhova, L. A. Demidova, and E. V. Nikulchev
ARTool—Augmented Reality Human-Machine Interface for
Machining Setup and Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Amedeo Setti, Paolo Bosetti, and Amedeo Ragni
Some Properties of Gyrostats Dynamical Regimes Close
to New Strange Attractors of the Newton-Leipnik Type. . . . . . . . . . . . . 156
Anton V. Doroshin
vii
viii Contents
Toward Designing an Efficient System for Delivering
Contextual Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Jawaher Al-Yahya, Nouf Al-Rowais, Sara Al-Shathri,
Lamees Alsuhaibani, Amal Alabdulkarim, and Lamya AlBraheem
The CaMeLi Framework—A Multimodal Virtual Companion
for Older Adults. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Christiana Tsiourti, João Quintas, Maher Ben-Moussa, Sten Hanke,
Niels Alexander Nijdam, and Dimitri Konstantas
Emotional Domotics: Inhabitable Home Automation System
for Emotion Modulation Through Facial Analysis . . . . . . . . . . . . . . . . . 218
Sergio A. Navarro-Tuch, M. Rogelio Bustamante-Bello,
Javier Izquierdo-Reyes, Roberto Avila-Vazquez,
Ricardo Ramirez-Mendoza, Pablos-Hach Jose Luis,
and Yadira Gutierrez-Martinez
Measuring Behavioural Change of Players in Public Goods Game . . . . 242
Polla Fattah, Uwe Aickelin, and Christian Wagner
Object Segmentation for Vehicle Video and Dental CBCT by
Neuromorphic Convolutional Recurrent Neural Network . . . . . . . . . . . 264
Woo-Sup Han and Il Song Han
Weighted Multi-resource Minority Games . . . . . . . . . . . . . . . . . . . . . . . 285
S. M. Mahdi Seyednezhad, Elissa Shinseki, Daniel Romero,
and Ronaldo Menezes
M2M Routing Protocol for Energy Efficient and Delay Constrained
in IoT Based on an Adaptive Sleep Mode . . . . . . . . . . . . . . . . . . . . . . . 306
Wasan Twayej and H. S. Al-Raweshidy
Integration of Fuzzy C-Means and Artificial Neural Network for
Short-Term Localized Rainfall Forecasting in Tropical Climate . . . . . . 325
Noor Zuraidin Mohd-Safar, David Ndzi, David Sanders,
Hassanuddin Mohamed Noor, and Latifah Munirah Kamarudin
Neural Network Configurations Analysis for Identification
of Speech Pattern with Low Order Parameters . . . . . . . . . . . . . . . . . . . 349
Priscila Lima, Allan Barros, and Washington Silva
Knowledge-Based Expert System Using a Set of Rules to Assist
a Tele-operated Mobile Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
David Adrian Sanders, Alexander Gegov, and David Ndzi
Fuzzy Waypoint Guidance Controller for Underactuated
Catamaran Wave Adaptive Modular Vessel. . . . . . . . . . . . . . . . . . . . . . 393
Jyotsna Pandey and Kazuhiko Hasegawa
Contents ix
Trust and Resource Oriented Communication Scheme
in Mobile Ad Hoc Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414
Burhan Ul Islam Khan, Rashidah F. Olanrewaju, Roohie Naaz Mir,
S. H. Yusoff, and Mistura L. Sanni
Hybrid Audio Steganography and Cryptography Method Based
on High Least Significant Bit (LSB) Layers and One-Time
Pad—A Novel Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
Samah M. H. Alwahbani and Huwaida T. I. Elshoush
Generalised and Versatile Connected Health Solution
on the Zynq SoC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454
Dina Ganem Abunahia, Hala Raafat Abou Al Ola, Tasnim AhmadIsmail,
Abbes Amira, Amine Ait Si Ali, and Faycal Bensaali
Author Index.. .... .... .... ..... .... .... .... .... .... ..... .... 475
Pattern Sets for Financial Prediction:
A Follow-Up
B
Mattias Wahde( )
Chalmers University of Technology, SE-412 96, G¨oteborg, Sweden
[email protected]
Abstract. As a follow-up to an earlier investigation, a true forward
test has been carried out by applying a previously developed financial
predictor(intheformofasocalledpattern set,optimizedusinganevo-
lutionary algorithm) to a new data set, involving data for 200 stocks
and covering a time period from February 2016 to the end of that year.
Despite being applied to previously unseen data, the pattern set gener-
atedasetoftradeswithanaverageone-dayreturnof0.394%.Moreover,
the pattern set’s total trading return (excluding transaction costs) over
the entire period covered by the new data, when applied as a trading
strategy with a simple m–day holding period for each trade, was 15.9%
for m=1, 24.9% for m=3, and 61.6% for m=6, compared to 16.2%
for the benchmark index (S&P 500) over the same period.
1 Introduction
In the academic literature on finance, there is a strong, and generally well-
motivated, scepticism against claims of predictability of financial time series.
Theso-calledefficient market hypothesis (EMH)(seee.g.[1,3])summarizesthis
scepticismbysuggestingthatallrelevantinformationisalreadycontainedinthe
price of a financial instrument (for example a stock), such that consistent, prof-
itable prediction of future prices is impossible. Even though many studies exist
that support such a conclusion, the EMH has also been challenged by consider-
ingtheirrationalandemotionalbehavioroftenseenamongmarketparticipants,
manifestedintheformofherd-likebehaviorresultinginoverreactions,bothpos-
itive and negative, to events that impact the price of a financial instrument [6].
Thus, some studies have reported short-term predictability under certain cir-
cumstances; see e.g. [2,4–6]. In [9], an evolutionary algorithm (EA) was applied
to a set of daily stock market data in order to find predictors, so called pat-
tern sets, with the best possible performance, measured as the Sharpe ratio of
the one-day returns, i.e. the average one-day return (minus the risk-free rate of
return) divided by its standard deviation. The results showed that pattern sets
could be found that gave consistent, strongly positive results.
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