Recent Trends in Information and Communication Technology

Faisal Saeed · Nadhmi GazemSrikanta Patnaik · Ali Saleh Saed BalaidFathey Mohammed Editors

Proceedings of the 2nd International Conference of Reliable Information and Communication Technology (IRICT 2017)

Lecture Notes on Data Engineeringand Communications Technologies 5

Lecture Notes on Data Engineeringand Communications Technologies

Volume 5

Series editor

Fatos Xhafa, Technical University of Catalonia, Barcelona, Spaine-mail: fatos@cs.upc.edu

The aim of the book series is to present cutting edge engineering approaches to datatechnologies and communications. It publishes latest advances on the engineeringtask of building and deploying distributed, scalable and reliable data infrastructuresand communication systems.The series has a prominent applied focus on data technologies and communicationswith aim to promote the bridging from fundamental research on data science andnetworking to data engineering and communications that lead to industry products,business knowledge and standardisation.

More information about this series at http://www.springer.com/series/15362

Faisal Saeed • Nadhmi GazemSrikanta Patnaik • Ali Saleh Saed BalaidFathey MohammedEditors

Recent Trends inInformation andCommunicationTechnologyProceedings of the 2nd InternationalConference of Reliable Information andCommunication Technology (IRICT 2017)

123

EditorsFaisal SaeedInformation Systems Department,Faculty of Computing

Universiti Teknologi MalaysiaJohor BahruMalaysia

Nadhmi GazemInformation Systems Department,Faculty of Computing

Universiti Teknologi MalaysiaJohor BahruMalaysia

Srikanta PatnaikDepartment of Computer Scienceand Engineering

SOA UniversityBhubaneswar, OdishaIndia

Ali Saleh Saed BalaidISSI Research Group, Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia

Fathey MohammedFaculty of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia

ISSN 2367-4512 ISSN 2367-4520 (electronic)Lecture Notes on Data Engineering and Communications TechnologiesISBN 978-3-319-59426-2 ISBN 978-3-319-59427-9 (eBook)DOI 10.1007/978-3-319-59427-9

Library of Congress Control Number: 2017940839

© Springer International Publishing AG 2018This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

Printed on acid-free paper

This Springer imprint is published by Springer NatureThe registered company is Springer International Publishing AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

On behalf of the organizing committee of the 2nd International Conference ofReliable Information and Communication Technology 2017 (IRICT 2017), it is anhonor and a great pleasure to welcome all of you to Johor, Malaysia, and to theIRICT 2017 Conference.

The conference is organized by the Information Service Systems and InnovationResearch Group (ISSIRG) in Universiti Teknologi Malaysia (UTM) and theYemeni Scientists Research Group (YSRG). IRICT 2017 is a forum for the pre-sentation of technological advances and research results in the field of ICT. Theconference aims to bring together leading researchers, engineers, and scientists inthe domain of interest from around the world.

IRICT 2017 attracted a total of 199 submissions from 22 countries includingAlgeria, Australia, China, Egypt, France, Indonesia, Iran, Iraq, Italy, Jordan,Malaysia, Nigeria, Oman, Pakistan, Palestine, Saudi Arabia, Sudan, Syria, Taiwan,Turkey, UK, and Yemen. These submissions underwent a rigorous double-blindpeer-review process. Of those 199 submissions, 94 submissions (47%) have beenselected to be included in this book.

The book presents several hot research topics which include Advances on BigData Analysis Techniques and Applications, Mobile Networks, Applicationsand Usability, Reliable Communication Systems, Advances on Computer Vision,Advances on Artificial Intelligence and Soft Computing, Reliable Health Informatics,Reliable Cloud Computing Environment, E-Learning Acceptance Models, RecentTrends on Knowledge Management, Security issues in the Cyber World, Society andInformation Technology, and Recent Trends on Software Engineering.

Many thanks go to the keynote speakers for sharing their knowledge andexpertise with us and to all authors who have spent the time and effort to contributesignificantly to this event. And we would like to thank the organizing committee fortheir great efforts in ensuring the successful implementation of the conference. Inparticular, we would like to thank the technical committee for their thorough andtimely reviewing of the papers: Prof. Dr. Fatos Xhafa, LNDECT series editor;

v

Anjana Bhargavan, Thomas Ditzinger, Holger Schaepe, and Viktoria Meyer fromSpringer; Dr. Tze Hiang Alex Sim, the head of Information Service Systemsand Innovation Research Group (ISSIRG) in Universiti Teknologi Malaysia;Prof. Dr. Rose Alinda Alias, the President of Association for InformationSystems—Malaysian Chapter; Prof. Dr. Ahmad Fauzi Bin Ismail, DeputyVice-Chancellor (Research & Innovation) of Universiti Teknologi Malaysia; andlast but not least, Prof. Datuk Ir. Dr. Wahid bin Omar, Vice-Chancellor ofUniversiti Teknologi Malaysia.

We would also like to acknowledge the following organizations: UniversitiTeknologi Malaysia (UTM) and Malaysia Digital Economy Corporation (MDEC)for the great support to IRICT 2017. Finally, we thank all the participants of IRICT2017 and hope to see you again in the next IRICT conference.

Faisal SaeedNadhmi GazemSrikanta Patnaik

Ali Saleh Saed BalaidFathey Mohammed

vi Preface

IRICT-2017 Organizing Committee

Patron

Wahid bin Omar University Teknologi Malaysia

Honorary Chair

Rose Alinda Alias Association for Information Systems –Malaysian Chapter

International Advisory Board

Abdul Samad Haji Ismail Universiti Teknologi Malaysia, MalaysiaAhmed Yassin Al-Dubai Edinburgh Napier University, UKAli Bastawissy Cairo University, EgyptAli Selamat Universiti Teknologi Malaysia, MalaysiaAyoub AL-Hamadi Otto-von-Guericke University Magdeburg,

GermanyEldon Y. Li National Chengchi University, TaiwanHabibollah Haron Universiti Teknologi Malaysia, MalaysiaKamalrulnizam Abu Bakar Universiti Teknologi Malaysia, MalaysiaMohamed M.S. Nasser Qatar University, QatarSrikanta Patnaik SOA University, Bhubaneswar, India

vii

Conference Chair

Faisal Saeed Universiti Teknologi Malaysia

Program Committee Co-chairs

Nadhmi Gazem Universiti Teknologi MalaysiaFathey Mohammed Universiti Teknologi Malaysia

Secretariat

Redwan Abdulkader Universiti Kebangsaan Malaysia

Publicity Committee

Abdullah Aysh Dahawi (Chair) Universiti Teknologi MalaysiaMuaadh Shaif Mukred Universiti Kebangsaan MalaysiaWaddah Waheeb Hassan Saeed Universiti Tun Hussein Onn MalaysiaMohammed Ali Ahmed Universiti Science MalaysiaHelal Ali Ahmed Mohammed King Khalid University, Saudi ArabiaMohammed Abdurabu Saleh

Al-AnsiAl Baath University, Syria

Basem Mohammed Qasem Aqlan King Saud University, Saudi ArabiaAbdullah Mohammed Saghir

ZobilahUniversiti Teknikal Malaysia Melaka

Mohammed Abdulkaliq AbdullahAl-Mahfadi

Universiti Malaysia Pahang

Abdullah Saleh Ali Alqamili Universiti Tun Hussein Onn MalaysiaKamal Ahmed Ali Kamal University International Islamic MalaysiaTaha Hussain Dahawi University of Malaya

Publications Committee

Ali Saleh Saed Balaid (Chair) Universiti Teknologi MalaysiaQais Alnuzaili Universiti Teknologi MalaysiaSaeed Balubaid Universiti Teknologi MalaysiaYahya M. Al-dheleai Universiti Teknologi Malaysia

viii IRICT-2017 Organizing Committee

Ahmed Majed Universiti Teknologi MalaysiaAbdulalem Ali Universiti Teknologi MalaysiaArafat Mohammed Rashad Universiti Teknologi MalaysiaHakim Qaid Abdullah Abdulrab Universiti Teknologi Malaysia

IT Committee

Bander Ali Saleh Al-rimy (Chair) Universiti Teknologi MalaysiaFuad Abdeljalil Al-shamiri Universiti Teknologi MalaysiaAmer Alsaket Universiti Putra MalaysiaAmer Alkathiri Universiti Teknologi Malaysia

Logistic Committee

Nabil Abduljalil Abdo Mohammed(Chair)

Universiti Teknologi Malaysia

Taha Sadeq Universiti Tunku Abdul RahmanMohammed Yousef Mohammed

RasamUniversiti Teknologi Malaysia

Mohammed Mohsen NasserAl-awfi

Universiti Teknologi Malaysia

Gehad Ahmed MohammedBen Azzan

Universiti Teknologi Malaysia

Mohammed Ameen AbdullahAl-mekhlafi

Universiti Teknologi Malaysia

Treasure Committee

Wahid Ali Hamood AL-Twayti(Chair)

Universiti Teknologi Malaysia

Yaseen Mohammed SulaimanAl-Wesabi

Universiti Teknologi Malaysia

Hamzah Gamal Abdo Allozy Universiti Teknologi Malaysia

Registration Committee

Sameer Hasan Albakri (Chair) Universiti Teknologi MalaysiaAbdullah Aysh Dahawi Universiti Teknologi Malaysia

IRICT-2017 Organizing Committee ix

Sponsorship

Saqr Abdulrakeeb Almuraisy Universiti Teknologi Malaysia

International Technical Committee

Ab Razak Che Hussin Universiti Teknologi Malaysia, MalaysiaAbdulbasit Darem Mysore University, IndiaAbdulghani Ali Ahmed Universiti Malaysia Pahang, MalaysiaAbdulrahman Alsewari Universiti Malaysia Pahang, MalaysiaAbdulrazak Alhababi UNIMAS, MalaysiaAhmed Al-Saman Universiti Teknologi Malaysia, MalaysiaAhmed Hamza King Abdulaziz University, KSAAli Balaid Universiti Teknologi Malaysia, MalaysiaAmeer Tawfik KMIT University, AustraliaAshraf Osman Alzaiem Alazhari University, SudanAsma Ahmed Alhashmi Mysore University, IndiaFaisal Saeed Universiti Teknologi Malaysia, MalaysiaFathey Mohammed Universiti Teknologi Malaysia, MalaysiaMohammed Alshargabi Najran University, KSAMurad Rassam Taiz University, YemenNadhmi Gazem Universiti Teknologi Malaysia, MalaysiaNasrin Makbol Universiti Science Malaysia, MalaysiaNoorminshah Iahad Universiti Teknologi Malaysia, MalaysiaQais Alnuzaili Universiti Teknologi Malaysia, MalaysiaRedhwan Q. Shaddad Taiz University, YemenSameer Albakri Universiti Teknologi Malaysia, MalaysiaTaha Hussein Universiti Malaysia Pahang, MalaysiaTawfik Hadhrami University of the West of Scotland, UKThabit Sabbah Al-Quds Open University, PalestineWaleed Al-rahmi Universiti Teknologi Malaysia, MalaysiaYogan J. Kumar Universiti Teknikal Malaysia Melaka,

Malaysia

x IRICT-2017 Organizing Committee

Contents

Big Data Analysis Techniques and Applications

Prediction of Financial Distress for Electricity SectorsUsing Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Maryam Mirzaei, Seyed Mehrshad Parvin Hosseini, Goh Guan Gan,and Pritish Kumar Sahu

Predictive Modeling for Dengue Patient’s Length of Stay (LoS)Using Big Data Analytics (BDA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Henni Jumita Muhamad Hendri and Hidayah Sulaiman

Experimental Performance Analysis of B+-Trees with Big DataIndexing Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Ali Usman Abdullahi, Rohiza Ahmad, and M. Nordin Zakaria

A Proposed Methodology for Integrating Oil and Gas DataUsing Semantic Big Data Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Kamaluddeen Usman Danyaro and M.S. Liew

Molecular Similarity Searching with Different Similarity Coefficientsand Different Molecular Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Fouaz Berrhail, Hacene Belhadef, Hamza Hentabli, and Faisal Saeed

Classification of Arabic Writer Based on Clustering Techniques . . . . . . 48Ahmed Abdullah Ahmed, Mohammed Sabbih Al-Tamimi,Omar Ismael Al-Sanjary, and Ghazali Sulong

Data Pre-processing Techniques for PublicationPerformance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Fatin Shahirah Zulkepli, Roliana Ibrahim, and Faisal Saeed

Data Mining Techniques: A Systematic Mapping Review . . . . . . . . . . . . 66Nouf A. Almozayen, Mohd Khalit Bin Othman, Abdullah Bin Gani,and Salman Z. Alharethi

xi

A Comprehensive Study on Opinion Mining Featuresand Their Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Shirin Noekhah, Naomie Binti Salim, and Nor Hawaniah Zakaria

Mobile Networks, Applications and Usability

Adaptive Hybrid Geo-casting Routing Protocol for MobileAd hoc Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Ahlam Hashim, Kamalrulnizam Abu Bakar, and Anazida Zainal

What Makes Older People Want to Use Mobile Devices? . . . . . . . . . . . . 100Sofianiza Abd Malik, Muna Azuddin, and Lili Marziana Abdullah

Mobile Augmented Reality Tourism Application Framework . . . . . . . . . 108Rashidi Abd Rashid, Halina Mohamed, and Ab Razak Che Hussin

Motion Artifact Reduction Algorithm Using Sequential AdaptiveNoise Filters and Estimation Methods for Mobile ECG . . . . . . . . . . . . . . 116Fuad A. Ghaleb, Maznah Kamat, Mazleena Salleh, Mohd. Foad Rohani,and Saif Eddine Hadji

Concerning Matters of Mobile Device Usage Among Older People. . . . .. . . . 124Muna Azuddin, Sofianiza Abd Malik, and Murni Mahmud

Adaptive Memory Size Based Fuzzy Control for MobilePedestrian Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad,Raja Zahilah Raja Mohd Radzi, Mazleena Salleh, and Ahmad Fadhil Yusof

Extraction of Common Concepts for the Mobile Forensics Domain . . . .. . . . 141Abdulalem Ali, Shukor Abd Razak, Siti Hajar Othman,and Arafat Mohammed

Revisiting the Usability of Smartphone User Interfacefor Elderly Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Hasanin Mohammed Salman, Wan Fatimah Wan Ahmad,and Suziah Sulaiman

Taguchi Methods for Ad Hoc on Demand Distance Vector RoutingProtocol Performances Improvement in VANETs . . . . . . . . . . . . . . . . . . 163Mohamed Elshaikh, Mohd Nazri Bin Mohd Warip, Naimah Yaakob,Ong. B. Lynn, Aalaa Kamal Yousif, and Zahereel Ishwar

Two Stage Integration of GPS, Kinematic Information,and Cooperative Awareness MessagesUsing Cascaded Kalman Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171Fuad A. Ghaleb, Anazida Zainal, Murad A. Rassam, and Faisal Saeed

xii Contents

Modeling of GPS Ionospheric Scintillation Using NonlinearRegression Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180E.F. Aon, Y.H. Ho, A.R. Othman, and R.Q. Shaddad

Hybrid LTE-VANETs Based Optimal Radio Access Selection . . . . . . . . 189Ayoob A. Ayoob, Gang Su, Desheng Wang, Muamer N. Mohammed,and Omar A. Hammood

Reliable Communication Systems

Optimization of B-MAC Protocol for Multi-scenario WSNby Differential Evolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Alaa Kamal, M.N. Mohd. Warip, Normaliza Omar, Mohammed Elshikh,Muzammil Jusoh, and Ong. B. Lynn

Power Consumption Optimization Based on B-MAC Protocolfor Multi-Scenario WSN by Taguchi Method . . . . . . . . . . . . . . . . . . . . . . 210Alaa Kamal, M.N. Mohd. Warip, Normaliza Omar, Mohammed Elshikh,Muzammil Jusoh, and Ong. B. Lynn

Modelling and Control of a Non-linear Inverted PendulumUsing an Adaptive Neuro-Fuzzy Controller . . . . . . . . . . . . . . . . . . . . . . . 218Mohammed A.A. Al-Mekhalfi and Herman Wahid

Adapted WLAN Fingerprint Indoor Positioning System (IPS)Based on User Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226Firdaus, Noor Azurati Ahmad, and Shamsul Sahibuddin

Performance Analysis of the Impact of Design Parametersto Network-on-Chip (NoC) Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 237Ng Yen Phing, M.N. Mohd Warip, Phaklen Ehkan, F.W. Zulkefli,and R. Badlishah Ahmad

A Comparative Review of Adaptive Routing Approachfor Network-on-Chip Router Architecture . . . . . . . . . . . . . . . . . . . . . . . . 247F.W. Zulkefli, P. Ehkan, M.N.M. Warip, Ng Yen Phing, and F.F. Zakaria

Intelligent Routing Algorithm Using Genetic Algorithm (IRAGA) . . . . . 255Nibras Abdullah, Ola A. Al-wesabi, Mahmoud Baklizi,and Mohammed M. Kadhum

Double Curved Tracks Simulation of FSO Link for Ground-to-TrainCommunications in Tropical Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264Wafi A. Mabrouk and M.F.L. Abdullah

Performance Evaluation of AODV, DSDV, and DSR RoutingProtocols in MANET Using NS-2 Simulator . . . . . . . . . . . . . . . . . . . . . . . 276Abdullah A. Al-khatib and Rosilah Hassan

Contents xiii

Planning and Optimization of LTE Radio Access Networkfor Urban Area at Taiz City, Yemen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Redhwan Q. Shaddad, Mohammed A. Abdulwadood, Nada Y. Kuradah,Samar A. Alsharaie, Mohammed A. Qaid, Amal A. Alramesi,and Akram A. Rassam

Advances on Computer Vision

Arabic Sign Language Recognition Using Optical Flow-BasedFeatures and HMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Ala addin I. Sidig, Hamzah Luqman, and Sabri A. Mahmoud

On the Design of Video on Demand Server-Based HybridStorage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Ola A. Al-wesabi, Nibras Abdullah, and Putra Sumari

An Adaptive Threshold Based on Multiple Resolution Levelsfor Canny Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316Zuraini Othman and Azizi Abdullah

The Design of an Adaptive Media Playout Technique Basedon Fuzzy Logic Control for Video Streaming Over IP Networks . . . . . . 324Farij Ehtiba, Z.I.A. Khalib, Naseer Sabri, R. Badlishah Ahmad,and Mingfu Li

A Preliminary Study on the Effect of Audio Feedback to SupportComprehension of Web Content Among Non-visual Internet Users . . . .. . . . 335Nur Fadhilah Mohd Noh, Suziah Sulaiman, Azelin Mohamed Noor,and Janson Luke Ong Wai Kit

Recognition of Holy Quran Recitation RulesUsing Phoneme Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343Ammar Mohammed, Mohd Shahrizal Bin Sunar, and Md. Sah Hj. Salam

Realistic Rendering Colored Light Shafts Using Light Texture. . . . . . . . 353Hatam H. Ali, Mohd Shahrizal Sunar, and Hoshang Kolivand

Evaluation of Digital Image Watermarking Techniques . . . . . . . . . . . . . 361Tanya Koohpayeh Araghi and Azizah B.T. Abdul Manaf

An Enhanced Quadratic Angular Feature Extraction Modelfor Arabic Handwritten Literal Amount Recognition . . . . . . . . . . . . . . . 369Qais Al-Nuzaili, Ali Hamdi, Siti Z. Mohd Hashim, Faisal Saeed,and Mohammed Sayim Khalil

Semi-automatic Methods in Video Forgery Detection Basedon Multi-view Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378Omar Ismael Al-Sanjary, Nurulhuda Ghazali, Ahmed Abdullah Ahmed,and Ghazali Sulong

xiv Contents

Neuronal Approach for Emotion Recognition Based on FeaturesMotion Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389Belhouchette Kenza, Berkane Mohamed, and Belhadef Hacene

Segmentation and Enhancement of Fingerprint Images Basedon Automatic Threshold Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400Alaa Ahmed Abbood, Ghazali Sulong, Atheer Akram Abdul Razzaq,and Sabine U. Peters

Capturing Haptic Experience Through Users’ Visual Sketches . . . . . . . . 412Fasihah Haji Abd Samad, Suziah Sulaiman,Dayang Rohaya Awang Rambli, and Halabi Hasbullah

A Comparative Study of a New Hand Recognition Model Basedon Line of Features and Other Techniques . . . . . . . . . . . . . . . . . . . . . . . . 420Mayyadah R. Mahmood and Adnan M. Abdulazeez

Novel FPGA Implementation of EPZS Motion Estimationin H.264 AVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433Nahed Ali Bahran, Abdelhalim Zekry, Ramy Ahmed Fathy,Mohamed Fathi Ebian, and Reem Ibrahim Sayed

Advances on Artificial Intelligence and Soft Computing

Comparison of Drought Forecasting Using ARIMAand Empirical Wavelet Transform-ARIMA . . . . . . . . . . . . . . . . . . . . . . . 449Muhammad Akram bin Shaari, Ruhaidah Samsudin,and Ani bin Shabri Ilman

Real Time Electrocardiogram Identification with Multi-modalMachine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Tuerxun Waili, Rizal Mohd Nor, Khairul Azami Sidek,Abdul Wahab Bin Abdul Rahman, and Gökhan Güven

An Analysis of Rough Set-Based Application Toolsin the Decision-Making Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467Masurah Mohamad and Ali Selamat

Differential Evolution Based Special Protection and ControlScheme for Contingency Monitoring of TransmissionLine Overloading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Mahmood Khalid Hadi, Mohammad Lutfi Othman,and Noor Izzri Abd Wahab

An Implementation of Metaheuristic Algorithms in BusinessIntelligence Focusing on Higher Education Case Study . . . . . . . . . . . . . . 488Mohd Shahizan Othman, Shamini Raja Kumaran, and Lizawati Mi Yusuf

Contents xv

Design and Control of Online Battery Energy Storage SystemUsing Programmable Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . 496Nabil Mohammed and Kumeresan A. Danapalasingam

Test Cases Minimization Strategy Based on FlowerPollination Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505AbdulRahman A. Alsewari, Ho C. Har, Ameen A. Ba Homaid,Abdullah B. Nasser, Kamal Z. Zamli, and Nasser M. Tairan

Predicting Global Solar Radiation in Nigeria Using AdaptiveNeuro-Fuzzy Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513Sani Salisu, M.W. Mustafa, and M. Mustapha

A Novel Hybrid Bird Mating Optimizer with Differential Evolutionfor Engineering Design Optimization Problems . . . . . . . . . . . . . . . . . . . . 522Haval Sadeeq, Adnan Abdulazeez, Najdavan Kako, and Araz Abrahim

Forecasting Crude Oil Prices Using Wavelet ARIMAModel Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535Nurull Qurraisya Nadiyya Md-Khair and Ruhaidah Samsudin

The Classification of Urban Growth Pattern Using TopologicalRelation Border Length Algorithm: An Experimental Study . . . . . . . . . 545Nur Laila Ab Ghani and Siti Zaleha Zainal Abidin

CMARPGA: Classification Based on Multiple Association RulesUsing Parallel Genetic Algorithm Pruned Decision Tree . . . . . . . . . . . . . 554HanChern-Tong and Izzatdin Aziz

Modified Cuckoo Search Algorithm for Solving GlobalOptimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561Mohammad Shehab, Ahamad Tajudin Khader, and Makhlouf Laouchedi

A New Hybrid K-Means Evolving Spiking Neural Network ModelBased on Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571Abdulrazak Yahya Saleh, Haza Nuzly Bin Abdull Hamed,Siti Mariyam Shamsuddin, and Ashraf Osman Ibrahim

Reliable Health Informatics

Local Search Based Enhanced Multi-objective Genetic Algorithmof Training Backpropagation Neural Network for BreastCancer Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587Ashraf Osman Ibrahim, Siti Mariyam Shamsuddin,and Abdulrazak Yahya Saleh

xvi Contents

Optimization Health Care Resources in Sensor NetworkUsing Fuzzy Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595Marwan Alhadi R. Ramdan, Mohammad Faiz Liew Abdullah,and Ahmed N. Abdalla

Towards Improving the Healthcare Services in Least DevelopedCountries: A Case of Health Needs Assessment for Telehealthin Yemen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605Abdulrahman A. Al-Fadhli, Marini Othman, Nor’ashikin Ali,and Bassam A. Al-Jamrh

User Requirements for Prediabetes Self-care Application:A Healthcare Professional Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . 616Suthashini Subramaniam, Jaspaljeet Singh Dhillon,Mohd. Sharifuddin Ahmad, Joyce W.S. Leong, and Cameron Teoh

Understanding Health Professionals’ Intention to Use Telehealthin Yemen: Using the DeLone and McLean IS Success Model . . . . . . . . . 627Abdulrahman A. Al-Fadhli, Marini Othman, Nor’ashikin Ali,and Bassam A. Al-Jamrh

Reliable Cloud Computing Environment

Quality of Service (QoS) Task Scheduling Algorithm with TaguchiOrthogonal Approach for Cloud Computing Environment . . . . . . . . . . . 641Danlami Gabi, Abdul Samad Ismail, Anazida Zainal,and Zalmiyah Zakaria

A Fuzzy Logic Based Risk Assessment Approach for Evaluatingand Prioritizing Risks in Cloud Computing Environment . . . . . . . . . . . . 650A. Amini, N. Jamil, A.R. Ahmad, and H. Sulaiman

Resolve Resource Contention for Multi-tier Cloud ServiceUsing Butterfly Optimization Algorithm in Cloud Environment . . . . . . . 660Mohamed Ghetas and Huah Yong Chan

Digital Forensic Challenges in the Cloud Computing Environment . . . .. . . . 669Ganthan Narayana Samy, Bharanidharan Shanmugam, Nurazean Maarop,Pritheega Magalingam, Sundresan Perumal, and Sameer Hasan Albakri

E-Learning Acceptance Models

Acceptance Model of Social Media for Informal Learning . . . . . . . . . . . 679Mohmed Y. Mohmed Al-Sabaawi and Halina Mohamed Dahlan

Critical Factors to Learning Management System Acceptanceand Satisfaction in a Blended Learning Environment . . . . . . . . . . . . . . . 688Samar Ghazal, Hanan Aldowah, and Irfan Umar

Contents xvii

End-User Perspectives on Effectiveness of Learning PerformanceThrough Massive Open Online Course (MOOCs) . . . . . . . . . . . . . . . . . . 699Mohd Shahizan Othman, Guligeina Tashimaimaiti, Lizawati Mi Yusuf,and Waleed Mugahed Al-Rahmi

A Reflective Practice of Using Digital StorytellingDuring Teaching Practicum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708Bee Choo Yee, Tina Abdullah, and Abdullah Mohd Nawi

Recent Trends on Knowledge Management

The Role of Knowledge Sharing in Business IncubatorsPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719Masoumeh Zibarzani and Mohd Zaidi Abd Rozan

Knowledge Creation Process Within Group Problem SolvingAmong Students in Academic Institutions . . . . . . . . . . . . . . . . . . . . . . . . . 728Saleh Abdullah Alkhabra, Haryani Haron, and Natrah Abdullah

Security in the Cyber World

Blockchain Security Hole: Issues and Solutions . . . . . . . . . . . . . . . . . . . . 739Norul Suhaliana bt Abd Halim, Md Arafatur Rahman, Saiful Azad,and Muhammad Nomani Kabir

A Robust DCT Based Technique for Image WatermarkingAgainst Cropping Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747Mohammad Javad Rajabi, Shahidan M. Abdullah, Majid Bakhtiari,and Saeid Bakhtiari

A 0-Day Aware Crypto-Ransomware Early BehavioralDetection Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758Bander Ali Saleh Al-rimy, Mohd Aizaini Maarof,and Syed Zainuddin Mohd Shaid

SBRT: API Signature Behaviour Based Representation Techniquefor Improving Metamorphic Malware Detection . . . . . . . . . . . . . . . . . . . 767Gamal A.N. Mohamed and Norafida Bte Ithnin

Society and Information Technology

Dimensions for Productive Ageing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781Norisan Abd Karim, Haryani Haron, Wan Adilah Wan Adnan,and Natrah Abdullah

Digital Games Acceptance in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . 789Normal Mat Jusoh and Bin Ismail Ishak

xviii Contents

Rising Ageing Population: A Preliminary Study of MalaysianOlder People Expectations in Informationand Communication Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796Lili Marziana Abdullah, Muna Azuddin, Sofianiza Abd Malik,and Murni Mahmud

Online Shopping Inventory Issues and Its Impact on ShoppingBehavior: Customer View. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804Muhammad Noman Malik, Huma Hayat Khan,Abdoulmohammad Gholamzadeh Chofreh, and Feybi Ariani Goni

The DeLone–McLean Information System Success Model forElectronic Records Management System Adoption in HigherProfessional Education Institutions of Yemen . . . . . . . . . . . . . . . . . . . . . . 812Muaadh Mukred and Zawiyah M. Yusof

Users’ Verification of Information System CurriculumDesign Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824Thong Chee Ling, Yusmadi Yah Jusoh, Rusli Adbullah,and Nor Hayati Alwi

Understanding NUI Among Children: A Usability Studyon Touch-Form and Free-Form Gesture-Based Interaction . . . . . . . . . . . 832Mohd Salihan Ab Rahman, Nazlena Mohamad Ali, and Masnizah Mohd

Influence Maximization Towards Target Users on Social Networksfor Information Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 842Abdus-Samad Temitope Olanrewaju, Rahayu Ahmad,and Massudi Mahmudin

Exploring Elements and Factors in Social Content Managementfor ICT Service Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 851Wan Azlin Zurita Wan Ahmad, Muriati Mukhtar, and Yazrina Yahya

Recent Trends on Software Engineering

Situational Requirement Engineering in GlobalSoftware Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863Huma Hayat Khan, Muhammad Noman Malik,Abdoulmohammad Gholamzadeh Chofreh, and Feybi Ariani Goni

Intellectual Property Challenges in the Crowdsourced SoftwareEngineering: An Analysis of Crowdsourcing Platforms . . . . . . . . . . . . . . 875Hani Al-bloush and Badariah Solemon

A Review of Advances in Extreme Learning Machine Techniquesand Its Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885Oyekale Abel Alade, Ali Selamat, and Roselina Sallehuddin

Contents xix

A Review on Meta-Heuristic Search Techniques for Automated TestData Generation: Applicability Towards Improving AutomaticProgramming Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896Rohaida Romli, Noorazreen Nordin, Mazni Omar,and Musyrifah Mahmod

Predicting Software Reliability with a Novel NeuralNetwork Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907Shirin Noekhah, Naomie Binti Salim, and Nor Hawaniah Zakaria

Performance Analysis of OpenMP Scheduling Typeon Embarrassingly Parallel Matrix Multiplication Algorithm . . . . . . . . . 917Ng Hui Qun, Z.I.A. Khalib, and R.A.A. Raof

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927

xx Contents

Big Data Analysis Techniquesand Applications

Prediction of Financial Distress for ElectricitySectors Using Data Mining

Maryam Mirzaei(&), Seyed Mehrshad Parvin Hosseini,Goh Guan Gan, and Pritish Kumar Sahu

Faculty of Business, Multimedia University, 75450 Malacca, Malaysiamaryam.mirzaei@mmu.edu.my

Abstract. This article addresses the financial aspects surrounding the stabilityof the electricity sector. We apply a variety of data mining techniques to buildfinancial distress warning models based on the financial statement analysismethod. The analysis reveal that neural networks with the accuracy of 80% andabove in different scenarios were found to be relatively more accurate comparedto decision trees and support vector machines. Additionally, in order to assessthe ability of financial indicators, we applied feature selection. The financialratios analyses proved the significance of profitability, liquidity and financialleverage for the default prediction models. Therefore, it is exigent that com-panies utilize their assets, liquidity and solvency as the core of their managementpolicy regulations. The key contribution of this paper is the formulation of aproper model for financial distress prediction among electricity sector compa-nies in Iran.

Keywords: Financial distress � Probability of default � Data mining

1 Introduction

Energy is among the most extensively discussed subjects in economics and it is listedamong the top priorities for several nations. Energy, along with other inputs, playscrucial in the production of almost all goods and services [1]. The levels of fossil fuelenergy consumption in developed countries are already very high and constantlygrowing in developing countries [2, 3]. The development of renewable energy is alsorelevant for Iran as an OPEC member and assumes the necessity for more extensiveproduction and consumption of energy from renewable sources. In this respect, theelectricity consumption in Iran in 2012 increased by 5.6% compared to the previousyear [4]. One of the main advantages of the Iranian power market is its access toelectricity hungry export markets [5]. The long-term electricity demand in Iran shows asubstantial increase, and it is expected to continue growing rapidly, which necessitatescomprehensive investment to meet the growing domestic consumption demand overthe next few decades. Therefore, it is of interest to determine Iran’s electricity sectorstability from the financial perspective. However, little has been written about thefinancial stability of Iran’s electricity sector to date.

Currently, maintaining robust financial stability is considered obligatory and is nolonger optional, but the number of organizations that practically achieve strong

© Springer International Publishing AG 2018F. Saeed et al. (eds.), Recent Trends in Informationand Communication Technology, Lecture Notes on Data Engineeringand Communications Technologies 5, DOI 10.1007/978-3-319-59427-9_1

sustainability is still very low [6]. It is noted that companies in the electricity sector aredeemed financially stable if they are financially viable and healthy. Financial failuregenerally leads to a decline in the company’s profitability. It is assumed that theprobability of default decreases as the financial status of companies improves [7].Therefore, it is essential to establish an effective early warning system of forecastingfinancial distress, among these companies. Nevertheless, financial stability, whichdenotes the financial feasibility and health of companies that make up the electricityenergy sector, remains unclear. Therefore, this article will address this gap.

Various methods can be applied to analyze and present the financial distress ofcompanies. In this study, the financial statement analysis method is employed with theassistance of the financial ratio technique. Accordingly, the 5 following groups of ratiosare computed: financial leverage, liquidity, profitability, activity and solvency ratios.The financial indicators and data mining techniques are employed to predict whichcompanies are likely to face default. Overall, the aim is to answer two main questions,with the first being which data mining techniques can predict financial distress ofelectricity companies most accurately? And secondly which financial indicators aremost effective in this type of forecasting? The results of this research provide earlywarning about the probability of default, so that companies can take the necessarycorrective measures to avoid financial distress. Section 2 of the paper will describe themethodology, that is being employed while discussions of the empirical results aredescribed in Sect. 3. Finally, conclusions are drawn in Sect. 4.

2 Methodology

2.1 Models for Predicting Financial Distress and Forecasting Probabilityof Default

Research methods for company failure prediction are developed from univariate tomultivariate models that implement machine learning techniques. Discriminant analysisis generally used for model improvement in the early stages of bankruptcy prediction[8–10]. Researchers have applied different machine learning techniques to predictdefault, but neural networks are the most commonly used method [11–14]. Othermachine learning techniques include decision trees [15–19], rough sets [20], andsupport vector machine [21, 25]. Recently, a new direction for prediction modelsincludes multiple classifiers, such as majority voting, Adaboost and Bagging. However,it seems that there is no unique financial distress prediction and bankruptcy forecastingmodel that is suitable for analysis in all industries and time periods [22].

In this study, default probability is predicted using the knowledge discovery pro-cess. To do so, Neural Networks (NN), Decision Trees (DT) and Support VectorMachines (SVM) were selected to predict the default probability of electricity com-panies. Moreover, Adaboost classifier was utilized as a multiple classifier. A combi-nation of different techniques was used to select appropriate financial indicators andachieve higher forecasting accuracy. These models’ performance was evaluated basedon four partition ratios, including 90:10, 80:20, 70:30 and 60:40, respectively. Asstated above, the aim is to ascertain whether it is possible to design an early warning

4 M. Mirzaei et al.

system for predicting signs of a company’s financial collapse. The prediction modelswere built on data collected from the listed companies 2, 3 and 4 years prior tobankruptcy. The developed models were run to predict bankruptcy of electricity andenergy industrial companies listed in the Tehran Stock Exchange (TSE). The modelswere implemented based on financial data obtained 2, 3 and 4 years before the com-panies received the ST label. Hence, financial data from year t − 2, t − 3 and t − 4were used to forecast whether a company was labelled as ST in year t. In financial riskprediction, accuracy and error rate are important indicators of classification algorithmreliability. To this end, the model evaluation metrics recommended by [23] are con-sidered. To do so, the models’ performance is estimated in terms of model accuracy(measure of the number of correct forecasts including default and non-default to totalnumber of observations), recall (the number of correctly forecasted to actual defaultrecords), and precision (ratio of correctly forecasted to total number of forecasteddefault records). The root mean square error (RMSE), or the mean of the squareddifferences between corresponding elements of forecasts and observations, is alsocalculated.

2.2 Financial Indicators

Financial information including financial ratios are important when examining com-pany default. When selecting a bankruptcy prediction model, several financial infor-mation could be included to the model. The ratios, which are computed and analyzed inthis article, include financial leverage, liquidity, profitability, solvency and activityratios. Financial leverage is defined as the degree to which a company is using bor-rowed funds. The financial flexibility of firms is considered as a primary driver for theircapital structure decision [24, 25]. Three financial leverage ratios are calculated,including: total liabilities to total assets, short-term liabilities to total assets, and totalassets to equity. Liquidity indicates a firm’s ability to serve ongoing expenditure.According to [26–28], liquidity is one of the main factors affecting default prediction.The liquidity ratios are: working capital to total assets (WCTA), current ratio (CA/STL)and cash to current liabilities (Ca/CL). Profitability refers to the company’s capabilityto yield profit over its costs over a distinct period of time. Profitability is a crucialindicator [29], which leads a company to survival and success. Among profitabilityratios, EBIT to total assets (EBIT/TA), net profit to total assets (NP/TA), and net profitto current assets (NP/CA) are used for the purpose of this study. Solvency ratio pro-vides an indication of the company’s ability to repay all financial obligations if allassets were sold, as well as an indication of the ability to continue operating as a viablecompany subsequent to a financial adversity. Activity ratios are used to measure therelative efficiency of a company based on its use of assets, leverage or other balancesheet items. The two activity ratios calculated are sales to current assets (S/CA) andreceivables to liabilities (R/L).

Prediction of Financial Distress for Electricity Sectors 5

3 Results and Discussion

3.1 Prediction Performance of Data Mining Techniques

Different scenarios were examined based on different training-to-testing ratios and threetime periods. The statistical output revealed that the NN prediction accuracy was higherthan DT, SVM and Ad in different time periods, including t − 2, t − 3 and t − 4,across different training-to-testing ratios. However, it is observed that Ad performedNN in some of these experiments. It seems obvious that NN outperformed the otherdata mining techniques for 2 years prior to default and different training-to-testingratios. Moreover, the findings indicate that the average prediction accuracy of NN wasthe highest in different scenarios, except for the case of 90:10 in the 2-year (82.7%) and3-year (60.37%) time periods and the cases of 60:40 (86.6%) and 80:20 (65.7%) in the3-year time period, where Ad exhibited higher accuracy for the mentioned time peri-ods, 86.2%, 81.8%, 88.2% and 82.6%, respectively (Table 1 and Fig. 1).

The performance of DT and SVM ranked next, close but not exceeding theaccuracy of NN. Besides, multiple classifiers based on the Adaboost ensemble classifierdid not confidently improve the best single classifiers, with the exception of the

Table 1. Prediction results of DT, NN, SVM and Adaboost.

90:10 80:20 70:30 60:40t − 2 t − 3 t − 4 t − 2 t − 3 t − 4 t − 2 t − 3 t − 4 t − 2 t − 3 t − 4

DDTAcc. 72.4 81.8 55.5 74.1 82.6 51.4 74.7 85.2 64.1 81.8 82.2 61.4Re. .77 .66 .71 .76 .77 .85 .77 .83 .65 .83 .71 .78Pre. .53 1 .45 .61 .77 .44 .66 .83 .63 .83 .88 .61RMSE .51 .42 .62 .48 .41 .52 .48 .34 58 .34 .41 .52NNAcc. 82.7 60.3 77.7 77.5 65.7 62.8 75.8 62.8 52.83 86.2 86.6 67.1Re. .88 .69 .71 .85 .71 .64 .59 .64 .53 .79 .81 .68Pre. .66 .58 .71 .62 .55 .52 .77 .52 .51 .76 .89 .70RMSE .38 .52 .41 .46 .49 .51 .35 .60 .63 .38 .35 .53SVMAcc. 79.3 74.7 50 75.8 75.8 62.8 74.7 79.4 69.8 71.5 84.4 65.7Re. .77 .80 .42 .90 .90 .64 .80 .8 .69 .81 .90 .60Pre. .63 .65 .42 .63 .63 .52 .65 .75 .69 .61 .79 .71RMSE .45 .51 .7 .47 .47 .60 .50 .45 .54 .53 .39 .58Ad.Acc. 86.2 81.8 44.4 70.6 82.6 65.7 71.2 88.2 60.3 72.4 88.2 62.8Re. .77 .8 .57 .76 .77 .71 .75 .86 .69 .81 .86 .76Pre. .77 .8 .36 .57 .77 .55 .62 .86 .58 .62 .86 .63RMSE .34 .37 54 .38 .37 .49 .41 .34 .52 .41 .34 .50

6 M. Mirzaei et al.

90:10 case in the 2-year period and the 60:40 case in the 3-year period prior to default.Nonetheless, multiple classifiers in the form of Ad enhanced NN in some of theexperiments. However, it did not enhance the NN prediction accuracy significantly.The standard deviation of model accuracy increased when the training-to-testing ratioincreased to 80:20 and 90:10, especially in the case of SVM (Fig. 2). The considerablevariation in accuracy obtained with different models across the time periods implies anovertraining problem in the 80:20 and 90:10 cases. Moreover, the standard deviationobtained with Adaboost was lower than single classifiers.

It is observed that the training-to-testing ratio affected prediction accuracy. Theresults indicate that the highest accuracy (0.956) was attained for a 60:40training-to-testing ratio and 2 years prior to default. Meanwhile, the best accuracy for3 years and 4 years (0.862 and 0.833, respectively) was attained at 90:10 training-to-testing ratio using Ad. Consequently, if most data are used for training and less are usedfor testing, larger disparities between training and testing would occur. Generally, thehigh segregation ratio is subject to greater training set overfitting, thus producing lowerprediction accuracy. It is obvious that the models’ accuracy decreased, when data from

Fig. 1. Predictive accuracy of DT, NN, SVM, and Ad for (a) 2-year time period, (b) 3-year timeperiod, and (c) 4-year time period.

Fig. 2. Standard deviation of prediction accuracy of DT, NN, SVM, and Ad across time periodsfor different training and testing ratios.

Prediction of Financial Distress for Electricity Sectors 7

earlier years were used. Indeed, the closer the default year (t), the higher the predictionaccuracy obtained was. On the other hand, it is argued that early detection of companies’financial distress is a vital concern to managers regarding obtaining sufficient time toavoid such financial distress. Accordingly, a trade-off between time period and predictionaccuracy is apparent. The findings depict that the predictive accuracy obtained for the3-year time period was close to that of the 2-year forecasting time period, when using DTas a single classifier. However, the predictive accuracy of the 4-year time to defaultdecreased significantly for different training-to-testing ratios.

Furthermore, the DT model developed “if-then” rules that led to insightful infor-mation (Table 2). Among the more robust rules developed by DT, the precedent ofNP/CA = 0.073 ranks as the first rule in the estimated tree. This reveals that the defaultprobability was high when the company’s net profit reached less than 7.3% of the valueof current assets held by the company. The critical values for other significant indi-cators that lead a company to default, including EBIT/TA, NP/L, S/CA and Ca/CL areshown in Table 3. As it is observed, financial leverage, liquidity and solvency ratiosalong with profitability ratios contribute in the DT model, while developing the“if-then” rules. Probability of default increases when the LTL/E ratio is higher than0.326. It indicates that probability of default increases as a result of using consequentialshare of debt. Likewise, decreasing the liquidity of companies (Ca/CL 0.116, then failIf LTL/E < 0.116 &S/CA

3.2 Importance of Financial Indicators

For default prediction in other research fields, such as system modelling, patternrecognition and so on, it is important to select a set of indicators with more predictioninformation. Reducing the number of insignificant or inappropriate features remarkablyreduces the running time of a learning algorithm and yields a more general concept.Feature selection was used in this study by ranking each financial indicator in terms ofits association with the target variable. To do so, principal component analysis(PCA) was applied for ranking and the financial indicators with higher predictionabilities were identified for the three different time periods. The findings in Table 3indicate that the most important indicators were from the dimension of profitability,including NP/TA and EBIT/TA. Other significant financial indicators were found to bethe same and mainly from solvency (i.e. current assets/current liabilities), liquidity (i.e.cash flow ratio) and financial leverage (i.e. liquidity to equity). Based on the rankingattained for the financial indicators, the numerical models were re-run following featureselection. Thus, for these experiments, only the selected financial indicators ranked byfeature selection were employed to forecast the default risk. Subsequent to the resultsobtained in the previous calculations, the training-to-testing ratio of 60:40 was used toreduce overfitting. It was found that employing feature selection improved the DT andNN model accuracy and reduced SVM model accuracy. However, the paired t-testrevealed that the models’ prediction accuracy did not change significantly afterremoving the unimportant variables from the models. It is obvious that the indicatorsselected from feature selection provided equal or higher prediction accuracy, whichenabled predicting whether companies are facing default (Table 4).

Table 4. Prediction results of DT, NN, SVM and Adaboost after feature selection.

t DT Changes for DT NN Changes for NN SVM Changes for SVM

t − 2Acc. .90 .47 .82 .004 .82 −.009Re. 1 .16 .71 −.07 .81 .04Pre. .83 .00 .88 .12 .81 .27RMSE .29 −.05 .39 −.01 .42 −.12t − 3Acc. .84 .02 .77 −.09 .71 −.16Re. .77 .05 .75 −.06 .81 −.08Pre. .84 −.04 .72 −.17 .61 −.23RMSE .37 −.04 .42 .07 .53 .08t − 4Acc. .79 .17 .62 −.04 .77 .12Re. .65 −.08 .63 −.04 .84 .10Pre. .89 .28 .66 −.03 .76 .10RMSE .45 −.07 .54 .01 .47 −.09

Prediction of Financial Distress for Electricity Sectors 9

4 Conclusion

In this study, two main questions were investigated: (1) Which data mining techniquescan predict the financial distress of electricity companies most accurately? (2) Whichfinancial indicators are most effective in this type of forecasting? The key contributionsof this study are as follows.

The prediction ability of the financial indicators was compared for different timeperiods, including 2, 3 and 4 years before the companies received the ST labels toinvestigate how early the signs of financial distress can be predicted with greateraccuracy. It is observed that the financial data attained acceptable accuracy for the2-year and 3-year time periods prior to default, while the accuracy decreased dra-matically for the 4-year period prior to default. Among the methods applied, NN wasidentified as the best technique for predicting financial default if compared with DT,SVM and Ad. Interesting findings relating to feature selection were that the mostsignificant indicators in the different time periods of 2 to 4 years were mostly the same.However, these indicators ranked differently for different time periods. In summary, itcan be stated that companies should use an efficient profitability management systemwith liquidity and solvency as a crucial part of their corporate policy framework.Besides, equal or improved accuracy was found for the various models subsequent tofeature selection compared with the models built using all financial indicators.

The result of this study can be applicable in the volatile financial markets such asIran, which is influenced by sanctions and financial instability. In concerns to thepolicies promoted in Iran for privatization of electricity sector, the upcoming situationmay lead these companies to default due to cut off subsidies. Therefore, an accurateearly warning system is required to alarm companies prior to bankruptcy. The findingsof this research provide an applicable technique for financial default prediction for theelectricity sector in Iran. The research presented in this study can be extended further.Based on the current findings, future research may investigate the predictive accuracyof different models across different countries and can include macroeconomic conditioneffects on financial distress.

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4. Tavanier: The details statistics of Iranian electricity industrial for electricity distribution in2012. Ministry of Niro, Tavanier Co. (2013)

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