Hemanth j artificial intelligence and applied mathematics in engineering problem 2020

Scope/Topics Conference Scope/Topics as not limited to: In Engineering Problems: • Machine Learning Applications • Deep Learning Applications • Intelligent Optimization Solutions • Robot

Artificial Intelligence

and Applied Mathematics

in Engineering Problems

D Jude Hemanth

Utku Kose Editors

Proceedings of the International

Conference on Artificial Intelligence

and Applied Mathematics in Engineering (ICAIAME 2019)

Lecture Notes on Data Engineering

and Communications Technologies 43

Lecture Notes on Data Engineering and Communications Technologies Volume 43

Series Editor

Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain

technologies and communications It will publish latest advances on the engineeringtask of building and deploying distributed, scalable and reliable data infrastructuresand communication systems.

The series will have a prominent applied focus on data technologies andcommunications with aim to promote the bridging from fundamental research ondata science and networking to data engineering and communications that lead toindustry products, business knowledge and standardisation

** Indexing: The books of this series are submitted to ISI Proceedings,MetaPress, Springerlink and DBLP **

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

D Jude Hemanth • Utku Kose

Editors

and Applied Mathematics

in Engineering Problems

Proceedings of the International Conference

Mathematics in Engineering

(ICAIAME 2019)

123

Suleyman Demirel UniversityIsparta, Isparta, Turkey

Lecture Notes on Data Engineering and Communications Technologies

ISBN 978-3-030-36177-8 ISBN 978-3-030-36178-5 (eBook)

https://doi.org/10.1007/978-3-030-36178-5

© Springer Nature Switzerland AG 2020

This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part

of the material is concerned, speci fically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on micro films or in any other physical way, and transmission

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to jurisdictional claims in published maps and institutional af filiations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

On behalf of the proceedings editors and the organization committee, it is with deephonor that I write this Preface to the Proceedings of the International Conference onArtificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019)held in Antalya, Manavgat (Turkey) The objective of the conference was to pro-mote in academia, industry, organizations and governments, progress and expan-sion of knowledge concerning artificial intelligence and mathematical modelingtechniques for advancing day-to-day affairs to make better and smart living Theconference provided the opportunity to exchange ideas on machine learning, deeplearning, robotics, algorithm design for intelligent solutions, image processing,prediction and diagnosis applications, operations research, discrete mathematicsand general engineering applications, to experience the state-of-the-art technolo-gies, identify solutions and build collaborations for real-time implementations Inthis context, the event provided a three-day, enjoyable scientific environment for allauthors and participants to share–discuss their research results and experiences with

an international audience

Based on reviews from the scientific committee and the external reviewers, atotal of 197 papers have been accepted to be presented within around 40 parallelsessions The proceedings are published by Springer under the Springer Series:Lecture Notes on Data Engineering and Communications Technologies, and theextended versions of papers with post-processing review will be published undersome reputable journals In terms of international scope, ICAIAME 2019 includedcontributions by 18 different countries such as Algeria, China, Cyprus, Denmark,England, France, India, Iraq, Jordan, Kuwait, Lebanon, Mexico, Pakistan, Palestine,Switzerland, Trinidad Tobago, Turkey and USA It is great to see the outcomes

of the research by the authors have found their way to the literature, thanks tovaluable efforts shown in that remarkable event

In addition to the contributed papers, a total of six invited keynote presentationswere delivered by top experts in artificial intelligence and applied mathematics

Dr Çetin Elmas highlighted the importance of ‘Artificial Intelligence in ProjectManagement,’ Dr Jude Hemanth covered technical aspects of ‘Innovative Artificial

v

Intelligence Approaches for Medical Image Analysis,’ Dr Paniel Reyes Cárdenas

Intelligence,’ Dr Ali Allahverdi enlightened the audience with regard to ‘How toPublish Your Paper in a Reputable Journal,’ Dr Ender Özcan discussed ‘RecentProgress in Selection Hyper-Heuristics for Intelligent Optimisation’ and finally

Dr Ekrem Savaş elaborated the topic titled ‘Some Sequence Spaces defined byInvariant Mean.’

The success of ICAIAME 2019 depends completely on the effort, talent andenergy of researchers in thefield of computer-based systems who have written andsubmitted papers on a variety of topics Praise is also deserved for the organizingand scientific committee members, and external reviewers, who have investedsignificant time in analyzing and assessing multiple papers, as holding and main-taining a high standard of quality for this conference The ICAIAME will act asstrong base for researchers and scientists in the form of that excellent referencebook

I would like to thank all authors and participants for their contributions

Anand Nayyar

Artificial intelligence (AI) is an exciting field of knowledge that had an explosion ofsophistication and technical nuance in the last few years Let us consider only howthe state of AI was purely hypothetical in many ways only 50 years ago and now wehave not only developments that were envisaged in the wildest imaginations, butdevelopments that were not even expected No doubt that AI is a field that hasincited us to question about the nature of what we define as intelligence and thelimits of our concepts about it

However, though the discipline of AI in itself is essentially transdisciplinary,there is an important connection with philosophy that has not always been under-lined properly: on the one hand because we need to every now and again stop andthink the meaning of the achievements we have gotten thus far On the other hand,philosophy becomes important to even question what we want to achieve We needphilosophy of AI to relate the achievements and plans that we engineer with thehighest purposes of humankind Indeed, no discipline of knowledge is alien tohuman ethical issues and AI is not the exception

One of the important lessons we have learned in the last few decades is, in myopinion, the ability to acknowledge that AI does not need to be necessarily modeled

in human intelligence, and that human minds have aspects that cannot be translatedinto modeling due to its own very nature of being self-conscious in ways thatartificial systems are not But the illustration also works for us: There are advan-tages that AI has given us that make humans recognize that we can flourish byintegrating to our life developments that are exclusive of AI systems and we couldnot do by ourselves For example, the world of mass communications has indeedmade people be easily connected and promoted an encounter of cultures thatotherwise can have little or no dialogue at all In this way, AI has made us morehuman and we can so giveflesh to people who were not visible to us before

A prominent aspect of the discussions between mathematicians, engineers,designers and philosophers is acknowledging that AI has grown in such a way thatillustrates us for having new ideas that are informing ethics, aesthetics, art, experi-mental sciences such as chemistry and metaphysics, medicine and even philosophy

vii

Philosophy of AI is then an important activity within the disciplines of AI:Engineers need the motivation to strive for better and deeper understanding of thecapacities of managing information A philosophical dose of thought helps theengineer to understand that her or his contribution is absolutely valuable and crucial

to the growth of humanity, and that technical advances are always a step forward indeveloping our humanity However, the philosophical dose of the engineer alsohelps her or him to acknowledge that there are ethical responsibilities to humanity,

to truth and to the advancement of AI The drive that has led us to where we are hasbeen an unrestricted desire for knowledge much more than economical rewards, forexample

This Springer edited collection at hand that came from the InternationalConference on Artificial Intelligence and Applied Mathematics in Engineering(ICAIAME 2019) is a great example of a sincere desire to have a dialogue guided

by truth and openness, and the human exchange of ideas has been a model to followfor other disciplines, since the mathematicians and engineers are less prone to beaffected by other egoistic interests but by a thirst of knowledge and inquiry All thecontributions connect in fascinating and innovative ways

Paniel Reyes Cárdenas

International Conference on Arti ficial Intelligence

and Applied Mathematics in Engineering 2019

Web:http://www.icaiame.com

Brie fly About

International Conference on Artificial Intelligence and Applied Mathematics inEngineering (ICAIAME 2019) held within April 20-21-22, 2019, at the Antalya,Manavgat (Turkey), which is the pearl of the Mediterranean, heaven corner ofTurkey and the fourth most visited city in the world

The main theme of the conference, which was held at Bella Resort & Spa withinternational participations along a three-day period, is solutions of artificialintelligence and applied mathematics in engineering applications The languages

of the ICAIAME 2019 are English and Turkish

Scope/Topics

Conference Scope/Topics (as not limited to):

In Engineering Problems:

• Machine Learning Applications

• Deep Learning Applications

• Intelligent Optimization Solutions

• Robotics/Soft Robotics and Control Applications

• Hybrid System-Based Solutions

• Algorithm Design for Intelligent Solutions

• Image/Signal Processing Supported Intelligent Solutions

• Data Processing-Oriented Intelligent Solutions

ix

• Prediction and Diagnosis Applications

• Linear Algebra and Applications

• Numerical Analysis

• Differential Equations and Applications

• Probability and Statistics

• Operations Research and Optimization

• Discrete Mathematics and Control

• Nonlinear Dynamical Systems and Chaos

• General Engineering Applications

Honorary Chairs

Organizing Committee

Malaysia

Muhammed MarufÖztürk Süleyman Demirel University, Turkey

Süleyman Demirel University, Turkey

Turkey

Igor Litvinchev Nuevo Leon State University, Mexico

Jose Antonio

Marmolejo

Panamerican University, Mexico

University, USA

Turkey

Turkey

MalaysiaAlexandrina Mirela

Pater

University of Oradea, Romania

J Anitha Karunya University, India

Muharrem Tolga

Sakalli

Trakya University, Turkey

and Technology “St Paul The Apostle”,Macedonia

Calp

Karadeniz Technical University, Turkey

of Puebla, Mexico

University, India

Vishal Kumar Bipin Chandra Tripathi Kumaon Institute

of Technology, India

Katarzyna

Kielce University of Technology, Poland

Keynote Speaks

Çetin Elmas

Gazi University, Turkey

“Artificial Intelligence in Project Management”

Ekrem Savaş

Usak University, Turkey

“Some Sequence Spaces Defined By Invariant Mean”

Ali Allahverdi

Kuwait University, Kuwait

“How to Publish Your Paper in a Reputable Journal”

Jude Hemanth

Karunya University, India

“Innovative Artificial Intelligence Approaches for Medical Image Analysis”Ender Ozcan

University of Nottingham, England

“Recent Progress in Selection Hyper-heuristics for Intelligent Optimisation”Paniel Reyes Cárdenas

Popular Autonomous University of the State of Puebla, Mexico

“Diagrammatic Reasoning, Topological Mathematics and Artificial Intelligence”

As the editors, we would like to thank Dr Gül Fatma TÜRKER (Süleyman DemirelUniversity, Turkey) for her valuable efforts on pre-organization of the book contentand the Springer team for their great support to publish the book

xv

State and Trends of Machine Learning Approaches in Business:

An Empirical Review 1

Samia Chehbi-Gamoura, Ridha Derrouiche, Halil-Ibrahim Koruca,

and Umran Kaya

Piecewise Demodulation Based on Combined Artificial

Neural Network for Quadrate Frequency Shift Keying

Communication Signals 17

Nihat Daldal and Kemal Polat

A New Variable Ordering Method for the K2 Algorithm 25

Betül Uzbaş and Ahmet Arslan

A Benefit Optimization Approach to the Evaluation

of Classification Algorithms 35

Shellyann Sooklal and Patrick Hosein

Feature Extraction of Hidden Oscillation in ECG Data

via Multiple-FOD Method 47

Ekin Can Erkuş and Vilda Purutçuoğlu

Financial Fraud Detection Through Artificial Intelligence 57

Roman Rodriguez-Aguilar, Jose A Marmolejo-Saucedo, Pandian Vasant,

and Igor Litvinchev

Deep Learning-Based Software Energy Consumption Profiling 73

Implementation of GIS for the Sanitation System in El-Oued City 84

Brarhim Lejdel

Prediction of Potential Bank Customers:

Application on Data Mining 96

xvii

The Model Selection Methods for Sparse Biological Networks 107

Mehmet Ali Kaygusuz and Vilda Purutçuoğlu

ICS Cyber Attack Analysis and a New Diagnosis Approach 127

Ercan Nurcan Yılmaz, Hasan Hüseyin Sayan, Furkan Üstünsoy,

Serkan Gönen, Erhan Sindiren, and Gökçe Karacayılmaz

Investigating the Impact of Code Refactoring Techniques

on Energy Consumption in Different Object-Oriented

Programming Languages 142

Ibrahim Sanlialp and Muhammed Maruf Ozturk

Determination of Numerical Papillae Distribution Affecting

the Taste Sensitivity on the Tongue with Image

Processing Techniques 153

SefaÇetinkol and İsmail Serkan Üncü

Comparison of Image Quality Measurements in Threshold

Determination of Most Popular Gradient Based Edge Detection

Algorithms Based on Particle Swarm Optimization 171

A Hybrid Approach for the Sentiment Analysis of Turkish

Twitter Data 182

H A Shehu and S Tokat

Text Mining and Statistical Learning for the Analysis of the Voice

of the Customer 191

Rosalía Andrade Gonzalez, Roman Rodriguez-Aguilar,

and Jose A Marmolejo-Saucedo

A Decision Support System for Role Assignment in Software

Project Management with Evaluation of Personality Types 200

Azer Celikten, Eda Kurt, and Aydin Cetin

A Survey of Methods for the Construction of an Intrusion

Detection System 211

Abdel Karim Kassem, Shymaa Abo Arkoub, Bassam Daya,

and Pierre Chauvet

A Novel Hybrid Model for Vendor Selection in a Supply Chain

by Using Artificial Intelligence Techniques Case Study:

Petroleum Companies 226

Mohsen Jafari Nodeh, M Hanefi Calp, and İsmail Şahin

Effect the Number of Reservations on Implementation of Operating

Room Scheduling with Genetic Algorithm 252

Tunahan Timuçin and Serdar Biroğul

Identifying Driver Behaviour Through Onboard Diagnostic Using

CAN Bus Signals 266

Gül Fatma Türker and Fatih Kürşad Gündüz

Statistical Learning Applied to Malware Detection 276

Roman Rodriguez-Aguilar and Jose A Marmolejo-Saucedo

A Novel Model for Risk Estimation in Software Projects Using

Artificial Neural Network 295

M Hanefi Calp and M Ali Akcayol

Routing of Maintenance and Repair Operations of Mobile Based

Fault Notifications of Municipal Services 320

Tuncay Yiğit and Huseyin Coskun

Safe Map Routing Using Heuristic Algorithm Based on Regional

Crime Rates 335

Atakan Alpkoçak and Aydin Cetin

Image Spam Detection Using FENOMAA Technique 347

Aziz Barbar and Anis Ismail

Fault Detection of CNC Machines from Vibration Signals Using

Machine Learning Methods 365

Huseyin Canbaz and Kemal Polat

Energy Hub Economic Dispatch by Symbiotic Organisms

Search Algorithm 375

Uğur Güvenç, Burçin Özkaya, Hüseyin Bakir, Serhat Duman,

and Okan Bingöl

An Extended Business Process Representation for Integrating IoT

Based on SWRL/OWL 386

Lynda Djakhdjakha, Djehina Boukara, Mounir Hemam,

and Zizette Boufaida

A Review on Watermarking Techniques for Multimedia Security 406

Hüseyin Bilal Macit and Arif Koyun

Realization of Artificial Neural Networks on FPGA 418

Mevlut Ersoy and Cem Deniz Kumral

Estimation of Heart Rate and Respiratory Rate from

Photoplethysmography Signal for the Detection of Obstructive

Sleep Apnea 429

E Smily Jeya Jothi and J Anitha

Improved Social Spider Algorithm via Differential Evolution 437

Fatih AhmetŞenel, Fatih Gökçe, and Tuncay Yiğit

Gender Determination from Teeth Images via Hybrid Feature

Extraction Method 446

Betül Uzbaş, Ahmet Arslan, Hatice Kök, and Ayşe Merve Acılar

Simulated Annealing Algorithm for a Medium-Sized TSP Data 457

Mehmet Fatih Demiral and Ali Hakan Işik

Gene Selection in Microarray Data Using an Improved Approach

of CLONALG 466

Ezgi DenizÜlker

Improvement for Traditional Genetic Algorithm to Use

in Optimized Path Finding 473

Hasan Alp Zengin and Ali Hakan Işik

Investigation of the Most Effective Meta-Heuristic Optimization

Technique for Constrained Engineering Problems 484

Hamdi Tolga Kahraman and Sefa Aras

The Development of Artificial Intelligence-Based Web Application

to Determine the Visibility Level of the Objects on the Road 502

Mehmet Kayakuş and Ismail Serkan Üncü

A Study on the Performance of Base-m Polynomial Selection

Algorithm Using GPU 509

Oğuzhan Durmuş, Umut Can Çabuk, and Feriştah Dalkılıç

Analysis of Permanent Magnet Synchronous Motor by Different

Control Methods with Ansys Maxwell and Simplorer Co-simulation 518

Huseyin Kocabiyik, Yusuf Oner, Metin Ersoz, Selami Kesler,

and Mustafa Tumbek

A Comparison of Data Mining Tools and Classification Algorithms:

Content Producers on the Video Sharing Platform 526

Ercan Atagün and İrem Düzdar Argun

Normal Mixture Model-Based Clustering of Data Using

Genetic Algorithm 539

Maruf Gogebakan and Hamza Erol

Analyzing and Processing of Supplier Database Based

on the Cross-Industry Standard Process for Data Mining

(CRISP-DM) Algorithm 544

Mohsen Jafari Nodeh, M Hanefi Calp, and İsmail Şahin

On the Prediction of Possibly Forgotten Shopping Basket Items 559

Anderson Singh and Patrick Hosein

Consensus Approaches of High-Value Crypto Currencies

and Application in SHA-3 572

Murat Emeç, Melike Karatay, Gökhan Dalkılıç, and Erdem Alkım

Estimation of Foam Concrete Mixture Rate with Randomed

Forest Algorithm 584

Şemsettin Kilinçarslan, Emine Yasemin Erkan, and Murat Ince

Pre-processing Effects of the Tuberculosis Chest X-Ray Images

on Pre-trained CNNs: An Investigation 589

Erdal Tasci

A Comparison of Neural Network Approaches for Network

Intrusion Detection 597

Mehmet Uğur Öney and Serhat Peker

A Case Study: Comparison of Software Cost Estimation

of Smart Shopping List Application 609

Tuncay Yiğit and Huseyin Coskun

Forecasting Housing Prices by Using Artificial Neural Networks 621

Tolga Yesil, Fatma Akyuz, and Utku Kose

High Power Density and High Speed Permanent Magnet

Synchronous Generator Design 633

Benhar Aydogan, YusufÖner, Metin Ersoz, Selami Kesler,

and Mustafa Tumbek

Selection and Training of School Administrators

in Different Countries 643

Fatma Köprülü, Behcet Öznacar, and Nevriye Yilmaz

Security on Cloud Computing Using Pseudo-random Number

Generator Along with Steganography 654

Moolchand Sharma, Suman Deswal, Jigyasa Sachdeva,

Varun Maheshwari, and Mayank Arora

Neural Network Prediction of the Effect of Nanoparticle

on Properties of Concrete 666

Şemsettin Kilinçarslan, Metin Davraz, Nanh Ridha Faisal, and Murat Ince

Tangibility of Fuzzy Approach Risk Assessment in Distributed

Software Development Projects 676

Kökten Ulaş Birant, Ali Hakan Işık, and Mustafa Batar

A Simple Iterative Algorithm for Boolean Knapsack Problem 684

Fidan Nuriyeva, Urfat Nuriyev, and Onur Ugurlu

A Review of the Solutions for the Container Loading Problem,

and the Use of Heuristics 690

Merve Aydemir and Tuncay Yigit

A Deep Learning Model Based on Convolutional Neural Networks

for Classification of Magnetic Resonance Prostate Images 701

Fatih Uysal, Fırat Hardalaç, and Mustafa Koç

Effect of Representation of Information in the Input of Deep

Learning on Prediction Success 709

Hikmet Yücel

The Applicability of Instructional Leadership

in Educational Institution 724

Behcet Oznacar and Gulyuz Debes

Production of Myoelectrically Controlled 3D Bionic Hand 736

Ferdi Alakus, Pinar Koc, Orhan Duzenli, and Kenan Unlu

The Arab Students’ Needs and Attitudes of Learning English:

A Study of Computer Engineering Undergraduates in Cyprus 744

Fatma Köprülü, Seda Cakmak, and Arhun Ersoy

Developing a Hybrid Network Architecture for Deep

Convolutional Neural Networks 750

H Hüseyin Sayan, Ö Faruk Tekgözoğlu, Yusuf Sönmez, and Bilal Turan

Blockchain-Based Secure Recognized Air Picture System Proposal

for NATO Air C2 Capabilities 758

Enis Konacakli and Enis Karaarslan

A New Genetic Algorithm for the Maximum Clique Problem 766

Gozde Kizilates Evin

Evaluation of Primary School Teachers’ Resistance to Change 775

BehcetÖznacar and Nevriye Yilmaz

Fuzzy Logic and Correlation-Based Hybrid Classification

on Hepatitis Disease Data Set 787

M Sinan Basarslan, H Bakir, andİ Yücedağ

Entropy-Based Skin Lesion Segmentation Using Stochastic Fractal

Search Algorithm 801

Okan Bingöl, Serdar Paçacı, and Uğur Güvenç

Providing the Moment of the Parabolic Reflector Antenna

in the Passive Millimeter Wave Imaging System

with the Equilibrium Weights 812

Mehmet Duman and Alp Oral Salman

Convolutional Auto-Encoder Based Degradation Point Forecasting

for Bearing Data Set 817

Abdullah Taha Arslan and Ugur Yayan

Moth Swarm Algorithm Based Approach for the ACOPF

Considering Wind and Tidal Energy 830

Serhat Duman, Lei Wu, and Jie Li

Churn Analysis with Machine Learning Classification Algorithms

in Python 844

OnurÖzdemir, Mustafa Batar, and Ali Hakan Işık

Real Time Performance Comparison of Multi-class Deep Learning

Methods at the Edge 853

Doruk Sonmez and Aydin Cetin

A Novel Blood Pressure Estimation Method with the Combination

of Long Short Term Memory Neural Network and Principal

Component Analysis Based on PPG Signals 868

Umit Senturk, Kemal Polat, and Ibrahim Yucedag

Design and Implementation of SDN-Based Secure Architecture

for IoT-Lab 877

Enis Karaarslan, Eren Karabacak, and Cihat Cetinkaya

Feature Selection by Using DE Algorithm and k-NN Classifier 886

Fatih AhmetŞenel, Asım Sinan Yüksel, and Tuncay Yiğit

Intelligent Water Drops Algorithm for Urban Transit Network

Design and Frequency Setting 894

Buket Capali and Halim Ceylan

Tweet and Account Based Spam Detection on Twitter 898

Kübra Nur Güngör, O Ayhan Erdem, and İbrahim Alper Doğru

A Walking and Balance Analysis Based on Pedobarography 906

Egehan Cetin, Suleyman Bilgin, and Okan Oral

Optimization of PMSM Design Parameters Using Update

Meta-heuristic Algorithms 914

Cemal Yılmaz, Burak Yenipınar, Yusuf Sönmez, and Cemil Ocak

Improve or Approximation of Nuclear Reaction Cross Section

Data Using Artificial Neural Network 935

Effect of the Clonal Selection Algorithm on Classifiers 949

Tuba Karagül Yildiz, Hüseyin Demirci, and Nilüfer Yurtay

Analyzing the Energy Potential of Hydroelectric Power Plant

on Kura River 960

Tamper Detection and Recovery on RGB Images 972

Hüseyin Bilal Macit and Arif Koyun

Assessment of Academic Performance at Akdeniz University 982

Taha Yiğit Alkan, Fatih Özbek, Melih Günay, Bekir Taner San,

and Olgun Kitapci

Predicting Breast Cancer with Deep Neural Networks 996

Abdulkadir Karaci

Utilizing Machine Learning Algorithms of Electrocardiogram

Signals to Detect Sleep/Awake Stages of Patients with Obstructive

Sleep Apnea 1004

Muhammed Kürşad Uçar, Ferda Bozkurt, Cahit Bilgin,

and Mehmet Recep Bozkurt

Development of a Flexible Software for Disassembly Line

Balancing with Heuristic Algorithms 1014

Ümran Kaya, Halil İbrahim Koruca, and Samia Chehbi-Gamoura

Parametrical Analysis of a New Design Outer-Rotor Line Start

Synchronous Motor 1027

Mustafa Tümbek, Selami Kesler, and Yusuf Öner

I-Statistically Localized Sequence in 2-Normed Spaces 1039

On the Notion of Structure Species in the Bourbaki’s Sense 1047

Aslanbek Naziev

On the Jost Solutions of the Zakharov-Shabat System

with a Polynomial Dependence in the Potential 1070

Anar Adiloglu Nabiev

Author Index 1079

Approaches in Business: An Empirical Review

Samia Chehbi-Gamoura1(&), Ridha Derrouiche1,Halil-Ibrahim Koruca2, and Umran Kaya31

EM Strasbourg Business School, University of Strasbourg,HuManiS (EA 7308), Strasbourg, France

{samia.gamoura,ridha.derrouiche}@em-strasbourg.eu

2 Department of Industrial Engineering, Süleyman Demirel University,

Isparta, Turkeyhalilkoruca@sdu.edu.tr3

Department of Industrial Engineering, Antalya Bilim University,

Antalya, Turkeyumran.kaya@antalya.edu.tr

Abstract Strong competition is imposing to enterprises an incessant need forextracting more business values from collected data The business value ofcontemporary volatile data derives from the meanings mainly for market ten-dencies, and overall customer behaviors With such continuous urge to minevaluable patterns from data, analytics have skipped to the top of research topics.One main solution for the analysis in such context is‘Machine Learning’ (ML).However, Machine Learning approaches and heuristics are plenty, and most ofthem require outward knowledge and deep thoughtful of the context to learn thetools fittingly Furthermore, application of prediction in business has certainconsiderations that strongly affects the effectiveness of ML techniques such asnoisy, criticality, and inaccuracy of business data due to human involvement in

an extensive number of business tasks The objective of this paper is to informabout the trends and research trajectory of Machine Learning approaches inbusinessfield Understanding the vantages and advantages of these methods canaid in selecting the suitable technique for a specific application in advance Thepaper presents a comprehensively review of the most relevant academic publi-cations in the topic carrying out a review methodology based on imbricatednomenclatures Thefindings can orient and guide academics and industrials intheir applications within business applications

Keywords: Machine learning
Analytics
Artificial intelligence
Business

information systems

1 Introduction

Since the early 2000’s, Business Information Systems (BIS) offer data collections andanalytical approaches to enterprises [1] As such, they merge fundamental theories ofmanagement, processes and Information Systems (IS) theory with engineering tech-nologies to manage theflows organization of data

© Springer Nature Switzerland AG 2020

D J Hemanth and U Kose (Eds.): ICAIAME 2019, LNDECT 43, pp 1 –16, 2020.

https://doi.org/10.1007/978-3-030-36178-5_1

In BIS, a number of approaches with adaptive and learning abilities are beingintegrated to mine the business operations and improve interacting with stakeholders[2] These approaches, commonly named Machine Learning (ML) approaches, con-stitute a new gold source for identifying new business values for organizations [3] Inthe academic side, research scientists believe that ML methods present today anavoidable opportunity for enterprises to process their data-business intelligently instead

of the traditional recorded and unexploited data sets [4] This is especially true with therapid growth of Big Data phenomenon [5] Furthermore, unlike traditional informationsystems, contemporary and future BIS will require the integration of mature andscalable ML techniques in all levels of business processes [6] such as opinion mining[7], risk management [8], recommendation systems [9], Business Process Management(BPM) [10], and so forth

Technically, many applied software programs have been developed to enable ferent types of learning algorithms [11] These algorithms have successfully proven to

dif-be of a great business value in containing valuable implicit regularities that can dif-berevealed autonomously [12]

To ease the use of ML techniques in BIS concerns, it is indispensable to ulate the empirical observations acquired on these approaches from the existing works[13] A methodical literature review can be a powerful tool to understand thoroughlythe evolution trends and answer numerous research questions about the applicability ofthese methods [14] However, academic research is lacking in extensive reviews thatclassify all the business activities in one panoramic view The existing reviews havealmost of them focused on a specific concern, such as decision systems [1], knowledgemanagement [15], accounting [11], product design and engineering [16], and so forth.The methodology we provide in this paper is an extensive analysis of the academicliterature regarding the use of ML approaches In order to perform, the proposedapproach identifies five nomenclatures based on five areas of items as categories Then,

recapit-a set of three structuredfindings are extracted and justified empirically based on MLapplications on enterprises scopes Furthermore, the methodology highlights a number

of research gaps that require serious consideration and more efforts from academiccommunity The main purpose is to situate the current status and the potential trends inthe application of ML approaches in BIS

The remainder of the paper is arranged as follows: Sect.2 provides a backgroundoverview of BIS and ML applications Section3 details the methodology followed inthe analysis of the literature Findings and empirical results are outlined in Sect.4.Section5concludes the paper with the relevant outcomes and opened views

2 Background and Research Gaps

Machine Learning approaches are the class of computational methods that automate theacquisition of knowledge from experience [17] One object in applying such methods

in BIS is to develop tools, heuristics, and techniques that can supplement domainexpertise in engineering and modeling tasks [11] such as stock prediction [18], suppliermanagement [19], and bankruptcy prediction [20] Such methods have the ability to

2 S Chehbi-Gamoura et al

relieve the workload of human workers [14], provide unseen patterns [6], and diminishirregularities in human errors [21].

ML applications in BIS have certain considerations that influence the performance

of the applied learning technique [22] Business data is characterized by noise asinaccuracies due to human involvement in an extensive number of business tasks [16]

in addition to the accumulated unstructured data in Big Data environment [23] In fact,this impreciseness strongly influences the effectiveness of ML methods [12] To clarifythe motives of using such advanced methods in business, we provide the main featuresand challenging points related to the business and managementfield

For decades, BIS have provided management with powerful and improved tional abilities with the major concern of studying the business information and itssourcing, movements and usage within organizations BIS incorporates both manualtasks and automated computing tasks The main objective of such systems is toadvance the services and capabilities of programs and humans to enable them to extractbusiness valuable aspects

computa-The landscape of management and applications in BIS goes from simply operations

of printing press to more advanced word-wide, mobile and cloudified operations [19]

(PLM) [25], Customer Relationship Management (CRM) [26], Supplier RelationshipManagement (SCM) [27] illustrate clearly the need to use ML as a required support incontemporary Big Data Analytic (BDA) environments To perform, BIS are required tooperate data analytics to investigate three main landmarks as classified in [11]: (1) Past:What has occurred, also called‘descriptive analytics’, (2) Present: is the improved way

to do through ‘prescriptive analytics’, and (3) Future: What will happen, namely

‘predictive analytics’

The above challenges are likely to be encouraged by the advent of Big Data toconstitute reasons of integrating ML approaches in different BIS platforms and solu-tions such as predictive maintenance [28], predictive scheduling [29], predictivemarketing [30], etc On the other hand, one of the motivations of integrating MLmethods is the outperforming of those methods in many other common challenges inthe other applicativefields such as medicine [31], chemical physics [32], neurosciences[33], and communication technologies [34], and many others The other significantmotivation is the availability of data in the advent of Big Data environment while most

of academics think that the coming back of ML algorithm is almost encouraged by BigData paradigm Definitely, because of the massive records of business data and theneed of enterprises to seek for business value

The examination of the academic bibliography in ML business applications includes anabundant methods, sub-methods, heuristics, and techniques Commonly, theseapproaches are divided into three subdomains: supervised learning, unsupervisedlearning, and reinforcement learning

• Supervised Machine Learning (SML): The main objective of those methods focuses

on classification and prediction concerns [9] where models are made for expecting avariable that supports one of a pre-arranged set of values [30] Classification definesthe assignment of data into predefined groups (classes) and learns the relationshipbetween the other variables and the target class [6] Classification and predictionpurposes can use the same approaches but are differentiated in the handled data[28] If the approach is applied to existing records, it has a classification purpose [2].But when applied to a new data for which the class is unknown, it becomes aprediction [35] The main advantage of these methods is their robustness in pro-cessing large data sets [19] However, one disadvantage of these methods is thatwhen a problem is easy to classify and its boundary function is more complex than

it needs to be, this function is expected to over-fit [35] Likewise, when a problem istoo complex and the function is not prevailing sufficiently, the boundary under-fits[36] Figure1 illustrates a simple model of classification and prediction methodwith 2 variables and 2 classes

• Unsupervised Machine Learning (UML): In unsupervised learning, data aregrouped and classified without labialization [37] Two important branches exist:– Clustering: Is a common class of approaches that are used in several fields,including ML [38] The method classifies a set of objects into different clusters(groups), so that the data in each group are characterized by one or moresimilarities of traits [39] The power of this approach is in the ability of iden-tifying thick and thin regions byfinding correlations among data attributes, andthen, discovering complete distribution patterns within a reasonable amount oftime [40] However, as clustering is basically a statistical algorithm, thus itsmajor drawback is in its slackness in bulky databases due to memory restrictionsand the extensive running times [41]

– Association-rules: This class of approaches defines rules that administer therelationships among sets of entities [42] and aim to reveal patterns between thevariable factors [43] The powerful ability of association rules is their generalapplicability and flexibility to be integrated in all business concerns [44].Fig 1 Illustration of supervised machine learning mechanism (2 variables and 2 classes)

4 S Chehbi-Gamoura et al

However, their main drawback is in a huge number of parameters, understandable for non-experts, and the acquired rules are often too many withlow clarity [45].

non-• Reinforcement Machine Learning (RML): The reinforcement learning approachesenable learning from feedback received through interactions with an externalenvironment [46] In these approaches, input/output combinations are not presented,and selected actions are implicitly adjusted [47]

The examination of literature reveals plenty cases of ML applications in business.Table1 summarizes the most relevant among them with respect to some relevantapplicationfields

3 Proposed Methodology

Before elaborating the review strategy in our bibliography analysis, we pro-pose aclassification model through which we construct five matrices: (1) Business Applica-tion (Table2), (2) business area (Table3), (3) data environment concept (Table4),(4) technical platform (Table5), and (5) machine learning approaches (Table6) Each

Table 1 Nomenclature 1: business application

table embeds multiple hierarchical levels in nomenclatures of levels, depending on thecontent of the publications we examined.

The conducting of the literature review analysis follows the procedure of data ration in the sub-sequent section as depicted in Fig.2

prepa-Step 1: Data Collection:By means of Harzing’s Publish or Purish V.5® tool [49],

we queried out the academic publications in the topic of ML and BIS (from 2010 to2016) Parameters of Harzing’s search request are enumerated in Table7

Table 2 Nomenclature 1: business application

Level 1 Information technology and information systems

Level 2 Enterprise information systems (EIS)

Level 3 Advanced information systems research (AISR)Level 3 ERP

Level 2 Management information systems (MIS)

Level 3 BPMLevel 3 Workflow managementLevel 3 Data management researchLevel 2 Transaction processing systems (TPS)

Level 3 Data privacy/Security managementLevel 3 Data accuracy managementLevel 2 Knowledge management

Level 3 AssessmentLevel 3 Ontology management (OM)Level 3 Natural language processing (NLP)Level 1 Design/engineering/manufacturing

Level 2 Product design/engineering/manufacturing

Level 3 Product design/engineeringLevel 3 Product manufacturingLevel 3 Product information managementLevel 2 Manufacturing systems

Level 3 Factory/machines design/engineeringLevel 3 Factory/machines manufacturingLevel 3 Factory/machines information managementLevel 3 Job shop scheduling

Level 3 Factory/machines design/engineeringLevel 3 Fault diagnostics

Level 1 Hybrid business applications?

6 S Chehbi-Gamoura et al

Step 2: Data Filtering: After sorting the records (publications), we cleansed andfiltered the database by removing the undesirable columns (ranks, ISSN, types) androws (citations, books, reports, white papers, patents), publications with topics thatare not linked to the enterprises systems (for example [50]) We kept only inter-esting columns (cites, authors, title, year, source, publisher, URL) and rows(journals and conferences).

Step 3: Data Aggregation:Each for wasfilled a form for each row (publication) ofthe nomenclatures in the matching cells The purpose of using this matching process

is to determine which research (publication) is used and in which item in thenomenclatures

At the end of this procedure, five filled nomenclatures were the base of a analysis The objective is having a reading of these date following three main axes inML-BIS research: (1) Chronology and trends of research, (2) Scope and purpose ofresearch, and (3) Industrial and academic impact of research The empirical results areconducted to answer those three main axes in the next section

cross-Table 3 Nomenclature 2: business area

Level 1 AccountingLevel 1 HealthcareLevel 1 IndustryLevel 1 EconomyLevel 1 MarketingLevel 1 CommerceLevel 1 Management

Table 4 Nomenclature 3: data environment concept

Level 1 Big data analytics (BDA)Level 1 Competitive intelligence (CI)Level 1 Business intelligence (BI)Level 1 Data mining (DM)Level 1 Traditional data analytics (DBA)Level 1 Hybrid data environment?

Table 5 Nomenclature 4: technical platform

Level 1 On-promiseLevel 1 Web-basedLevel 1 Big dataLevel 1 Cloud computingLevel 1 Grid computingLevel 1 Hybrid technical context?

4 Empirical Analysis and Findings

This section presents the empirical results based on the aforementioned methodology.Through three keyfindings, we summarized publishing evolution by frequency, typesand categories, distribution of data by scopes and purposes, andfinally the distribution

of techniques by businessfields

Table 6 Nomenclature 5: machine learning approaches

Level 1 Supervised learning

Level 2 Classification/prediction

Level 3 Supervised ANNLevel 3 Fisher’s linear discriminantLevel 3 Regression

Level 3 Polynomial regressionLevel 3 Linear regressionLevel 3 Maximum entropyLevel 3 k-nearest neighbor (k-NN)Level 3 Decision trees (DT)Level 3 Conditional randomfields (CRF)Level 3 Naive Bayes classifier

Level 3 Bayesian networksLevel 3 SVM

Level 3 Case-bases reasoning (CBR)Level 3 Hidden Markov modelsLevel 1 Unsupervised learning

Level 2 Clustering

Level 3 k-meansLevel 3 Mixture modelsLevel 3 Hierarchical cluster analysisLevel 2 Unsupervised ANN

Level 3 Hebbian learningLevel 3 Generative adversarial networks (GAN)Level 2 Association-rule

Level 3 FP-growthLevel 3 Apriori algorithmLevel 1 Reinforcement learning

Level 2 SarsaLevel 2 Q-learningLevel 1 Hybrid approach?

Level 1 All approaches (review papers)

8 S Chehbi-Gamoura et al

4.1 Finding 1: Chronological Evolution of ML-BIS Research

In Fig.3, we illustrate the tendencies of evolution (numbers and rates) of publicationschronologically per types, categories, and frequencies of cites for the periods 2010–2018

As clearly illustrated in Fig.3, the frequency of papers have taken more attentionfrom year to year: It was jumped from 1%–0% to 1%–14% between 2010 and 2013,and then 3%–26% from 2014 to 2016, to finally increase more than the double (84%)

in 2018 Following the trajectory of tendency line, the growth is going faster since

2016 Therefore, this lead us to think that ML use in BIS will continue taking moreconsideration in the next years

Fig 2 Proposed method of review

Table 7 Search request in Harzing’s Publish or Purish V.5® [49]

Maximum papers number 1000

Any of the words {Machine learning, Business, Information, System}Publication type Journal

Search engine Google scholar engine

Location Title, abstract, key words

Fig 3 Year–wise growth of publications (cites, types, categories) (finding 1).

10 S Chehbi-Gamoura et al

About the bars graph about frequency of papers by categories (review papers,surveys or original researches), we notice the proportion of use of original researches istwice higher than literature studies (63%–32%), as also illustrated in the pie graph.However, when examining in details by years, this proportion kept true except for theyear 2015 (11%–9%) This seems rational, as for any new paradigm in a research field;researches must go through a profound analysis of literature before applications in thebeginning stage Although ML methods are not new, their use in the business envi-ronment remains an innovative topic However, the surveys do not exceed 5% overalland 1% per year, due to the scarcity of practical cases.

Table8 illustrates the rate (number) of publications by journals with the businessapplications (scopes)

Table 8 Distribution of publications by business scopes (finding 2)

4.3 Finding 3: Distribution of ML Techniques in BIS Research

In thisfinding, we are interested to study the influence of the business application areaand construct an influence graph among these criteria The synthesis graph in Fig 4

aims to reveal interdependencies among these criteria and their assets To do this, wesuperposed the nomenclatures 1 (Business Application, Level 2) and 5 (MachineLearning Approaches, Level 2) for only original researches (132 publications)

As shown in Fig.4, the‘Enterprise Information Systems (EIS)’ is the most populararea with 36% while the areas of Management Information systems (MIS)’, ‘Trans-action Processing Systems (TPS)’, and ‘Decision Support Systems (DSS)’ are thefollowingfields with a rate halved to 19% The other fields of ‘Knowledge Manage-ment (KM)’, ‘Product Design/Engineering/Manufacturing‘, and ‘Manufacturing Sys-tems (MS)’ are ranked lastly with insignificant rates of respectively 4%, 2%%, and 1%.Firstly, in EIS wefind mainly ‘Advanced Information Systems Research (AISR)’ and

‘Enterprise Resource Planning (ERP)’ Secondly, in Management Information Systems

(WM), and Data Management Research (DMR) Thirdly, in Transaction ProcessingSystems (TPS), we note predominantly the sub-fields ‘Data Privacy/Security Man-

Integrity/Compliance Management (DTCM)’ And lastly, in ‘Decision support systems

(‘Predictive Aid-Decision (PAD)’, ‘Opinion Mining & Sentiment Analysis OMSA)’,

‘Risk Management RM)’, ‘Business Intelligence (BI)’, ‘Information Filtering System(IFS)’ (‘Recommender System (RS)’), and ‘Content Discovery Platform (CDP)’)

To summarize, we see that classification and prediction techniques are the mostused ML approaches in BIS, including mostly supervised learning with a majority rates

of 98% in EIS, 96% in MIS, 87% in TPS, and 69% in DSS

ClassificaƟon/predicƟon (Supervised Learning)

Clustering (Unsupervised Learning)

unsupervised ANN (Unsupervised Learning)

AssociaƟon-rule (Unsupervised Learning)

ClassificaƟon/predicƟon (Supervised Learning)

Clustering (Unsupervised Learning)

unsupervised ANN (Unsupervised Learning)

AssociaƟon-rule (Unsupervised Learning)

ClassificaƟon/predicƟon (Supervised Learning)

Clustering (Unsupervised Learning)

unsupervised ANN (Unsupervised Learning)

AssociaƟon-rule (Unsupervised Learning)

ML in Decision Support Systems (DSS)

ML in Product Design/Engineering/Manufacturing (PDEM)

50%

50% 0%

0%

0%

ClassificaƟon/predicƟon (Supervised Learning) Clustering (Unsupervised Learning) unsupervised ANN (Unsupervised Learning) AssociaƟon-rule (Unsupervised Learning) Reinforcement Learning

ML in Knowledge Management (KM)

Fig 4 Distribution of using machine learning techniques in business information systems(finding 3)

12 S Chehbi-Gamoura et al

5 Conclusion and Discussion

In this paper, we have examined the evolution of Machine Learning approaches in therelevant research works of Business Information Systems through a comprehensiveliterature review By this paper, we contribute in drawing a guideline for academic andpractitioners regarding the application of the ML techniques in resolving businessconcerns

Throw the deep analysis of the examined literature in this paper; we submit that theapplication of machine learning to enterprises systems is just at its beginning and willcertainly spread in the near and far futures In addition, resultsfigured out that clas-

sification and prediction techniques are the most predominant ML approaches that haveconducted research in BIS during the last ten years

The main outcome in this paper led our thinking to understand that businessinformation systems are moving to mutate into data-driven models in the middle andlong terms future

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