Advances in Intelligent Systems and Computing

Volume 1197

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

Advisory Editors

Nikhil R. Pal, Indian Statistical Institute, Kolkata, India

Rafael Bello Perez, Faculty of Mathematics, Physics and Computing,Universidad Central de Las Villas, Santa Clara, Cuba

Emilio S. Corchado, University of Salamanca, Salamanca, Spain

Hani Hagras, School of Computer Science and Electronic Engineering,University of Essex, Colchester, UK

László T. Kóczy, Department of Automation, Széchenyi István University,Gyor, Hungary

Vladik Kreinovich, Department of Computer Science, University of Texasat El Paso, El Paso, TX, USA

Chin-Teng Lin, Department of Electrical Engineering, National ChiaoTung University, Hsinchu, Taiwan

Jie Lu, Faculty of Engineering and Information Technology,University of Technology Sydney, Sydney, NSW, Australia

Patricia Melin, Graduate Program of Computer Science, Tijuana Instituteof Technology, Tijuana, Mexico

Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro,Rio de Janeiro, Brazil

Ngoc Thanh Nguyen , Faculty of Computer Science and Management,Wrocław University of Technology, Wrocław, Poland

Jun Wang, Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publicationson theory, applications, and design methods of Intelligent Systems and IntelligentComputing. Virtually all disciplines such as engineering, natural sciences, computerand information science, ICT, economics, business, e-commerce, environment,healthcare, life science are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, soft comput-ing including neural networks, fuzzy systems, evolutionary computing and the fusionof these paradigms, social intelligence, ambient intelligence, computational neuro-science, artificial life, virtual worlds and society, cognitive science and systems,Perception and Vision, DNA and immune based systems, self-organizing andadaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronicsincluding human-machine teaming, knowledge-based paradigms, learning para-digms, machine ethics, intelligent data analysis, knowledge management, intelligentagents, intelligent decision making and support, intelligent network security, trustmanagement, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” areprimarily proceedings of important conferences, symposia and congresses. Theycover significant recent developments in the field, both of a foundational andapplicable character. An important characteristic feature of the series is the shortpublication time and world-wide distribution. This permits a rapid and broaddissemination of research results.

** Indexing: The books of this series are submitted to ISI Proceedings,EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/11156

Cengiz Kahraman • Sezi Cevik Onar •

Basar Oztaysi • Irem Ucal Sari •

Selcuk Cebi • A. Cagri TolgaEditors

Intelligent and FuzzyTechniques: Smartand Innovative SolutionsProceedings of the INFUS 2020 Conference,Istanbul, Turkey, July 21-23, 2020

123

EditorsCengiz KahramanDepartment of Industrial EngineeringIstanbul Technical UniversityIstanbul, Turkey

Basar OztaysiDepartment of Industrial EngineeringIstanbul Technical Universityİstanbul, Turkey

Selcuk CebiIndustrial Engineering DepartmentYildiz Technical UniversityIstanbul, Turkey

Sezi Cevik OnarDepartment of Industrial EngineeringIstanbul Technical UniversityIstanbul, Turkey

Irem Ucal SariDepartment of Industrial EngineeringIstanbul Technical UniversityIstanbul, Turkey

A. Cagri TolgaIndustrial Engineering DepartmentGalatasaray UniversityIstanbul, Turkey

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-030-51155-5 ISBN 978-3-030-51156-2 (eBook)https://doi.org/10.1007/978-3-030-51156-2

© The Editor(s) (if applicable) and The Author(s), under exclusive licenseto Springer Nature Switzerland AG 2021This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whetherthe whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse ofillustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, andtransmission or information storage and retrieval, electronic adaptation, computer software, or by similaror dissimilar methodology 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, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

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

Preface

INFUS is an acronym for Intelligent and Fuzzy Systems. It is a well-establishedinternational research forum to advance the foundations and applications of intel-ligent and fuzzy systems, computational intelligence, and soft computing forapplied research in general and for complex engineering and decision supportsystems.

The principal mission of INFUS is to construct a bridge between fuzzy &intelligence systems and real complex systems via joint research between univer-sities and international research institutions, encouraging interdisciplinary researchand bringing multidiscipline researchers together.

INFUS 2019 was organized in July 21–23, 2019, in Istanbul, Turkey. Thenumber of participants was about 200. The proceedings of INFUS 2020 waspublished by Springer publishing house with an excellent quality before the con-ference began. INFUS 2020 proceedings is again published by Springer under“Advances in Intelligent Systems and Computing Series” and will be indexed inScopus. Its title is Intelligent and Fuzzy Techniques: Smart and InnovativeSolutions. Moreover, special issues of indexed journals will be devoted to a strictlyrefereed selection of extended papers presented at INFUS 2020.

Smart technologies, when integrated with data analytics platforms and networks,offer the potential of expanding the fundamental capacity of any system.Organizations that invest in smart technologies can see rewards including higherrevenues, an improved customer experience, and increased employee satisfaction.Smart Solutions let you cost-effectively achieve and manage your objectives forefficiency, capacity, and availability. Innovative technologies in all business seg-ments provide significant competitive advantages for successful development.Innovative decisions should be based on the clearly formulated innovative strate-gies and fit into business strategies. Innovative decisions start with formation of anupdated information field regarding the innovations being implemented and pos-sible for the implementation in all operating segments of the business. INFUS 2020focuses on smart and innovative technologies and solutions.

v

Our invited speakers this year are Prof. Krassimir Atanassov, Prof. Vicenc Torra,Prof. Janusz Kacprzyk, and Prof. Ahmet Fahri Özok. It is an honor to include themin our conference program. We appreciate their voluntary contributions to INFUS2020, and we hope to see them at INFUS conferences for many years.

In the beginning of the planning process, we had planned to organize INFUS2020 in Izmir at Izmir Katip Celebi University as the host. Unfortunately, coron-avirus pandemic prevented it as all of you know. We hope to organize an interactiveconference in 2021 with your participation in Izmir. Our social program of INFUS2020 in Izmir will be exactly realized at INFUS 2021. We thank all of you verymuch since you did not give up your participation to INFUS 2020. We appreciateyour sincerity and fidelity.

This year, the number of submitted papers became 345. After the review process,about 40% of these papers have been rejected. The distribution of the remainingpapers is as follows from the most to the least: Turkey, Russia, China, Iran, Poland,India, Azerbaijan, Bulgaria, Morocco, Spain, Algeria, Serbia, Ukraine, Pakistan,Canada, South Korea, UK, Indonesia, USA, Vietnam, Finland, Romania, France,Uzbekistan, Italy, and Austria. We again thank all the representatives of theircountries for selecting INFUS 2020 as an international scientific arena.

We also thank the anonymous reviewers for their hard works in selectinghigh-quality papers of INFUS 2020. Each of the organizing committee membersprovided invaluable contributions to INFUS 2020. INFUS conferences would beimpossible without them. We hope meeting you all next year in Turkey.

Cengiz KahramanSelcuk Cebi

Basar OztaysiSezi Cevik OnarIrem Ucal SariA. Cagri Tolga

vi Preface

Contents

Invited Speakers’ Papers

Fuzzy Meets Privacy: A Short Overview . . . . . . . . . . . . . . . . . . . . . . . . 3Vicenç Torra and Guillermo Navarro-Arribas

Intelligent Planning of Spatial Analysis ProcessBased on Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Stanislav Belyakov, Alexander Bozhenyuk, Janusz Kacprzyk,and Igor Rozenberg

The Method of Finding the Base Set of Intuitionistic Fuzzy Graph . . . . 18Alexander Bozhenyuk, Stanislav Belyakov, Janusz Kacprzyk,and Margarita Knyazeva

Intuitionistic Fuzzy Assessments of the Abdominal Aortaand Its Branches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Valetin Vasilev, Evdokia Sotirova, Krassimir Atanassov, and Sotir Sotirov

Mathematical Philosophy and Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . 32Ahmet Fahri Özok

Clustering

Segmentation of Retail Consumers with Soft Clustering Approach . . . . 39Onur Dogan, Abdulkadir Hiziroglu, and Omer Faruk Seymen

Segmentation Analysis of Companies’ Natural Gas Consumptionby Soft Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Onur Dogan

Comparison of Fuzzy C-Means and K-Means ClusteringPerformance: An Application on Household Budget Survey Data . . . . . 54Songul Cinaroglu

vii

A Hybrid Approach for Clustering and Selecting of Cloud ServicesBased on User Preferences Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 63Ouassila Hioual, Ouided Hioual, and Sofiane Mounine Hemam

Basket Patterns in Turkey: A Clustering of FMCG Baskets UsingConsumer Panel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Tolga Kaya, Ahmet Talha Yiğit, and Utku Doğruak

Journey Segmentation of Turkish Tobacco Users Using SequenceClustering Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Ahmet Talha Yiğit, Tolga Kaya, and Utku Doğruak

A Survey on Spherical Fuzzy Sets and Clustering the Literature . . . . . . 87Eren Ozceylan, Baris Ozkan, Mehmet Kabak, and Metin Dagdeviren

Picture Fuzzy Sets and Spherical Fuzzy Sets

Picture Fuzzy Linear Assignment Method and Its Applicationto Selection of Pest House Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Fatma Kutlu Gundogdu

Simple Additive Weighting and Weighted Product Methods UsingPicture Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Fatma Kutlu Gundoğdu and Eda Bolturk

Evaluating Sustainable Vehicle Technologies for FreightTransportation Using Spherical Fuzzy AHP and TOPSIS . . . . . . . . . . . 118Miguel Jaller and Irem Otay

Evaluating Strategic Entry Decisions Using Spherical Fuzzy Sets . . . . . 127Sezi Cevik Onar, Cengiz Kahraman, and Basar Oztaysi

Spherical Fuzzy Cost/Benefit Analysis of Wind Energy Investments . . . 134Sezi Cevik Onar, Basar Oztaysi, and Cengiz Kahraman

A Fuzzy Pricing Model for Mobile Advertisements by Using SphericalFuzzy AHP Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142Basar Oztaysi, Sezi Cevik Onar, and Cengiz Kahraman

A Framework for Selection of the Best Food Waste ManagementAlternative by a Spherical Fuzzy AHP Based Approach . . . . . . . . . . . . 151Aysu Melis Buyuk and Gul Tekin Temur

Using Spherical Fuzzy AHP Based Approach for Prioritizationof Criteria Affecting Sustainable Supplier Selection . . . . . . . . . . . . . . . . 160Yagmur Unal and Gul T. Temur

viii Contents

Estimation and Prediction

Estimating Shopping Center Visitor Numbers Based on VariousEnvironmental Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171Cagatay Ozdemir, Sezi Cevik Onar, and Selami Bagriyanik

Predicting Customers’ Next-to-Be Purchased Products . . . . . . . . . . . . . 180Buse Mert, Defne İdil Eskiocak, and Işıl Öztürk

Estimating Breathing Levels of Asthma Patients with ArtificialIntelligence Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187Asım Sinan Yüksel and Fatma Gülşah Tan

The Problem of Selection with the Fuzzy Axiomatic Design of MEMSBased Sensors in Industry 4.0 Predictive Maintenance Process . . . . . . . 195Arif Sercan Erez, Mehmet Cakmakci, and Rasit Imrenci

Fuzzy Based Noise Removal, Age Group and Gender Predictionwith CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204Ali Tunç, Sakir Taşdemir, and Murat Köklü

Click Prediction in Digital Advertisements: A Fuzzy Approachto Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Ahmet Tezcan Tekin, Tolga Kaya, and Ferhan Çebi

Neural Fuzzy Inference Hybrid System with Support Vector Machinefor Identification of False Singling in Stock Market Predictionfor Profit Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Bhupinder Singh and Santosh Kumar Henge

Estimation of Potential Locations of Trade Objects on the Basisof Fuzzy Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228Alekperov Ramiz Balashirin Oglu and Salahli Vuqar Mamadali Oglu

Surface Roughness Prediction Using ANFIS and Validationwith Advanced Regression Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 238M. A. Vinod Kumar

Predicting Likelihood to Purchase of Users for E-commerce . . . . . . . . . 246Çağlar İçer, Deniz Parmaksız, and Altan Çakır

Hesitant Fuzzy Sets

Webpage Design Based on Generation Differences Using HesitantFuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257M. Çağrı Budak and Sezi Çevik Onar

A Hesitant Fuzzy Linguistic Group Decision Making Modelfor Energy Storage Unit Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265Ahmet Aktas and Mehmet Kabak

Contents ix

Solar Energy Power Plant Investment Selection with UnbalancedHesitant Fuzzy Linguistic MULTIMOORA Method BasedScore-HeDLiSF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274Veysel Çoban and Sezi Çevik Onar

Application of Linear Programming Model in Multiple CriteriaDecision Making Under the Framework of Interval-Valued HesitantFuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282Tabasam Rashid and M. Sarwar Sindhu

Neutrosophic Sets

Evaluation of the Fourth Party Logistics Provider UnderNeutrosophic Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Serhat Aydın, Mehmet Yörükoğlu, and Mehmet Kabak

A* Algorithm Under Single-Valued NeutrosophicFuzzy Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302Esra Çakır and Ziya Ulukan

Bipolar Neutrosophic Fuzzy Dijkstra Algorithm and Its Application . . . 311Esra Çakır and Ziya Ulukan

Physician Selection with a Neutrosophic Multi-criteria DecisionMaking Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Ahmet Sarucan, M. Emin Baysal, and Orhan Engin

Intuitionistic Fuzzy Sets

An Intuitionist Fuzzy Method for Discovering OrganizationalStructures that Support Digital Transformation . . . . . . . . . . . . . . . . . . . 331Zineb Besri and Azedine Boulmakoul

Modeling Humanoid Robots Mimics Using Intuitionistic Fuzzy Sets . . . 339Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, and Irem Otay

Understanding the Blockchain Technology Adoption fromProcurement Professionals’ Perspective - An Analysis of theTechnology Acceptance Model Using Intuitionistic FuzzyCognitive Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Ayça Maden and Emre Alptekin

Distance and Similarity Measures of Generalized Intuitionistic FuzzySoft Set and Its Applications in Decision Support System . . . . . . . . . . . 355Muhammad Jabir Khan and Poom Kumam

Intuitionistic Fuzzy Analysis of Variance of Movie Ticket Sales . . . . . . . 363Velichka Traneva and Stoyan Tranev

x Contents

Interval Valued Intuitionistic Fuzzy Gaussian Membership Function:A Novel Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372Janani Bharatraj

An Integrated IVIF-DEMATEL and IVIF-TOPSIS Methodologyfor Hotel Information System Selection . . . . . . . . . . . . . . . . . . . . . . . . . 381Gizem Erkal, Huseyin Selcuk Kilic, Zeynep Tugce Kalender,Ahmet Selcuk Yalcin, and Gulfem Tuzkaya

Decision Making Using New Distances of Intuitionistic Fuzzy Setsand Study Their Application in the Universities . . . . . . . . . . . . . . . . . . . 390Shuker Mahmood Khalil and Mohanad Abdulkareem Hasan Hasab

Some New Ordered Semi-linear Spaces of Intuitionistic FuzzyProcesses and the Pair of Closely Related States . . . . . . . . . . . . . . . . . . 397Nguyen Dinh Phu, Nguyen Nhut Hung, and Le Thi Ngoc Quynh

Pythagorean Fuzzy Sets and Q-Rung Orthopair Fuzzy Sets

Emergency Decision Making Problem of Power Cut in TurkeyUsing Pythagorean Fuzzy Thermodynamic Approachwith Prospect Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415Veysel Çoban and Sezi Çevik Onar

Multi-criteria Cloud Computing Service Provider SelectionEmploying Pythagorean Fuzzy AHP and VIKOR . . . . . . . . . . . . . . . . . 423Irem Otay and Tuğba Yıldız

Extension of Classical Analytic Hierarchy Process Using q-RungOrthopair Fuzzy Sets and Its Application to Disaster LogisticsLocation Center Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432Seyed Amin Seyfi Shishavan, Yaser Donyatalab,and Elmira Farrokhizadeh

Interval Valued q-Rung Orthopair Fuzzy Prioritized Dual MuirheadMean Operator and Their Application in Group Decision Making . . . . 440Salih Berkan Aydemir and Sevcan Yilmaz Gündüz

Fuzzy Capital Budgeting Using Fermatean Fuzzy Sets . . . . . . . . . . . . . . 448Duygu Sergi and Irem Ucal Sari

Interval Valued q-Rung Orthopair Fuzzy EDAS Methodand Its Application to Supplier Selection . . . . . . . . . . . . . . . . . . . . . . . . 457Elmira Farrokhizadeh, Seyed Amin Seyfi Shishavan, Yaser Donyatalab,and Seyyed Hadi Seifi

Contents xi

Hamacher Aggregation Operators Based on Interval-Valued q-RungOrthopair Fuzzy Sets and Their Applications to DecisionMaking Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466Yaser Donyatalab, Elmira Farrokhizadeh, Seyed Amin Seyfi Shishavan,and Seyyed Hadi Seifi

Similarity Measures of q-Rung Orthopair Fuzzy Sets Based on SquareRoot Cosine Similarity Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Yaser Donyatalab, Elmira Farrokhizadeh,and Seyed Amin Seyfi Shishavan

The Dice (Sorensen) Similarity Measures for Optimal Selectionwith q-Rung Orthopair Fuzzy Information . . . . . . . . . . . . . . . . . . . . . . 484Elmira Farrokhizadeh, Seyed Amin Seyfi Shishavan, Yaser Donyatalab,and Sohrab Abdollahzadeh

Technology Selection of Indoor Location Systems Using IntervalValued Type-2 Intuitionistic Fuzzy WASPAS . . . . . . . . . . . . . . . . . . . . . 494Basar Oztaysi, Sezi Cevik Onar, and Cengiz Kahraman

Evaluating the Influencing Factors on Adoption of Self-drivingVehicles by Using Interval-Valued Pythagorean Fuzzy AHP . . . . . . . . . 503Gozde Bakioglu and Ali Osman Atahan

Evaluation of Maintenance Strategies Using Pythagorean FuzzyTOPSIS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512Tolga Gedikli, Beyzanur Cayir Ervural, and Durmus Tayyar Sen

Multicriteria Decision Making-Applications

Process Robot Automation Selection with MADM in AirlineCargo Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525Gul Durak and A. Cagri Tolga

Smart System Evaluation in Vertical Farming via FuzzyWEDBA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534Murat Basar and A. Cagri Tolga

Blockchain Software Selection for a Maritime Organizationwith MCDM Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543Melis Kaska and A. Cagri Tolga

Prioritization of Influence Factors for Selecting E–Learning Systems . . . 550Ali Karasan and Melike Erdogan

Special Agriculture Production Selection Using IntervalType-2 Fuzzy AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Sarper Alem

xii Contents

Investigating Enablers to Improve Transparency in Sustainable FoodSupply Chain Using F-BWM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567Anish Kumar, Sachin Kumar Mangla, Pradeep Kumar,and Yasanur Kayikci

Resilience Measurement System: A Fuzzy Approach . . . . . . . . . . . . . . . 576Merve Kadan, Gökhan Özkan, and Mehmet Hilmi Özdemir

Road Selection for Autonomous Trucks in Turkey with Fuzzy AHP . . . 582Zeynep Hasgul and Can Aytore

Multi-criteria Oil Station Location Evaluation Using SphericalAHP&WASPAS: A Real-Life Case Study . . . . . . . . . . . . . . . . . . . . . . . 591Irem Otay and Serhat Atik

Managing Cultural Built Heritage in Smart Cities Using Fuzzyand Interval Multi-criteria Decision Making . . . . . . . . . . . . . . . . . . . . . 599Mimica Milošević, Dušan Milošević, and Ana Stanojević

Criteria Weighting for Blockchain Software Selection UsingFuzzy AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608Ferhat Karayazi and Ilke Bereketli

A Proposed Decision Making Methodology to Select IT Suppliersfor Software Development Outsourcing in Banking Sector UsingEDAS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616Murat Levent Demircan and Algı Acarbay

Analysis of Traffic Accidents to Identify Factors Affecting InjurySeverity with Fuzzy and Crisp Techniques . . . . . . . . . . . . . . . . . . . . . . . 625Tutku Tuncalı Yaman, Emrah Bilgiç, and M. Fevzi Esen

Logistics Performance Index (LPI) Analysis with Using AHPand Fuzzy AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634Emir Şahin, İrem Pala, and Berrin Denizhan

Sustainable Transportation Service Provider Evaluation UtilizingFuzzy MCDM Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642Mehtap Dursun and Ergun Ari

Development of Assessment Model for Selection of Sustainable EnergySource in India: Hybrid Fuzzy MCDM Approach . . . . . . . . . . . . . . . . . 649S. K. Saraswat, Abhijeet Digalwar, and S. S. Yadav

Evaluation of Criteria that Affect the Sustainability of Smart SupplyChain in a Textile Firm by Fuzzy SWARA Method . . . . . . . . . . . . . . . . 658Arzu Organ, Kevser Arman, and Ali Katrancı

Green Supplier Selection Using Intuitionistic Fuzzy AHP and TOPSISMethods: A Case Study from the Paper Mills . . . . . . . . . . . . . . . . . . . . 666Ezgi Demir and Gözde Koca

Contents xiii

InterCriteria Analysis Applied to Emerging Europe and Central AsiaUniversity Rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674Veselina Bureva

Determining Significant Factors Affecting Vaccine Demandand Factor Relationships Using Fuzzy DEMATEL Method . . . . . . . . . . 682İkbal Ece Dizbay and Ömer Öztürkoğlu

Benchmarking and Visualization of the Ergonomic Risksin a Manufacturing Plant: Joint Application of Fuzzy AHPand Treemap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690Samira Keivanpour

Multicriteria Decision Making-Theory

An Integrated Fuzzy DEMATEL and Fuzzy Cognitive MappingMethodology for Prioritizing Smart Campus Investments . . . . . . . . . . . 701Ali Karasan and Cengiz Kahraman

Different Approaches to Fuzzy Extension of an MCDA Methodand Their Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709Boris Yatsalo and Alexander Korobov

Multi-objective Task Scheduling in Cloud Computing Environmentby Hybridized Bat Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718Timea Bezdan, Miodrag Zivkovic, Eva Tuba, Ivana Strumberger,Nebojsa Bacanin, and Milan Tuba

Qualitative Factors in the Fuzzy Earned Value Method . . . . . . . . . . . . . 726Dorota Kuchta

The Novel Integrated Model of Plithogenic Sets and MAIRCA Methodfor MCDM Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733Abdullah Özçil, Ayşegül Tuş, Gülin Zeynep Öztaş, Esra Aytaç Adalı,and Tayfun Öztaş

An Alternative Approach for Performance Evaluation:Plithogenic Sets and DEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742Gülin Zeynep Öztaş, Esra Aytaç Adalı, Ayşegül Tuş, Tayfun Öztaş,and Abdullah Özçil

Multi-criteria Decision Making Problem with Intuitionistic FuzzySets: A Novel Ranking Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 750Hatice Ercan-Tekşen

Preference-Oriented Fuzzy TOPSIS Method . . . . . . . . . . . . . . . . . . . . . 758Alicja Mieszkowicz-Rolka and Leszek Rolka

xiv Contents

Intelligent Statistical Analyses

A New Approach to Analyze Perceived Design Quality of MobilePhone Using Fuzzy Hierarchical Conjoint Analysis . . . . . . . . . . . . . . . . 769Yusra Erdem, Selcuk Cebi, and Esra Ilbahar

Congestion Trajectories Using Fuzzy Gaussian Travel Time Basedon Mesoscopic and Cellular Automata Traffic Model . . . . . . . . . . . . . . . 779A. Boulmakoul, L. Karim, M. Nahri, and A. Lbath

Data Mining Algorithms for Classification Modelof Engineering Grade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 788Ching-Lung Fan

A Fuzzy Strategy Analysis Simulator for Exploring the Potentialof Industry 4.0 in End of Life Aircraft Recycling . . . . . . . . . . . . . . . . . . 797Samira Keivanpour

Knowledge Data Discovery (Frequent Pattern Growth):The Association Rules for Evergreen Activitieson Computer Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807Fauzan Asrin, Saide Saide, Silvia Ratna, and Alex Wenda

Enabling Big Data Analytics at Manufacturing Fieldsof Farplas Automotive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817Özgün Akın, Halil Faruk Deniz, Doğukan Nefis, Alp Kızıltan,and Altan Çakır

Big Data Analytics Framework for Smart City Real-Time FuzzyComplex Event Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825Basmi Wadii, Azedine Boulmakoul, Lamia Karim, and Ahmed Lbath

Cloud Based Big Data DNS Analytics at Turknet . . . . . . . . . . . . . . . . . 833Altan Çakır, Yousef Alkhanafseh, Esra Karabıyık, Erhan Kurubaş,Rabia Burcu Bunyak, and Cenk Anıl Bahçevan

A Combined Bioinspired Algorithm for Big Data Processing . . . . . . . . . 842Elmar Kuliev, Dmitry Zaporozhets, Yury Kravchenko, and Ilona Kursitys

Exploration of the Waves of Feminism Using Sentiment BasedText Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 850H. Umutcan Ay, S. Nazlı Günesen, and Tolga Kaya

SVM-Based Hybrid Robust PIO Fault Diagnosis for Bearing . . . . . . . . 858Farzin Piltan and Jong-Myon Kim

Modeling Urban Traffic by Means of Traffic Density Data:Istanbul Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867Tutku Tuncalı Yaman, Hülya Başeğmez Sezer, and Emrah Sezer

Contents xv

Realising Newspaper Sales by Using Statistic Methods . . . . . . . . . . . . . 875Onur Dogan and Omer Faruk Gurcan

Extensions of Fuzzy Sets in Big Data Applications:A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 884Nurşah Alkan and Cengiz Kahraman

A Big Data Semantic Driven Context AwareRecommendation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894Manuel J. Barranco, Pedro J. Sanchez, Jorge Castro, and Raciel Yera

Blended Environment of Naive Bayes and Support Vector Machine(SVM) for Designing Simulation Based E-LearningRespiratory System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903Anuradha Verma Babbar and Santosh Kumar Henge

InterCriteria Analysis of Public Health Data in Bulgaria . . . . . . . . . . . . 910Evdokia Sotirova, Valentin Vasilev, Sotir Sotirov, and Hristo Bozov

A Case Study on Vehicle Battery Manufacturing Using Fuzzy Analysisof Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916Abbas Parchami, Mashaallah Mashinchi, and Cengiz Kahraman

New Suggestion for Fuzzy Random Variableand Its Statistical Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924Abbas Parchami

Networks

Fuzzy Centrality Analysis for Smart City Trajectories . . . . . . . . . . . . . . 933Lamia Karim, Azedine Boulmakoul, Ghyzlane Cherradi, and Ahmed Lbath

Improving Customer Experience for an Internet Service Provider:A Neural Networks Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941Özge H. Namlı, Seda Yanık, Faranak Nouri, N. Serap Şengör,Yusuf Mertkan Koyuncu, and İrem Küçükali

Segmentation of Larynx Histopathology Images via ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 949Ahmet Haşim Yurttakal and Hasan Erbay

Glioma Brain Tumor Grade Classification from MRI UsingConvolutional Neural Networks Designed by Modified FA . . . . . . . . . . 955Timea Bezdan, Miodrag Zivkovic, Eva Tuba, Ivana Strumberger,Nebojsa Bacanin, and Milan Tuba

Dynamic System Control Using Z-number Based FuzzyNeural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964Rahib H. Abiyev

xvi Contents

Comprehensive Research of the Commodity Market: Theoretical,Methodological and Modern Approaches Using Neural Networkswith Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973Mikayilova Rena Nuru Kizi

The Maximum Lexicographic Contraflow Finding in a FuzzyDynamic Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981Evgeniya Gerasimenko and Vladimir Kureichik

An Enhanced Particle Swarm Optimization with Levy Flightfor RBF Neural Network in Typical Karst Area, South China . . . . . . . 990Zhangjun Cao, Dong Wang, and Lachun Wang

AMethod for Forecasting the Demand for Pharmaceutical Products ina Distributed Pharmacy Network Based on an Integrated ApproachUsing Fuzzy Logic and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 998Alekperov Ramiz Balashirin Oglu and Isgandarova Ilhama Tarlan Kizi

Marketing Strategy Selection Using Fuzzy Analytic Network Process . . . 1008Serhan Hamal, Bahar Sennaroglu, and Mahmure Övül Arıoğlu

A Deep Learning Model for Skin Lesion Analysis Using GaussianAdversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015Fatih Ergin and Ismail Burak Parlak

Learning Channel-Wise Ordered Aggregationsin Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023Iris Dominguez-Catena, Daniel Paternain, and Mikel Galar

Design of Nano-scale Synaptic Neural Network Using RRAM . . . . . . . . 1031Ali Mohamed and Osama Rayis

Neural Network-Based Control Framework for SISO UncertainSystem: Passive Fault Tolerant Approach . . . . . . . . . . . . . . . . . . . . . . . 1039Sejal Raval, Himanshukumar R. Patel, and Vipul A. Shah

Forecasting the Day-Ahead Prices in Electricity Spot Marketof Turkey by Using Artificial Neural Networks . . . . . . . . . . . . . . . . . . . 1048Berna Tektaş, Aygülen Kayahan Karakul, and Rozi Mizrahi

Intelligent Learning

Using Formal Concept Analysis Tools in RoadEnvironment-Type Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059A. Boulmakoul, Z. Fazekas, L. Karim, G. Cherradi, and P. Gáspár

An Intelligent Overtaking Assistant Systemfor Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1068Ersin Armağan and Tufan Kumbasar

Contents xvii

Predicting Movie Ratings with Machine Learning Algorithms . . . . . . . . 1077Sandy Çağlıyor and Başar Öztayşi

Trajectory Tracking of a Quadcopter Using Adaptive Neuro-FuzzyController with Sliding Mode Learning Algorithm . . . . . . . . . . . . . . . . 1084Hasan Kemik, Mehmet Berkin Dal, and Yesim Oniz

Influencer Identification System Design Using MachineLearning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1092Elvira Israfilova, Armagan Arslan, Nihan Yildirim, and Tolga Kaya

Efficient and Real-Time Face Recognition Based on Single ShotMultibox Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1100Youngshin Ahn, Sumi Kim, Fei Chen, and Jaeho Choi

Machine Learning-Based Robust Feedback Observer for FaultDiagnosis in Bearing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107Farzin Piltan and Jong-Myon Kim

Malfunction Detection on Production Line Using Machine Learning:Case Study in Wood Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116Kağan Özgün, Sami Can Aklan, Ahmet Tezcan Tekin, and Ferhan Çebi

Classification of Breast DCE-MRI Images via Boostingand Deep Learning Based Stacking Ensemble Approach . . . . . . . . . . . . 1125Ahmet Haşim Yurttakal, Hasan Erbay, Türkan İkizceli, Seyhan Karaçavuş,and Cenker Biçer

Machine Learning Approach for Automatic Categorizationof Service Support Requests on University InformationManagement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1133Aytuğ Onan, Erdem Atik, and Adnan Yalçın

Comparative Study of Different Machine Learning Models for RemoteSensing Bathymetry Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1140Shen Wei, Ji Qian, Rao Yali, and Meng Ran

Container Terminal Workload Modeling Using MachineLearning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149Üstün Atak, Tolga Kaya, and Yasin Arslanoğlu

Usage of Machine Learning Algorithms for Flow Based AnomalyDetection System in Software Defined Networks . . . . . . . . . . . . . . . . . . 1156Muhammet Fatih Akbaş, Cengiz Güngör, and Enis Karaarslan

Speech Analysis with Deep Learning to Determine SpeechTherapy for Learning Difficulties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1164Nogol Memari, Saranaz Abdollahi, Sonia Khodabakhsh, Saeideh Rezaei,and Mehrdad Moghbel

xviii Contents

A Compromise-Based New Approach to Learning FuzzyCognitive Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1172Miraç Murat and Umut Asan

Fuzzy Analytics

Fuzzy Analytics of Spaces’ Dynamic Use in Smart Buildings . . . . . . . . . 1183Azedine Boulmakoul, Abdellah Daissaoui, Ahmed Lbath,and Lamia Karim

Object Monitoring Under Z-Information Based on Rating Points . . . . . 1191Olga M. Poleshchuk

An Intelligent Decision Support System: Application of Fuzzy Toolsand System Dynamics Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1199Merve Kadan, Gökhan Özkan, and Mehmet Hilmi Özdemir

Fuzzy Uncertainty Modelling in Cost and Cash Flow Forecastingin Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1206Dorota Kuchta and Adam Zabor

Fuzzy System for Building Energy Flows Management . . . . . . . . . . . . . 1216Penka V. Georgieva

Fuzzy RFM Analysis: An Application in E-Commerce . . . . . . . . . . . . . 1225Basar Oztaysi and Mert Kavi

A New Structure of Nullnorms on Bounded Lattices . . . . . . . . . . . . . . . 1233Gül Deniz Çaylı

A New Perspective to Design Phase of Water Supply Systemsfrom Aspect of Water Demand Using Fuzzy Automation . . . . . . . . . . . . 1242Halid Akdemir, Ayşegül Alaybeyoğlu, and Ali Danandeh Mehr

Fuzzy Cognitive Map Based PI Controller Design . . . . . . . . . . . . . . . . . 1250Aykut Denizci, Sinan Karadeniz, and Cenk Ulu

On 2-Fuzzy Metric Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1258Elif Güner and Halis Aygün

Some Properties of Partial Fuzzy Metric Topology . . . . . . . . . . . . . . . . 1267Ebru Aydogdu, Başak Aldemir, Elif Güner, and Halis Aygün

Fuzzy Classifier Based Colorimetric QuantificationUsing a Smartphone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276Öykü Berfin Mercan and Volkan Kılıç

LabHub: A New Generation Architecture Proposalfor Intelligent Healthcare Medical Laboratories . . . . . . . . . . . . . . . . . . . 1284Bengi Tugcu Idemen, Emine Sezer, and Murat Osman Unalir

Contents xix

Center-of-Gravity Real Options Method Based on Interval-ValuedFuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1292Jani Kinnunen, Irina Georgescu, and Mikael Collan

Fusing Fuzzy Preferences in Contexts of Social Choice:Towards a Topological Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1301María Jesús Campión, Esteban Induráin, and Armajac Raventós-Pujol

Understanding the Behavior of Zadeh’s Extension Principlefor One-to-One Functions by R Programming Language . . . . . . . . . . . . 1309Abbas Parchami and Parisa Khalilpoor

Intuitionistic Fuzzy Z-numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316Irem Ucal Sari and Cengiz Kahraman

Risk Assessment

Risk Assessment of R&D Activities Using Intuitionistic Fuzzy AHPand FIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1327Esra Ilbahar, Selcuk Cebi, and Cengiz Kahraman

Risk Matrix Technique Based Fuzzy MCDM Risk Assessment Methodand Application in Glass Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1336Sukran Seker

A New Risk Assessment Approach for Occupational Healthand Safety Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1345Selcuk Cebi

Intuitionistic Fuzzy Group Decision Making on Risk Assessmentand Control Efficiency of Accounting Information Systemswith TOPSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1355Aygülen Kayahan Karakul, Rozi Mizrahi, and Berna Tektaş

Evaluation of Occupational Safety Risk in Underground MiningUsing Fuzzy Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1363Fatma Yaşlı and Bersam Bolat

Risk Analysis for Digitalization Oriented Sustainable Supply ChainUsing Interval-Valued Pythagorean Fuzzy AHP . . . . . . . . . . . . . . . . . . . 1373Nurşah Alkan

Intelligent Quality

Design for Six Sigma and Process Capability Using PenthagoreanFuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1385Elif Haktanır and Cengiz Kahraman

xx Contents

Defects Control Charts Using Interval-Valued PenthagoreanFuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1396Elif Haktanır and Cengiz Kahraman

Fuzzy AHP Based Prioritization and Taxonomy of InformationQuality Factors in Accounting Information Systems . . . . . . . . . . . . . . . 1407Rozi Mizrahi, Berna Tektaş Aygülen, and Kayahan Karakul

Analytical Techniques to Compute Cp and Cpm CapabilityIndices by R Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415Abbas Parchami

Heuristics

Proposal of Genetic Algorithm Approach for Solving Single MachineScheduling Problem Under Learning Effect . . . . . . . . . . . . . . . . . . . . . . 1423Derya Deliktas and Mustafa Urhan

Hybrid Krill Herd Algorithm with Particle Swarm Optimizationfor Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1431Elnaz Pashaei, Elham Pashaei, and Nizamettin Aydin

Distributed Fuzzy Permutation Flow Shop Scheduling Problem:A Bee Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1440M. Emin Baysal, Ahmet Sarucan, Kadir Büyüközkan, and Orhan Engin

Fuzzy Metaheuristics: A State-of-the-Art Review . . . . . . . . . . . . . . . . . . 1447Nurşah Alkan and Cengiz Kahraman

Fuzzy and Evolutionary Algorithms for Transport LogisticsUnder Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1456Yuriy Kondratenko, Galyna Kondratenko, Ievgen Sidenko,and Mykyta Taranov

Using GA for Locating Post-disaster Field Hospitals:The Case of Istanbul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1464Yesim Komurcu and Seda Yanik

Optimization

Optimization of Spatial-Time Planning Resource AllocationUnder Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1475Olesiya Kosenko, Alexander Bozhenyuk, Stanislav Belyakov,and Margarita Knyazeva

Solving Fuzzy Multi-objective Linear Programming ProblemsUsing Multi-player Zero-Sum Game . . . . . . . . . . . . . . . . . . . . . . . . . . . 1483Gizem Temelcan, Inci Albayrak, Hale Kocken, and Mustafa Sivri

Contents xxi

A Bioinspired Algorithm for Improving the Effectivenessof Knowledge Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1491Ilona Kursitys, Yury Kravchenko, Elmar Kuliev, and Alexandr Natskevich

Spatial Coherence Radius of Plane Wave Propagating in AnisotropicMaritime Atmospheric Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1499Bing Guan, Fei Chen, and Jaeho Choi

A Comparative Performance Analysis of Consensus ModelsBased on a Minimum Cost Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1506Á. Labella, R. M. Rodríguez, and L. Martínez

Pedestrian Detection Based on Improved Mask R-CNN Algorithm . . . . 1515Wenjun Yu, Sumi Kim, Fei Chen, and Jaeho Choi

A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithmfor Probabilistic Demand and Customer Positions . . . . . . . . . . . . . . . . . 1523Engin Bayturk, Sakir Esnaf, and Tarik Kucukdeniz

Mobile Application Based Automatic Caption Generationfor Visually Impaired . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1532Özkan Çaylı, Burak Makav, Volkan Kılıç, and Aytuğ Onan

Chaos Control of BLDC Motor via Fuzzy Based PID Controller . . . . . . 1540Alkım Gökçen, Mehmet Uğur Soydemir, and Savaş Şahin

Sustainability Oriented Scheduling Procedure for Public Projects . . . . . 1548Dorota Kuchta and Ewa Marchwicka

Optimization of an Oil Refinery Valuation System Throughthe Intuitionistic Fuzzy InterCriteria Analysis . . . . . . . . . . . . . . . . . . . . 1555Velichka Traneva and Stoyan Tranev

Double Edge–Vertex Domination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1564Bünyamin Şahin and Abdulgani Şahin

Order Acceptance Scheduling on a Single Machinewith Energy Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1573Mariam Bouzid, Oussama Masmoudi, and Alice Yalaoui

The Analysis of Human Oriented System of Weighted Fuzzy PetriNets for the Passenger Transport Logistics Problem . . . . . . . . . . . . . . . 1581Zbigniew Suraj, Oksana Olar, and Yurii Bloshko

Concepts and Methods of “Digital Twins” Models Creationin Industrial Asset Performance Management Systems . . . . . . . . . . . . . 1589Nodirbek Yusupbekov, Fakhritdin Abdurasulov, Farukh Adilov,and Arsen Ivanyan

xxii Contents

Finding the Optimal Features Reduct, a Hybrid Model of Rough Setand Polar Bear Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1596Amer Mirkhan and Numan Çelebi

Design and Manufacture a Vehicle Auxiliary System ModelControlled by Using Smartphone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1604Nguyen Phu Thuong Luu

A Study on Vehicle Air Condition System Model Controlledby Using Smartphone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1612Nguyen Phu Thuong Luu

Multi-shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty . . . 1620F. Nucci

On Merrifield-Simmons Index of Trees . . . . . . . . . . . . . . . . . . . . . . . . . 1628Bünyamin Şahin

A Fuzzy Goal Programming Model to Evaluate GreenEnergy Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1635Beyzanur Cayir Ervural

Application of Fuzzy Logic Model for Correct Lighting in ComputerAided Interior Design Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1644Rahib Imamguluyev

Review and Discussion Papers

Extensions of Ordinary Fuzzy Sets: A ComparativeLiterature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1655Cengiz Kahraman, Basar Oztaysi, Irem Otay, and Sezi Cevik Onar

Systematic Literature Review for Work Sampling with IOTTechnology in Retail Store Operations . . . . . . . . . . . . . . . . . . . . . . . . . . 1666Gizem Omeroglu, Abdullah Ertug, and Aysegul Ozkavukcu

Engineering Economics Using Fuzzy Sets: A Literature Review . . . . . . 1675Eda Boltürk

Scientific Cooperation in the Field of Economics in SelectedEuropean Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1683Paweł Lula, Urszula Cieraszewska, and Monika Hamerska

Sentiment Analysis on Students’ Evaluation of HigherEducational Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1693Mansur Alp Toçoğlu and Aytuğ Onan

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1701

Contents xxiii