Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2025Scopus SJRQ4Deep learning-based hybrid traffic flow prediction model for freight transport
Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit
Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.
- MakaleYalnız künye2025Scopus SJRQ1AI-enabled human capital management (HCM) software adoption using full consistency method (FUCOM): evidence from banking industry
Yadav, Rama Shankar; Kaya, Sema Kayapinar; Pant, Abhay; Tiwari, Anurag
Purpose: Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector. Design/methodology/approach: Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs. Findings: The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks. Originality/value: The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.
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İncele - MakaleAçık Erişim2023Scopus SJRQ1An Integrated Approach for Assessing Suppliers Considering Economic Sustainability Innovation
Ahmadi, Hadi Badri; Pamucar, Dragan; Pourhejazy, Pourya; Kaya, Sema Kayapinar; Liou, James J. H.
Innovation plays a key role for improving the sustainability performance of corporations. A truly sustainable innovative firm needs to take the social, environmental, and economic aspects of sustainable innovation into consideration. However, limited studies have investigated the economic aspect of sustainable innovation, especially in the context of supplier evaluation from emerging economies. To deal with this issue, this manuscript puts forward a criteria decision framework for evaluating economic sustainable innovative suppliers. A new methodology based on fuzzy Full Consistency Method (FFUCOM) and Improved Combinative Distance-based Assessment (ICODAS) is developed and extended using interval rough Dombi-Bonferroni operators. In the developed approach, the FFUCOM is employed to determine the criteria weights, and the ICODAS is responsible for assessing and ranking the suppliers. A case study from the manufacturing sector of an emerging economy is considered for validating the developed framework and decision model. The findings introduce the 'financial resumption of products' as the most critical economic innovation criterion for evaluating suppliers. This article significantly helps decision-makers and practitioners to better comprehend the underpinnings of sustainable innovation economic dimension, particularly when assessing their suppliers. Furthermore, the applicability and validity of the proposed model was confirmed by the results of sensitivity analysis. Findings demonstrated that the proposed method is a solid and useful MCDM framework, and its outputs are precise and acceptable.
AtıfScopus9OpenAlex6İncele - MakaleYalnız künye2023Scopus SJRQ3Evaluation of social factors within the circular economy concept for European countries
Kaya, Sema Kayapinar; Ayçin, Ejder; Pamucar, Dragan
The circular economy (CE) is a rapidly growing theme, particularly in the European Union (EU), that encourages the responsible and circular use of resources in the field of contributing to long-term development. The environmental and economic magnitude of development are frequently debated in the subject of CE whereas, social aspects have been only rarely and sporadically combined into the CE. Therefore, this study involves a multidisciplinary holistic framework of clustering methods and Multi-Criteria Decision Making (MCDM) for evaluating the CE paradigm in relation to EU countries' social growth. In terms of the social impact of CE strategies, the k-means cluster analysis was used to group the 27 EU members with similar social impact levels. Subsequently, a novel integrated CRITIC (The criteria importance through intercriteria correlation) and MEREC (Method based on the Removal Effects of Criteria) methods were employed for determining the weights of social indicators in order to balance the two weighting methods. In order to select the best country in each cluster, a novel extension of the hybrid MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method combined with Power averaging and Heronian operator was developed. According to final solutions, Western European countries in the first cluster have the lowest unemployment and corruption rates, with the Netherlands having the best performance in this cluster. The second cluster includes countries with the lowest employment rates following university graduates between the ages of 20 and 64. Accordingly, Croatia is the best social performance in this cluster. Countries with the highest income distribution and unemployment rate are in the 3rd cluster, the best country in this group is Lithuania. Finally, the results obtained from the innovative MCDM methods have validated in order to demonstrate the proposed methodology's applicability.
AtıfScopus30WoS23OpenAlex42İncele - MakaleYalnız künye2023Scopus SJRQ1A novel two-phase group decision-making model for circular supplier selection under picture fuzzy environment
Kaya, Sema Kayapinar
Circular supply chain management (CSCM), which incorporates circular thinking into supply chain management, promotes supply chain sustainability by offering a novel and compelling viewpoint. In the CSCM, supplier selection is crucial in establishing a competitive edge among businesses by decreasing environmental degradation and related supply chain expenses. This paper aims to propose a novel two-phase group decision-making approach, which combines the picture fuzzy Analytical Hierarchical Process (PF-AHP) and grey Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS-G) model to select railway material suppliers within the scope of circular economy perspective. In group decision-making, a picture fuzzy number-based approach aggregates individual decision makers’(DMs) opinions and provides a lower level of computing complexity and a higher level of performance. The PF-AHP has been used to produce the weights of the criteria, and the MARCOS-G technique has been used to rank the suppliers and choose the most appropriate one. The results and verification of the novel method are carried out throughout a comprehensive sensitivity analysis. For this purpose, 50 scenarios with changes in the weight values of criteria were developed. In the validation analysis, a comparison with other grey COPRAS, TOPSIS, ARAS and WASPAS methods were performed. Due to the ranking results obtained from Spearman’s correlation analyses, the MARCOS-G is almost consistent and it can be seen that the A4 remains the top supplier in all 50 scenarios.
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İncele - MakaleYalnız künye2023Scopus SJRQ1Evaluation of container port sustainability using WASPAS technique using on type-2 neutrosophic fuzzy numbers
Kaya, Sema Kayapinar; Kundu, Pradip; Görçün, Ömer Faruk
According to the common opinion in the literature, the sustainability of container ports is a tremendously complex topic owing to the maritime sector's excessively dynamic form and many highly complex, predictable and unpredictable uncertainties in this industry. The current paper proposes two powerful, practical, inspiring approaches to fill these gaps. It proposes a novel type-2 neutrosophic fuzzy numbers (T2NFNs) based Delphi method to determine the criteria logically and optimal and extends the WASPAS technique based on the T2NFNs for evaluating the alternatives. The current paper presents practical managerial implications that many stakeholders can consider, such as port authorities, ship owners, logistics service providers, governments, and local authorities, when making strategic and managerial decisions. In addition, the results of a comprehensive sensitivity analysis performed to test the robustness and applicability of the model approve the validity of the proposed T2NFN-based integrated approach.
AtıfScopus20WoS16OpenAlex24İncele - MakaleYalnız künye2023Scopus SJRQ2New Deep Learning-Based Passenger Flow Prediction Model
Utku, Anıl; Kaya, Sema Kayapinar
The ability to predict passenger flow in transport networks is an important aspect of public transport management. It helps improve transport services, aids those responsible for management to obtain early warning signals of emergencies and unusual circumstances and, in general, makes cities smarter and safer. This paper develops a long short-term memory-based (LTSM-based) deep learning model to predict short-term transit passenger volume on transport routes in Istanbul. This prediction model has been created using a dataset that included the number of people who used different transit routes in Istanbul at one-hour intervals between January and December 2020. The proposed multilayer LSTM-based deep learning model has been compared with popular models such as random forest (RF), support vector machines, autoregressive integrated moving average, multilayer perceptron, and convolutional neural network. The experimental findings showed that the proposed multilayer LSTM-based deep learning model outperformed the other models with regard to prediction for each transfer route. Furthermore, RF, one of the machine learning models used, produced remarkably successful results.
AtıfScopus19WoS12OpenAlex16İncele - MakaleAçık Erişim2022Scopus SJRQ2A New Hybrid Fuzzy Multi-Criteria Decision Methodology for Prioritizing the Antivirus Mask over COVID-19 Pandemic
Kaya, Sema Kayapinar; Pamucar, Dragan; Aycin, Ejder
During the COVID-19 pandemic, masks have become essential items for all people to protect themselves from the virus. Because of considering multiple factors when selecting an antivirus mask, the decision-making process has become more complicated. This paper proposes an integrated approach that uses F-BWM-RAFSI methods for antivirus mask selection process with respect to the COVID-19 pandemic. Finally, sensitivity analysis was demonstrated by evaluating the effects of changing the weight coefficients of the criterion on the ranking results, simulating changes in Heronian operator parameters, and comparing the obtained solution to other MCDM approaches to ensure its robustness.
AtıfScopus21WoS15OpenAlex23İncele - MakaleYalnız künye2022Scopus SJRQ2Interval basic uncertain information and related aggregations in decision making
Jin, Lesheng; Mesiar, Radko; Yager, Ronald; Kaya, Sema Kayapinar
The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is presented.
AtıfScopus18OpenAlex17İncele - MakaleAçık Erişim2022Scopus SJRQ1DEEP LEARNING BASED A COMPREHENSIVE ANALYSIS FOR WASTE PREDICTION
Utku, Anıl; Kaya, Sema Kayapinar
In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.
AtıfScopus13OpenAlex13İncele - MakaleYalnız künye2020Derin sinir ağları kullanarak türkiye’deki otomobil satışlarının tahmini için bir model
Kaya, Sema Kayapinar; Yıldırım, Özal
Tüm kararlarda itici güç olan tedarik zinciri sürecinde talep tahmini, tedarik zinciri sürecinin en önemli bileşenlerinden birisidir. Gelecekteki ürün ve hizmetlerin tahmin edilmesi, diğer tüm tahminlerin başlangıç noktası olup diğer tüm süreçlerin temel girdisini oluşturmaktadır. Bu çalışmada, otomobil satış tahmini için 8 katmanlı Derin Sinir Ağ (DSA) modeli önerilmiştir. Modelin girdileri, döviz kuru, gayrisafi yurt içi hasıla, tüketici güven endeksi ve tüketici fiyat endeksi gibi çeşitli ekonomik göstergelerden oluşmaktadır. Modelin çıkışına göre araç satış tahmini yapılmıştır. 2011 ve 2018 yılları arasında aylık bazda toplam 90 veri toplanılarak analizler yapılmıştır. Elde edilen sonuçlar, bu yaklaşımın çeşitli satış tahmin problemlerinde kullanılabileceğini göstermektedir.
AtıfTR Dizin5
İncele - MakaleAçık Erişim2020Determination of Microbiological Quality of Fermented Sausage Samples by Fuzzy Logic Approach
Ertekin, Özlem; Kaya, Sema Kayapinar
Mathematical methods may be useful in cases with certainty by using numerical data, but may be insufficient in cases of uncertainty. One of the uncertainties is the quality assessment in the food industry. In this case, one of the methods that can be used is fuzzy logic method. In this study, a fuzzy logic based decision support system was designed for the classification of sausage samples into quality classes. The inputs of the system are Micrococcus/Staphylococcus, Lactobacillus, Yeast-mold count values of fermented sausage samples. The output of the fuzzy system was sausage quality assessment. In order to determine the success of the experimental study, a comparison was made with expert decisions and the system was found to be successful at 85%. The system was modeled using Matlab R2016a package program.
- MakaleYalnız künye2020Scopus SJRQ1A scale to measure sustainable campus services in higher education: “Sustainable Service Quality”
Ozdemir, Yasal; Kaya, Sema Kayapinar; Turhan, Erkan
This study aims to develop a measuring tool which can address two independent research areas in the literature: Sustainability in higher education and service quality in higher education. Both subjects are studied independently in the literature, showing a need for an integrated approach that bridges these areas. For achieving this purpose, a new scale was developed based on a questionnaire applied to two universities in Turkey. The scale aimed to measure the perceptions of the students regarding the quality of sustainable campus services. The questionnaire consisted of 22 questions on several aspects of campus services which are related to sustainability. A total of 234 samples were used for Exploratory Factor Analysis for determining the underlying structures in the data set. The scale was validated with satisfactory Kaiser-Meyer Olkin and Bartlett's test results. Five dimensions were extracted, which represent sustainable service quality. After validation, the scale was applied to two universities located in geographically and socio-economically different regions of Turkey. The term chosen for the proposed combined scale is Sustainable Service Quality (SusServQual). With additional data, t-tests were applied to 308 students aiming to compare the two universities based on perceived quality scores. The results show that the university located in the socio-economically more developed region performed better regarding the overall satisfaction score and four of the five dimensions. The scores were also weighted utilizing a modified method of Analytical Hierarchy Process. Instead of crisp numbers which forces the decision maker to think in mathematical terms while comparing criteria, linguistic terms, each of which corresponds to a fuzzy number were used. Thus, a more accurate evaluation was obtained where the decision maker's subjectivity and ambiguity of the situation are important.
AtıfScopus62WoS50OpenAlex77İncele - BildiriYalnız künye2020Design factors for sustainable university campus via interval type 2 fuzzy set
Kaya, Sema Kayapinar; Erginel, Nihal; Kahraman, C.; Cevik Onar, S.; Oztaysi, B.; Sari, I.U.; Cebi, S.; Tolga, A.C.
Sustainable development has gained importance in recent years as a development model that aims to create environmental awareness for future generations within the economic and social development goals of the countries. Sustainable development is concerned with the effective and efficient use of rapidly declining natural resources for the growing population. The achievement of the goals determined in sustainable development and the transfer to the next generations is realized through sustainable education. Due to the innovative and pioneering role of universities in society, they are the most important educational institutions that contribute to the spread of a sustainable lifestyle in society. In this study, Interval Type-2 Fuzzy Quality Function Deployment (IT2_FQFD) approach was developed to improve sustainable university attributes. Firstly, the most important 12 sustainability criteria (SRs) were weighted with Interval Type-2 Fuzzy Analytic Hierarchy Process (IT2_FAHP). Then QFD approach was used to determine the importance of sustainability factor of university campus, the relations between SRs and SDs, and the correlations matrix among SDs via interval type-2 fuzzy sets. After that, the design attributes were ranked with fuzzy trade-off method. The fuzzy trade-off is a novel ranking method covering all of the solution space in order to find an optimal solution in decision-making problems with conflict criteria. This paper is the first study that presents a new method of combined IT2_FQFD and IT2_FAHP, which evaluates the sustainable university model considering the needs of student and academicians. As a result of this study, the most appropriate SDs were determined and ranked according to their importance level.
AtıfScopus2OpenAlex2İncele - MakaleAçık Erişim2020Evaluation of the effect of COVID-19 on countries' sustainable development level: A comparative MCDM framework
Kaya, Sema Kayapinar
The extent of the outbreak of coronavirus disease (COVID-19) had a major impact on health, social life, economic and environmental activities in almost every country over the world. It has disrupted the sustainable development of countries and brought many uncertainties for their future capabilities. In this study, the effects of the COVID-19 on OECD countries' sustainable development were investigated, and the sustainable development performance of the countries was evaluated by the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method. Data for the second quarter of 2020 and the same quarter of the previous year is considered. Then, the results obtained by the MAIRCA method were compared with two different multi-criteria decision-making (MCDM) methods called MABAC (Multi‐Attributive Border Approximation Area Comparison) and WASPAS (Weighted Aggregated Sum Product Assessment). The effectiveness and validity of the results obtained from these methods were tested with Spearman's correlation coefficient. Finally, to examine the effect of COVID-19 on the indicators of sustainable development, a non-parametric Wilcoxon signed-rank test was applied. As a result, it was concluded that COVID-19 negatively affected the sustainable development of countries. However, sustainable development performances of developed countries have been observed to be better than developing countries.
AtıfScopus49OpenAlex56İncele - BildiriYalnız künye2020Scopus SJRQ3Dynamic Features Spaces and Machine Learning: Open Problems and Synthetic Data Sets
Kaya, Sema Kayapinar; Navarro-Arribas, Guillermo; Torra, Vicenç; Huynh, V.; Entani, T.; Jeenanunta, C.; Inuiguchi, M.; Yenradee, P.
Dynamic feature spaces appear when different records or instances in databases are defined in terms of different features. This is in contrast with usual (static) feature spaces in standard databases, where the schema of the database is known and fixed. Then, all records in the database have the same set of variables, attributes or features. Dynamic feature mining algorithms are to extract knowledge from data on dynamic feature spaces. As an example, spam detection methods have been developed from a dynamic feature space perspective. Words are taken as features and new words appearing in new emails are, therefore, considered new features. In this case, the problem of spam detection is represented as a classification problem (a supervised machine learning problem). The relevance of dynamic feature spaces is increasing. The large amounts of data currently available or received by systems are not necessarily described using the same feature spaces. This is the case of distributed databases with data about customers, providers, etc. Industry 4.0, Internet of Things, and RFIDs are and will be a source of data in dynamic feature spaces. New sensors added in an industrial environment, new devices connected into a smart home, new types of analysis and new types of sensors in healthcare, all are examples of dynamic feature spaces. Machine learning algorithms are needed to deal with these type of scenarios. In this paper we motivate the interest for dynamic feature mining, we give some examples of scenarios where these techniques are needed, we review some of the existing solutions and its relationship with other areas of machine learning and data mining (e.g., incremental learning, concept drift, topic modeling), we discuss some open problems, and we discuss synthetic data generation for this type of problem.
AtıfScopus4WoS3OpenAlex3İncele - Kitap BölümüYalnız künye2020Industrial internet of things: How industrial internet of things impacts the supply chain
Kaya, Sema Kayapinar
Industrial Internet of Things (IIoT) refers to the extension of the Internet of Things and It is used for industrial purposes such as manufacturing, supply chain. IIoT can be connected with billions of industrial devices and machines that supported machine learning and big data technology. Because of its potential to authorize faster and better decision making, the IIoT becomes essential for supply chain processes. The IIoT is set to revolutionize the supply chain with both operational efficiencies and revenue opportunities made possible with just this type of transparency. This study attempts to fill this gap by developing a conceptual framework for predicting the advantage of the IIoT and supply chain performance. This chapter synthesizes existing literature reviews and making bibliometric analysis on the IIoT and supply chain. Additionally, It also outlines avenues and opportunities for future research aiming at the contribution of the IIoT to Supply Chain processes.
AtıfScopus18OpenAlex15İncele - MakaleAçık Erişim2019Comparison of the public and private university campusses in Turkey in terms of the sustainable – ecological parameters
Askin, Ali; Kaya, Sema Kayapinar; Dal, Murat
In this study, it is aimed to highlight the properties of the increasing number of the university campusses in Turkey in terms of sustainable and ecological properties. 5 public (Boğaziçi, Bülent Ecevit, Karabük, İstanbul Teknik, Ege) and 5 private (Özyeğin, Yeditepe, Bahçeşehir, Bilgi and Bilkent) universities are investigated in terms of sustainable and ecological campus criterias such as “Energy Management”, “Sustainable Transportation”, “Waste Management”, “Water Management”, “Environmental Management” and “Sustainable Education”.
AtıfOpenAlex13TR Dizin9İncele