Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ2Forecasting daily natural gas consumption with regression, time series and machine learning based methods
Yucesan, Melih; Pekel, Engin; Celik, Erkan; Gul, Muhammet; Serin, Faruk
An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework’s applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R2), and mean squared error (MSE). According to each method’s results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.
AtıfScopus16WoS19OpenAlex22
İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using interval-valued pythagorean fuzzy vikor
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
This chapter aims at the adaptation of the Fine–Kinney occupational risk assessment concept into the VIKOR multi-attribute decision-making method with an interval-valued Pythagorean fuzzy set. The classical fuzzy set theory has been improved by proposing a number of extended versions. One of them is the Pythagorean fuzzy set. It has been firstly developed by Yager (IEEE Transactions on Fuzzy Systems 22:958–965, [1]). In this chapter, we use this type of fuzzy set with VIKOR since it reflects uncertainty in occupational risk assessment and decision-making better than other fuzzy extensions. To demonstrate the proposed approach applicability, a case study regarding the activities of the surface treatment area in a chrome plating unit of a gun factory is performed. Some additional analysis to test the solidity and validity of the approach is executed. Finally, the Python codes in the implementation of the proposed approach are given for scholars and practitioners for usage in further studies.
AtıfScopus14OpenAlex5İncele - EditoryalYalnız künye2021Preface
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
AtıfScopus2İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using interval type-2 fuzzy vikor
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
In this chapter, we improved Fine–Kinney occupational risk assessment approach with interval type-2 fuzzy VIKOR (IT2FVIKOR). VIKOR is a compromise multi-attribute decision-making method proposed by Opricovic (1998). Similar to other compromised solution-based approaches, it considers the solution which is closest to the ideal. In this chapter, we adapted the interval type-2 fuzzy sets (IT2FSs) into VIKOR as it reflects the uncertainty well in decision-making. IT2FVIKOR algorithm under the Fine–Kinney concept provides a useful and solid approach to the occupational health and safety risk assessment. In addition to proposing this new approach, a case study is performed in a gun and rifle barrel external surface oxidation and coloring unit of a gun factory. A validation and a sensitivity analysis is also attached to this study. Finally, the proposed approach is implemented in Python.
AtıfScopus4OpenAlex5İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using fuzzy best and worst method (f-bwm) and fuzzy mairca
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
The best-worst method (BWM) proposed by Rezaei (Omega 53:49–57, [1]) is an MCDM method used to achieve the weights of the criteria by making fewer pairwise comparisons and using more consistent decision matrices. In this chapter, instead of crisp numbers, triangular fuzzy numbers that reflect the uncertainty well in real-world problems are used in integration with the BWM method (Guo and Zhao in Knowl Based Syst 121:23–31, [2]) in determining factor weights. Using the fuzzy best and worst method (F-BWM), a model based on Fine–Kinney occupational health and safety risk assessment method was developed for the first time in the literature. Three parameters of Fine–Kinney method are weighted by the mathematical models of F-BWM. Then, the risks are prioritized by fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA). A case study was conducted to demonstrate the feasibility of the approach, and besides this case study, a comparative study was also conducted to test the validity of the proposed approach. This approach led to the conclusion that Fine–Kinney’s method, BWM, MAIRCA, and triangular fuzzy sets make the risk decision-making process more dynamic, taking into account the benefits of these methods individually or in integration.
AtıfScopus11OpenAlex12İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using hexagonal fuzzy multimoora
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
Hexagonal fuzzy numbers (HFNs) can be used as a proficient logic to simplify understanding of ambiguity information. HFNs present the usual information in a comprehensive way and also the ambiguity section can be exemplified in a reasonable way. In this chapter, we proposed an improved Fine–Kinney occupational risk assessment approach using a well-known MCDM method Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) using hexagonal fuzzy numbers. Since the mere MULTIMOORA has failed to handle uncertainty and vague information which usually exist in real world problems, we follow integration of HFNs and MULTIMOORA (HFMULTIMOORA). To show the applicability of the novel approach, a case study of risk assessment of a raw mill in cement plant was provided. Comparative analysis with using two aggregation tools as reciprocal rank method and dominance theory are carried out. Finally, the Python implementation of the proposed approach is implemented to be effective for those concerned in the future.
AtıfScopus7OpenAlex5İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using intuitionistic fuzzy todim
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
This chapter applies a novel occupational risk assessment approach which merges TODIM with the Fine–Kinney method under the intuitionistic fuzzy set concept. Risk parameters of Fine–Kinney and OHS experts are weighted by an intuitionistic fuzzy weighted averaging (IFWA) aggregation operator. Hence, hazards are quantitatively evaluated and prioritized using the proposed approach. To illustrate the novel risk assessment approach, processes of the gun and rifle assembly line of a factory are handled. A comprehensive risk assessment is carried out to improve operational safety and reliability in the industry. We adapt intuitionistic fuzzy sets in the existing study since they reflect uncertainty with the aid of their membership and nonmembership functions in decision-making better than classical fuzzy extensions. An additional sensitivity analysis by changing the attenuation parameter of TODIM is performed to test the validity of the approach. Finally, the Python codes in the implementation of the proposed approach are given for scholars and practitioners for usage in further studies.
AtıfScopus5OpenAlex9İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using single-valued neutrosophic topsis
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
Neutrosophic sets are initially recommended by Smarandache (First international conference on neutrosophy, neutrosophic logic, set, probability, and statistics. University of New Mexico, Gallup, NM, pp 338–353, 2002 [1]). These sets reflect uncertainty and vagueness in real-world problems better than classical fuzzy set theory. It takes into consideration three decision-making situations called indeterminacy, truthiness, and falsity. In Zadeh traditional fuzzy set theory, there is just membership function fuzzy set degree. But, in neutrosophic environment, it considers three membership functions. Unlike intuitionistic fuzzy sets, an indeterminacy degree is considered. In this chapter, we applied a special form of neutrosophic set as single-valued neutrosophic set (SVNs) with the technique for order preference by similarity to ideal solution (TOPSIS) under the concept of Fine–Kinney occupational risk assessment. Since the mere TOPSIS has failed to handle imprecise and vague information which usually exist in real-world problems, we follow the integration of SVNs and TOPSIS. To demonstrate the applicability of the novel approach, a case study of risk assessment of a wind turbine in times of operation was provided. Comparative analysis with some similar approaches and sensitivity analysis by changing the weights of Fine–Kinney parameters are carried out. Finally, the Python implementation of the proposed approach is executed to be useful for those concerned in the future.
AtıfScopus12OpenAlex9İncele - Kitap BölümüYalnız künye2021Fine–kinney occupational risk assessment method and its extensions by fuzzy sets: A state-of-the-art review
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
The Fine–Kinney method (Fine in J Saf Res 3:157–166, 1971; Kinney and Wiruth in Practical risk analysis for safety management. Naval Weapons Center, pp 1–20, 1976), which was first introduced as an occupational health and safety risk analysis tool in the 1970s, is a systematic methodology that provides a mathematical formula for calculating the risk that arises due to a specified hazard. In the traditional version of Fine–Kinney as suggested in its original version, a risk score (RS) is calculated as a result of mathematical multiplication of probability (P), exposure (E), and consequence (C) parameters. These calculated risk scores are used to establish priorities for the corrective efforts in order to eliminate risks or reduce their effects to a reasonable level. This simple and useful method is preferred and implemented by small and medium-sized enterprises. In the academic literature, it has been applied for many risk analysis problems, although it includes several drawbacks recently revealed. In this method, no weight assignment is made for each risk parameter. Also, it is hard to assess consequence, exposure, and probability, precisely. Multi-criteria decision making (MCDM) is a pool of methods used in occupational health and safety risk analysis both by international standard-setting organizations and scholars from the literature. In classical MCDM methods, performance values and weights of decision criteria are known precisely and are specified with crisp numbers. However, many real-world problems contain uncertainties, and the knowledge and judgment of experts cannot be expressed precisely. Fuzzy-based MCDM methods, which are developed to reflect types and degrees of uncertainties better, produce more accurate results compared to classical methods. In this chapter, we first present the basics of Fine–Kinney method, including its implementing procedure, basic terminology, and drawbacks. Then, we provide a state-of-the-art review of Fine–Kinney occupational risk assessment method and its extensions by fuzzy sets. Graphical results obtained from the review are demonstrated to show the current state. Future work suggestions are also included to the chapter to show the possible gaps and possible opportunities.
AtıfScopus12OpenAlex12İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using interval type-2 fuzzy qualiflex
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
In this chapter, we improved Fine–Kinney occupational risk assessment approach with interval type-2 fuzzy QUALIFLEX (IT2FQUALIFLEX). QUALIFLEX is an outranking multi-attribute decision-making method proposed by an extension of the Paelinck’s (Pap Reg Sci Assoc 36:59–74,[1]), generalized Jacquet-Lagreze’s permutation method. Similar to other outranking solution-based approaches, it considers the solution which is a comparison of hazards. In this chapter, we adapted the interval type-2 fuzzy sets (IT2FSs) into QUALIFLEX as it reflects the uncertainty well in decision-making. IT2FQUALIFLEX algorithm under the Fine–Kinney concept provides a useful and solid approach to the occupational health and safety risk assessment. In addition to proposing this new approach, a case study is performed in the chrome plating unit. A validation is also performed in this study. Finally, the proposed approach is implemented in Python.
AtıfScopus7OpenAlex7İncele - Kitap BölümüYalnız künye2021Fine–kinney-based occupational risk assessment using interval type-2 fuzzy topsis
Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan
This chapter proposed an improved Fine–Kinney occupational risk assessment approach using a well-known MCDM method “TOPSIS” under interval type-2 fuzzy set concept. It is defined as a technique for order preference by similarity to ideal solution by Hwang and Yoon [1]. It is based on separation from ideal and anti-ideal solution concept. Since the initial crisp data-based version is insufficient by time in reflecting the uncertainty in decision-maker’s opinions, fuzzy sets are integrated to the TOPSIS algorithm to provide a solid and comprehensive method. Interval type-2 fuzzy set is an improved version of the type-1 fuzzy set. It is also a special version of a general type-2 fuzzy set. Since general type-2 fuzzy systems contain complex computational operations, they cannot be easily applied to real-world problems such as occupational risk assessment. Interval type-2 fuzzy sets are the most frequently used type-2 fuzzy sets due to their ability in handling more uncertainty and producing more accurate and solid results. The Fine–Kinney concept is merged with the interval type-2 fuzzy set concept and TOPSIS for the first time through the literature. To demonstrate the applicability of the proposed approach, a case study is carried out in a chrome plating unit of a gun factory. Some beneficial validation and sensitivity analysis are also performed. Finally, as a creative contribution of our book, the implementation of the proposed approach in Python is performed.
AtıfScopus2OpenAlex3İncele - MakaleAçık Erişim2020A Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Images
Serin, Faruk; Erturkler, Metin; Gul, Mehmet
In this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&E stained histopathological tissue images. The proposed algorithm does not require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels' color value in the images with RGB color space, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. Two more algorithms are also proposed to distinguish nuclei cluster from the other clusters obtained by K-means, and eliminate false positives in nuclei cluster, which are not nuclei. In order to compare and evaluate the performance of the proposed segmentation algorithm in terms of time and cost efficiency, K -Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations show that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K -Means and expert is 5%.
AtıfOpenAlex2
İncele - KitapYalnız künye2020Computational intelligence and soft computing applications in healthcare management science
Gul, Muhammet; Celik, Erkan; Mete, Suleyman; Serin, Faruk
In today's modernized world, the field of healthcare has seen significant practical innovations with the implementation of computational intelligence approaches and soft computing methods. These two concepts present various solutions to complex scientific problems and imperfect data issues. This has made both very popular in the medical profession. There are still various areas to be studied and improved by these two schemes as healthcare practices continue to develop.
AtıfOpenAlex7İncele - EditoryalYalnız künye2020Preface
Gul, Muhammet; Celik, Erkan; Mete, Suleyman; Serin, Faruk
AtıfScopus1İncele - Kitap BölümüYalnız künye2020NARX neural networks model for forecasting daily patient arrivals in the Emergency Department
Yucesan, Melih; Mete, Suleyman; Serin, Faruk; Celik, Erkan; Gul, Muhammet
Regarding measuring of service quality at the emergency departments (ED), essential parameters are length of stay (LOS) and waiting times. Patient arrivals, which is related to LOS and waiting times, is hard to forecast and is affected by many parameters. Therefore, authors employed a Nonlinear Autoregressive Exogenous (NARX) model for forecasting of ED arrivals. NARX models are used extensively in many applications that show non-linear and dynamic behavior, but as far as authors know, the NARX method has not yet been used in the forecast of ED arrivals before. In this study, calendar and climatic variables are defined as input parameters. Patient Arrivals is defined as output parameter. A commercial software, MATLAB, was used to train and test the data set. To find the best network architecture Levenberg- Marquardt (LM) and Bayesian Regularization (BR) algorithms, different lags, and number of neurons were tested. R-squared and mean square error (MSE) are used to evaluate the accuracy of the tested networks.
AtıfScopus2OpenAlex4İncele - MakaleYalnız künye2020Scopus SJRQ2A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing
Serin, Faruk; Erturkler, Metin
The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell-location-based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance-based threshold, K-Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell-graph. Histopathological images of liver were classified by using features extracted from T-Distance, K-Nearest Neighbor, Voronoi, Delaunay, and the proposed cell-graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell-graph model.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2020Scopus SJRQ1Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach
Çil, Zeynel Abidin; Mete, Suleyman; Serin, Faruk
The use of robots is significantly increasing day by day in manufacturing systems, and especially improving the efficiency of the lines. Robots can be used to complete disassembly tasks, and each of the robots can need different operation times to perform the tasks. In this paper, the balancing of the robotic disassembly line problem has been studied to develop efficient solution techniques. Firstly, a mixed-integer linear mathematical model is proposed to determine and solve the problem optimally. A case study from literature is addressed to assess and show the efficiency and effectiveness of the model to minimize cycle time. Secondly, a heuristic algorithm based on ant colony optimization is also proposed to discover a solution for especially the large-size test problems due to the complexity of the problem. The performance of the proposed heuristic algorithm is verified and compared with the different heuristic on data sets. The computational results indicate that the proposed mathematical model and the algorithms are promising for the small and large-size test problems, respectively. Finally, it should be stated that robots have great potential to use in the area of disassembly line and useful solutions provide according to test results.
AtıfScopus80OpenAlex84İncele - BildiriYalnız künye2020A risk assessment approach using both stochastic data and subjective judgments
Mete, Suleyman; Oz, Nurdan Ece; Gul, Muhammet; Serin, Faruk; Celik, Erkan; Kahraman, C.; Cevik Onar, S.; Oztaysi, B.; Sari, I.U.; Cebi, S.
Risk assessment describes the overall process regarding identifying hazards and risk factors that have the potential to cause harm, analyzing and evaluating the risk associated with that hazard and determining appropriate ways to eliminate the hazard or control the risk. In the literature, lots of approaches are proposed to prioritize hazards and associated risks quantitatively. Fuzzy sets-based multi-criteria decision-making approaches are forefront that they have the ability in expressing the risks verbally based on decision-maker’s opinions and judgments. MCDA-based risk assessment approaches explain different types of uncertainties, which are generally modeled using stochastic analysis or fuzzy sets. The stochastic analysis is more suitable in modelling the probabilistic uncertainty. Therefore, in this study, a risk assessment approach using both stochastic data and subjective judgments is proposed, and a case study is also performed in a construction project. The VIKOR method is extended and a new risk assessment approach is developed where part of the data is stochastic. A three-year data is used to determine a fitted probability distribution of respected frequency. Evaluation of risks with respect to severity and probability parameters are then made by subjective judgments. In conclusion, a risk prioritization and potential action plan suggestion is performed.
AtıfScopus2OpenAlex3İncele - MakaleAçık Erişim2019Public transportation graph: A graph theoretical model of public transportation network for efficient trip planning
Serin, Faruk; Mete, Suleyman
The presentation and usage of traditional graphs is very important for effective and fast solution of routing in public transportation. However, the traditional graph approach is unable to consider the passenger requests such as total travel time, minimum number of transfer and total distance of travel without pre-processing and/or post-processing. Moreover, the vehicles are not represented on traditional graph. In this paper, after analyzing the different kind of graphs, we propose a novel graph named as public transportation graph. The proposed graph models the public transportation system and considers distance, waiting time, travel time, self-transportation and number of transfers simultaneously for efficient trip planning. In this way, passenger requests can be met without pre-processing and post-processing. In addition, the vehicles are also considered and demonstrated in the proposed graph.
AtıfWoS1OpenAlex4TR Dizin1İncele - MakaleYalnız künye2019Scopus SJRQ2Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards
Oz, Nurdan Ece; Mete, Suleyman; Serin, Faruk; Gul, Muhammet
A natural gas pipeline project is a broad construction system consisting of both offshore and onshore section, which aims to supply the natural gas to one country to another. In such these projects, main focus of the works should cover workplace, environment, health, and safety of workers and employers. To achieve this, a new occupational health and safety (OHS) risk assessment is necessary for reducing the negative effects of emerged risks. In this article, a fuzzy-based risk model is developed for prioritizing primary and residual risk. A two-dimensional (2D) risk matrix method is initially performed and the Pythagorean fuzzy technique for order preference by similarity to ideal solution (PFTOPSIS) method is then used to prioritize previously identified hazards. For the prioritization of primary and residual risks, hazards are assessed against two parameters (probability and severity) of 2D risk matrix. Unlike previous risk assessment studies, PFTOPSIS is applied for the first time. The fuzzy model is applied to a case study for clearing and grading process of a natural gas pipeline project. A sensitivity analysis is also performed on parameter weights in order to validate the model.
AtıfScopus93WoS80OpenAlex97İncele