Muharrem Düğenci

A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information

The aim of this study is to introduce a novel generalized distance measure for interval valued intuitionistic fuzzy sets and to illustrate the applicability of the proposed distance measure to group decision making problems. Firstly, a generalized distance measure is proposed along with proofs satisfying its axioms. Then, a comparison between the proposed distance measure and well-known distance measures is performed in terms of counter-intuitive cases. Subsequently, the extension of TOPSIS method, in which the proposed distance measure is used to calculate separation measures, to an interval valued intuitionistic fuzzy (IVIF) environment is demonstrated to solve multi-criteria group decision making (MCGDM) problems using optimal criteria weights determined with linear programming model based on the concept of maximizing relative closeness coefficient. Finally, two illustrative examples are provided for proof-of-concept purposes and to demonstrate benefits of using the proposed distance measure over the existing ones in IVIF TOPSIS method for MCGDM problems.

Heuristic-based neural networks for stochastic dynamic lot sizing problem

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.

Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms

Polymeric materials, being capable of high mouldability, usability of long lifetime up to 50 years and availability at low cost properties compared to metallic materials, are in demand but finite element-based design engineers have limited means in terms of the limited material data and mathematical models. In particular, in the analysis of products with complex geometry, the stresses and strains of various amounts formed in the product should be known and evaluated in terms of a precise design of the product to fulfil life expectancy. Due to time and cost constraints, experimental data cannot be available for all cases required in analysis, therefore, finite element method-based simulations are commonly used by design engineers. This is also computationally expensive and requires a simpler and more precise way to complete the design more realistically. In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN models have provided much accurate and precise results, which are in line with 10% accuracy of experimental data.

Extracting the dielectric constant of materials using ABC-based ANNs and NRW algorithms

ABSTRACT Five different Nicolson–Ross–Weir (NRW) extracting techniques are used to extract the dielectric constants of Teflon, Rexolite, Glass (borosilicate and soda-lime), Paper, and Ultralam 3850HT from S-parameters. The results of these extraction techniques are used to train the Artificial Neural Networks. In order to improve the accuracy of the results, the weights of ANNs are calculated using artificial bee colony estimation method. The results are compared with that obtained using NRW, Newton–Raphson, and genetic algorithm. The obtained results indicate that the proposed model gives good extracted parameters as compared with the previously published results.

Silah Sesleri Kullanılarak Ateşli Silahların Sınıflandırılmasında Akustik Parametrelerin Etkisi

Atesli silahlarla ilgili eylemler hem guvenlik gucleri hem de halk icin artan bir endise kaynagidir. Silah seslerinin siniflandirilmasi icin ticari veya deneysel cesitli calismalar mevcuttur. Bu calismanin amaci silah seslerini kullanarak atesli silahlarin turunu tespit etmede akustik analizin etkilerinin incelenmesidir. Calismada 23 atesli silaha ait 510 atis ses kaydi kullanilmistir. Akustik analiz icin formant frekanslari, MFCC, LPCC ve enerji parametreleri incelenmistir. Akustik parametrelerin silahlari siniflandirmadaki etkinligi istatistiksel olarak analiz edilmis ve kullanilan tum parametrelerin etkili oldugu gorulmustur. MLP siniflandirici ile siniflandirma performansi test edilmis ve atesli silah tanima orani %71.56 elde edilmistir. Tanima orani en yuksek silah turu “Carl Gustav M45” ve “Tokarev PPSh”, tanima orani en dusuk olan silah “Tikka Model T2” olarak tespit edilmistir.

Estimating Seebeck Coefficient of a p -Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception

Thermoelectric generators (TEGs) convert heat into electrical energy. These energy-conversion systems do not involve any moving parts and are made of thermoelectric (TE) elements connected electrically in a series and thermally in parallel; however, they are currently not suitable for use in regular operations due to their low efficiency levels. In order to produce high-efficiency TEGs, there is a need for highly heat-resistant thermoelectric materials (TEMs) with an improved figure of merit (ZT). Production and test methods used for TEMs today are highly expensive. This study attempts to estimate the Seebeck coefficient of TEMs by using the values of existing materials in the literature. The estimation is made within an artificial neural network (ANN) based on the amount of doping and production methods. Results of the estimations show that the Seebeck coefficient can approximate the real values with an average accuracy of 94.4%. In addition, ANN has detected that any change in production methods is followed by a change in the Seebeck coefficient.

Face recognition by distance and slope between facial landmarks

Facial landmark is to be determined point by point of areas such as eyes, nose, mouth, eyebrows on the face. Facial recognition studies can be generally categorized into two categories, local and global. All of the faces are used in the global face recognition while the face domain is divided into subspaces in the local face recognition. In face recognition studies facial landmarks are used for facial recognition with detection of regions located at the face and image processing methods. In this study, face recognition has been successfully analyzed using distance and slope between facial landmarks. Analyzes were performed with both statistical and classifiers. According to the results obtained, the distance and slope between the 14 landmarks used in the study were found to be statistically significant in the facial recognition. In addition, the classification was performed with the help of these features and the highest success was found with 94.60% with MLP classifier. Obtained findings show the usability of the distance and slope between the landmarks in facial recognition.

Statistical analysis of water discharging from rocks of different origin: a case study from Turkey

The present study was conducted in Sivas, Karabük and Bartın regions of Turkey, which have rocks of different origins, agricultural and mining activities. Correlation, principal components, hierarchical cluster and multidimensional scaling analyses were applied to determine the processes controlling the chemical composition of groundwater. The results show that dissolution-weathering process, agricultural activities, oxidation processes of sulfide minerals, mining activities, coal levels, alteration of volcanics and progressive silicate hydrolysis effects the physicochemical properties of groundwater in the study areas. Principal components and multidimensional scaling analyses provided excellent visual representations of the grouping of the waters. The significant variables in the first factor are SO4, Mn, Fe, Al, and pH. The factor represents the groundwater reached by these elements via the dissolution and oxidation processes of sulfide minerals (especially pyrite). Ca, EC, and HCO3 are generally grouped under the second factor representing the dissolution of carbonate rocks. The third factor represented by Na, CO3, and pH is mostly related to alteration of volcanics, progressive silicate hydrolysis and dissolution, and probably ion exchange between Ca and Na. The fourth factor of NO3 and Cl is strongly influenced by agricultural activity. The measurement, analyses and evaluation results showed that the groundwater contamination is caused by (1) NO3 in waters discharging from clastic rocks in areas where intensive agricultural activities are conducted; (2) Al, Fe, Mn, and SO4 ions in water emerging from volcanics containing Pb–Zn–Cu ore deposits; and (3) Al, Fe, and Mn in water issuing from coal levels and altered volcanics. Some of these waters are used by adjacent towns for drinking, domestic, and irrigation purposes.

Intolerance of Uncertainty and Coping Mechanisms in Nonclinical Young Subjects

INTRODUCTION We aimed to explore the relationship between intolerance of uncertainty (IU) and coping mechanisms in a nonclinical sample with the same age and educational level. METHODS The Coping Orientations to Problems Experienced (COPE) scale was used to evaluate the coping mechanisms. The IU scale was used to evaluate IU situations. RESULTS We found that the negative impact of uncertainty on the action in female students was greater than males. While female students used more planning, instrumental support, reinterpretation, religion, emotional support, venting, and mental disengagement coping styles, male students used more humor, denial, and alcohol/drug abuse coping styles. Subjects with psychological problems had higher IU scores and used some more coping mechanisms (restraint, acceptance, behavioral disengagement, and alcohol/drug abuse) than the others. CONCLUSION Our results suggest that healthy subjects use different coping styles and respond differently to uncertainty in both genders.