Yazar "Dugenci, Muharrem" seçeneğine göre listele

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    Application of a Seat-based Booking Control Mechanism in Rail Transport with Customer Diversion
    (Eos Assoc, 2022) Bulum, Ahmet Z.; Dugenci, Muharrem; Ipek, Mumtaz
    The ticket booking control mechanism is a part of the Revenue Management (RM), commonly used in the airline industry. This study aims to optimize seat allocation in the railway industry and compare the performance of three booking control techniques by considering customer behavior. The preferences of customers who cannot find their desired ticket are considered as a customer diversion matrix, which also includes waiting and no-purchase probability. Alpha Ticket Booking System (TBS) with buckets, which assigns seats to buckets, was adapted and implemented on the Turkish railway for the first time. A genetic algorithm that is specifically written to apply the TBS, including customer diversion, is used in simulations to obtain approximate solutions. It is seen that TBS gave successful results with a revenue increase of around 5.8%. We can also suggest, considering customer behavior, that the revenue can be raised by sales in periods.
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    Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms
    (Pergamon-Elsevier Science Ltd, 2015) Dugenci, Muharrem; Aydemir, Alpay; Esen, Ismail; Aydin, Mehmet Emin
    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. (C) 2015 Elsevier Ltd. All rights reserved.
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    Diversifying Search in Bee Algorithms for Numerical Optimisation
    (Springer International Publishing Ag, 2018) Dugenci, Muharrem; Aydin, Mehmet Emin
    Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving wellknown highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks.
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    The effect of age and gender on the acoustic analysis of anxious sound
    (Inst Advanced Science Extension, 2016) Ozseven, Turgut; Dugenci, Muharrem; Doruk, Ali
    The aim of this study is to investigate the effects of age and gender in sound reflection of anxiety with acoustic analysis. In the study, 148 speech records that express the emotions of the actors as anxiety and neutral were used as the data set. PRAAT software is used for acoustic analysis. The ANOVA method was used to analyze the data. The according to the results of statistical analysis, gender and age increased the count of acoustic parameters that affected of anxiety. The standard deviation of F0 increased too much, jitter local and jitter rap increased mid-range and other parameters did not change when examined changes based gender. The mean of F0, shimmer apq3 and number of unvoiced frame decreased to mid-range, the standard deviation of F0 and jitter local increased too much, the standard deviation of F3 and jitter rap increased to mid-range and other parameters did not change when examined changes based age. The changes occurring in emotions cause changes in sound by affecting respiratory and muscle tension. The anxiety has been changed according to gender and age because the number of parameters in the analysis based on the gender and age is more. The gender causes change in the speed of glottic cycle and this change increases with anxiety. In addition, vocal cords by both male and female occur irregularities and this case also differs according to age. The irregularities in intensity of sound in lower ages are being further while the pauses in the conversation with advancing age are increasing. (C) 2016 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    The effects of digital filters on acoustic parameters, gender, age and emotion
    (Pamukkale Univ, 2017) Ozseven, Turgut; Dugenci, Muharrem
    Acoustic sound analysis is used for decision making process performed feature extraction from sound signal in environments such as machine, human and underwater. The pitch, formants, jitter and shimmer of acoustic parameters are mostly used in sound analysis. All frequency bands of various sound signals obtained from environments is not available for every system. The low frequency component is used for emotion detection in the human voice. In addition, sound signal must be have unwanted noise due to both form of records and environment. The desired frequency band of sound signal can be obtained, processes such as noise reduction, echo reduction and sound quality improving are realized with the help of digital filters. The purpose of this study is to investigate the effects of low-pass, high-pass and band-pass filters on acoustics parameters depending on age, gender and emotion.
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    Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception
    (Springer, 2017) Uysal, Fatih; Kilinc, Enes; Kurt, Huseyin; Celik, Erdal; Dugenci, Muharrem; Sagiroglu, Selami
    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.
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    Extracting the dielectric constant of materials using ABC-based ANNs and NRW algorithms
    (Taylor & Francis Ltd, 2016) Ozturk, Turgut; Elhawil, Amna; Dugenci, Muharrem; Unal, Ilhami; Uluer, Ihsan
    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.
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    Face Recognition by Distance and Slope between Facial Landmarks
    (Ieee, 2017) Ozseven, Turgut; Dugenci, Muharrem
    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.
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    Heuristic-based neural networks for stochastic dynamic lot sizing problem
    (Elsevier, 2013) Senyigit, Ercan; Dugenci, Muharrem; Aydin, Mehmet E.; Zeydan, Mithat
    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. (C) 2012 Elsevier B. V. All rights reserved.
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    A honeybees-inspired heuristic algorithm for numerical optimisation
    (Springer London Ltd, 2020) Dugenci, Muharrem; Aydin, Mehmet Emin
    Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.
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    Intolerance of Uncertainty and Coping Mechanisms in Nonclinical Young Subjects
    (Turkish Neuropsychiatry Assoc-Turk Noropsikiyatri Dernegi, 2015) Doruk, Ali; Dugenci, Muharrem; Ersoz, Filiz; Oznur, Taner
    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.
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    A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information
    (Elsevier Science Bv, 2016) Dugenci, Muharrem
    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. (C) 2015 Elsevier B.V. All rights reserved.
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    A prediction model of artificial neural networks in development of thermoelectric materials with innovative approaches
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2020) Kokyay, Seyma; Kilinc, Enes; Uysal, Fatih; Kurt, Huseyin; Celik, Erdal; Dugenci, Muharrem
    The fact that the properties of thermoelectric materials are to be estimated with Artificial Neural Networks without production and measurement will help researchers in terms of time and cost. For this purpose, figure of merit, which is the performance value of thermoelectric materials, is estimated by Artificial Neural Networks without an experimental study. P-and n-type thermoelectric bulk samples were obtained in 19 different compositions by doping different elements into Ca2.7Ag0.3Co4O9- and Zn0.98Al0.02O-based oxide thermoelectric materials. The Seebeck coefficient, electrical resistivity and thermal diffusivity values of the bulk samples were measured from 200 degrees C to 800 degrees C with an increase rate of 100 degrees C, and figure of merit values were calculated. 7 different Artificial Neural Network models were created using 123 measured results of experimental data and the molar masses of the doping elements. In this system aiming to predict the electrical resistivity, thermal diffusivity and figure of merit values of thermoelectric materials, the average R value and accuracy rate of these values were estimated to be 94% and 80%, respectively. (c) 2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    Prediction of electrical conductivity using ANN and MLR: a case study from Turkey
    (Springer Int Publ Ag, 2020) Keskin, TUlay Ekemen; Ozler, Emre; Sander, Emrah; Dugenci, Muharrem; Ahmed, Mohammed Yadgar
    The study areas are located in Turkey (Kastamonu, Bartin, Karabuk, Sivas) and contain very different rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have different chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difficult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artificial neural networks (ANN) and multiple linear regressions (MLR). Twelve different hydrochemical parameters, which affect the EC, such as major/minor ions and trace elements, were used in the analysis. Multilayer feed-forward ANN trained with backpropagation in Python machine learning libraries was used in this study. In order to obtain the most appropriate ANN architecture, trial-and-error procedure was used and different numbers of hidden layers, neurons, activation functions, optimizers, and test sizes were constructed. This study also tests the usability of input parameters in EC prediction studies. As a result, comparisons between the measured and predicted values indicated that the machine learning models could be successfully applied and provide high accuracy and reliability for EC and similar parameters forecasting.
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    Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabuk and Bartin (Turkey)
    (Springer, 2015) Keskin, Tulay Ekemen; Dugenci, Muharrem; Kacaroglu, Fikret
    The determination of the rock types from which the water is recharged/discharged is an essential component of hydrochemical, hydrogeological and water pollution studies. Especially, detection of sources of groundwater contamination is very important in terms of human health and other living organism. This study aims at prediction of water pollution sources using artificial neural networks (ANNs) in Sivas, Karabuk and Bartin areas of Turkey, which have different types of rocks, agricultural activity and mining activity. In this study, a model based on ANNs was developed for forecast to the water discharging from different types of rocks and the water pollution sources in the study areas. Back propagation and Bee Algorithm (BA) were used in ANN training. For achieving the aim of the study, 14 hydrochemical data set were used. The best ANN classification of water discharging from different type of rocks was accomplished with 80 % accuracy using BA. These results indicate that the researches that are similar to this study can provide quite convenience for the assessment of groundwater pollution sources when applied on a large and regional scale.
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    SPeech ACoustic (SPAC): A novel tool for speech feature extraction and classification
    (Elsevier Sci Ltd, 2018) Ozseven, Turgut; Dugenci, Muharrem
    Background and objective: The acoustic analysis, an objective evaluation method, is used to determine the descriptive attributes of the voices. Although there are many tools available in the literature for acoustic analysis, these tools are separated by features such as ease of use, visual interface, and acoustic parameter library. In this work, we have developed a new toolbox named SPAC for extracting and simulating attributes from speech files. Methods: SPAC has a modular structure and user-friendly interface, which will make up for the shortcomings of existing vehicles. In addition, modules can be used independently of each other. With SPAC, about 723 attributes can be extracted from each voice file in 9 categories. A validation test was applied to verify the validity of the toolbox-derived attributes. When the validation test was performed, the attributes obtained with Praat and OpenSMILE were grouped as standard, the attributes obtained with SPAC as test data, and the general differences between the attributes were evaluated with mean square error and mean percentage error. In another method used for verification, the classification performance is tested using the SPAC-derived attributes for classification. Results: According to the validation test results, SPAC attributes differ between 0.2% and 9.7% compared to other toolboxes. According to the results of the classification test, the SPAC attribute clusters can identify each class and the classification success varies between 1% and 3% according to the attributes obtained from other toolboxes. As a result, the attributes obtained with SPAC accurately describe the voice data. Conclusions: SPAC's superiority over existing toolboxes is that it has an easy-to-use user-friendly interface, it is modular, allows graphical representation of results, includes classification module and allows to work with SPAC data or data obtained from different toolboxes. In addition, operations performed with other tools can be performed more easily with SPAC.
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    Statistical analysis of water discharging from rocks of different origin: a case study from Turkey
    (Springer, 2016) Keskin, Tulay Ekemen; Kacaroglu, Fikret; Ersoz, Taner; Dugenci, Muharrem
    The present study was conducted in Sivas, Karabuk and Bartin 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.
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    Voice Traces of Anxiety: Acoustic Parameters Affected by Anxiety Disorder
    (Polska Akad Nauk, Polish Acad Sciences, Inst Fundamental Tech Res Pas, 2018) Ozseven, Turgut; Dugenci, Muharrem; Doruk, Ali; Kahraman, Hilal I.
    Although the emotions and learning based on emotional reaction are individual-specific, the main features are consistent among all people. Depending on the emotional states of the persons, various physical and physiological changes can be observed in pulse and breathing, blood flow velocity, hormonal balance, sound properties, face expression and hand movements. The diversity, size and grade of these changes are shaped by different emotional states. Acoustic analysis, which is an objective evaluation method, is used to determine the emotional state of people's voice characteristics. In this study, the reflection of anxiety disorder in people's voices was investigated through acoustic parameters. The study is a case-control study in cross-sectional quality. Voice recordings were obtained from healthy people and patients. With acoustic analysis, 122 acoustic parameters were obtained from these voice recordings. The relation of these parameters to anxious state was investigated statistically. According to the results obtained, 42 acoustic parameters are variable in the anxious state. In the anxious state, the subglottic pressure increases and the vocalization of the vowels decreases. The MFCC parameter, which changes in the anxious state, indicates that people can perceive this situation while listening to the speech. It has also been shown that text reading is also effective in triggering the emotions. These findings show that there is a change in the voice in the anxious state and that the acoustic parameters are influenced by the anxious state. For this reason, acoustic analysis can be used as an expert decision support system for the diagnosis of anxiety.