Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/10262
  Improved estimation of population variance in stratified successive sampling using calibrated weights under non-response

  Pandey, M. K.; Singh, G. N.; Zaman, Tolga; Al Mutairi, Aned; Mustafa, Manahil SidAhmed

  This paper introduces a new method to estimate the population variance of a study variable in stratified successive sampling over two occasions, while accounting for random non -response. The method uses a logarithmic type estimator and leverages information from a highly positively correlated auxiliary variable. The paper also presents calibrated weights for the new estimator and examines its properties through numerical and simulation studies. The results indicate that the suggested estimator is more effective than the standard estimator for estimating the population variance.

  AtıfScopus22·WoS18·OpenAlex21

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/14534
  Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

  Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana

  This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.

  AtıfScopus51·WoS19·OpenAlex54

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/14275
  Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis

  Yanarateş, Cağfer; Zhou, Zhongfu; Altan, Aytaç

  This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.

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  AtıfScopus13·WoS7·OpenAlex10

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/13989
  Determination of fatty acid, cholesterol, alpha-tocopherol, and aroma components of traditionally and commercially produced Trabzon butters

  Gündoğdu, Engin; Beşer, Musa

  This study investigated 29 butter samples from Trabzon, Turkey. Cholesterol contents ranged from 134.13 to 325 mg/100 g, α-tocopherol contents ranged from 1.62 to 3.37 mg/100 g, and β-carotene contents ranged from 4.46 to 15.60 μg/g. Fatty acid composition analysis showed variations, with palmitic acid ranging from 26.11 % to 44.25 %, oleic acid from 19.55 % to 29.80 %, and linoleic acid from 1.63 % to 3.04 %. A total of 44 aroma components were identified. Traditional butter samples exhibited differences in aroma components and fatty acid composition compared to commercial butter. Notably, traditional butter had higher concentrations of octanoic and n-decanoic acids than commercial butter. Moreover, some aroma components such as N-butanoic acid 2-ethylhexyl ester, decanoic acid, and pentadecane were found exclusively in traditional butters. Traditional butter showed higher α-tocopherol and β-carotene contents. These findings underscore the distinct chemical profiles of traditional and commercial butter samples, influenced by production methods and possibly geographical origin.

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  AtıfScopus1·OpenAlex4

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• MakaleAçık Erişim2023Scopus SJRQ1/r/gumushane/11946
  Performance analysis of digitally controlled nonlinear systems considering time delay issues

  Yanarateş, Cağfer; Okur, Serkan; Altan, Aytaç

  In this paper, a comprehensive investigation into discretization, effective sample time selection considering delays in the system, and time and frequency domain analysis of a DC-DC buck converter, which plays a vital role in photovoltaic (PV) systems, is conducted to enhance the understanding of their dynamic behavior, optimize control algorithms, improve system efficiency, and ensure reliable power conversion in photovoltaic applications. To effectively address the non-linear behavior and enhance digital control of a buck converter by selecting the best sample time, several approaches can be employed. These include accurate modeling and identification of non-linear elements, development of advanced control algorithms that account for non-linearities, implementation of adaptive control techniques, and utilization of feedback mechanisms to compensate for deviations from linearity. By considering and mitigating the non-linear behavior, digital control systems can achieve improved accuracy, stability, and transient behavior in regulating the buck converter's output waveforms (voltage or current). The results of the study demonstrated that the trapezoidal integration method which is also known as bilinear approximation, or Tustin's approach outperformed other commonly used discretization methods, such as first-order hold (FOH), zero-order hold (ZOH), impulse response matching (impulse invariant), and matched pole-zero (MPZ) technique, in dual-domain (both time and frequency) analysis. The key finding highlighting the superiority of the bilinear approximation was its ability to achieve the closest match in the frequency domain bridging the continuous-time and discrete systems. This finding emphasizes the significance of the bilinear approach in preserving the frequency characteristics of the original continuous-time system during discretization. By employing this method, the discrete system closely approximated the behavior of its continuous-time counterpart, ensuring accurate frequency-domain representation.

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  AtıfScopus60·WoS57·OpenAlex58

  İncele
• MakaleAçık Erişim2021Scopus SJRQ1/r/gumushane/10551
  Antidiabetic, anticholinergic and antioxidant activities of aerial parts of shaggy bindweed (Convulvulus betonicifolia Miller subsp.) - profiling of phenolic compounds by LC-HRMS

  Bingol, Zeynebe; Kiziltas, Hatice; Gören, Ahmet C.; Kose, Leyla Polat; Topal, Meryem; Durmaz, Lokman; Alwasel, Saleh H.; Gulcin, Ilhami

  In order to evaluate the antioxidant activity of evaporated ethanolic extract (EESB) and lyophilized water extract (WESB) of Shaggy bindweed (Convulvulus betonicifolia Mill. Subs), some putative antioxidant methods such as DPPH. scavenging activity, ABTS(center dot)(-) scavenging effect, ferric ions (Fe3+) reduction method, cupric ions (Cu2+) reducing capacity, and ferrous ions (Fe2+) binding activities were separately performed. Also, ascorbic acid, alpha-tocopherol and BHT were used as the standard compounds. Additionally, some phenolic compounds that responsible for antioxidant abilities of EESB and WESB were screened by liquid chromatography-high resolution mass spectrometry (LC-HRMS). At the same concentration, EESB and WESB demonstrated effective antioxidant abilities when compared to standards. In addition, EESB demonstrated IC50 values of 1.946 mu g/mL against acetylcholinesterase (AChE), 0.815 mu g/mL against alpha-glycosidase and 0.675 mu g/mL against alpha-amylase enzymes.

  AtıfScopus84·WoS83·OpenAlex88

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