Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - Scopus: 46
    Willingness to Pay for Renewable Electricity: A Contingent Valuation Study in Turkey
    (Elsevier Inc., 2019-12) Dogan, Eyup; Muhammad, Iftikhar
    Renewable energy sources are advised as an important alternative vehicle for dealing with a high rate of energy dependency and global warming. Turkey has also an ambitious national energy goal of minimizing energy import and producing 30% of electricity from renewable energy sources by 2023. However, it may not be easy to reach these goals. Willingness to Pay (WTP) thus plays a central role in directing appropriate policies for the country to realize its energy targets. This study reviews previous studies in the same literature as well as examines WTP of Turkish citizens for renewable electricity energy by using a stratified-sample and contingent valuation survey of 2500 households. The results from estimated models show that environmental conscience, membership to an environmental organization, age, education level, gender and income of households are significant determinants of WTP. In addition, the mean value of WTP for green electricity by Turkish households is estimated at around US$ 1 (with the exchange rate 5,3 TL/ US$) per month per household. A number of policy suggestions are further discussed. © 2023 Elsevier B.V., All rights reserved.
  • Article
    Citation - Scopus: 2
    Machine Learning Models With Hyperparameter Optimization for Voice Pathology Classification on Saarbrücken Voice Database
    (Elsevier Inc., 2025-01) Gulsen, Pervin; Gulsen, Abdulkadir; Alçı, Mustafa
    Early diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification. However, machine learning models and performance metrics employed in these studies vary significantly, making it challenging to determine the optimal model for voice pathology classification. In this study, healthy and pathological voices were classified with state-of-the-art machine learning models, and the performance results of the models were compared. The voice samples employed in our research were sourced from the Saarbrücken Voice Database, a reputable German database. Feature extraction from voice signals was conducted using the Mel Frequency Cepstral Coefficients method. To assess and enhance the models’ performance adequately, we employed hyperparameter optimization and implemented a 10-fold cross-validation approach. The outcomes revealed that the support vector machine model exhibited the highest accuracy, achieving 99.19% and 99.50% accuracies in the classification of male and female voice pathologies, respectively. © 2025 Elsevier B.V., All rights reserved.
  • Book Part
    Citation - Scopus: 1
    Estimation of Protease Activity by Use of the Mixolab
    (Elsevier Inc., 2013) Kahraman, Kevser; Köksel, Hamit F.
  • Book Part
    Citation - Scopus: 54
    Energy Harvesting and Battery Technologies for Powering Wireless Sensor Networks
    (Elsevier Inc., 2016) Tuna, Gürkan; Güngör, Vehbi Çağrı
    Due to the advances in wireless sensor networks (WSNs), factory and plant process automation systems are being reinvented. WSN-based industrial applications often cost much less than wired networks in both the short and long terms; automation engineers are empowering existing solutions with the new capabilities of WSNs. On the other hand, since industrial wireless sensor networks (IWSNs) consist of thousands of nodes, the problem of powering the nodes is critical. Power to the nodes is usually provided through primary batteries and this necessitates replacement when the batteries are depleted. However, the replacement may not be cost-effective or even feasible in most industrial applications.Though advancements in integrated circuit technologies help in saving more energy by leading to lower energy consumption levels, they do not eliminate the use of battery power. In this regard, energy harvesting technologies play a key role in extending the battery lifetime of the nodes. Wireless sensor nodes within industrial plants can operate from energy harvested from available energy sources such as heat, mechanical motion or vibration, indoor lighting, electromagnetic fields, and air flow. In this chapter, a review of existing energy storage technologies and various energy-harvesting techniques is given. The chapter then discusses open research issues in these topics. © 2020 Elsevier B.V., All rights reserved.