Scopus Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10761

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    Estimating the COVID-19 Death Counts Using a Hesitant Fuzzy Linear Regression Depend on Race, Age and Location
    (2022) Dengiz, Asiye Ozge; Atalay, Kumru Didem
    The COVID-19 pandemic that has struck the world has caused social and economic problems in people's lives. Many countries are trying to reduce the impact of the pandemic by taking precautions to prevent the spread of the virus and reduce the number of deaths. Despite the precautions, many people have contracted the virus and a significant number have lost their lives. This suggests that some factors about the exact mechanism of how people contract the virus and get sick are still unclear. Therefore, many researchers wonder if there are other factors that make people more susceptible to the COVID-19 virus. In this study, hesitant fuzzy linear regression (HFLR) models are applied depending on variables such as age, race, and place of residence that are thought to influence COVID-19 deaths. HFLR provides an alternative approach to statistical regression for modeling situations with incomplete information. The models include input and output variables as hesitant fuzzy elements (HFEs). The relationship between the considered variables and the number of deaths is examined using data related to people living in different states of the United States. In addition, HFLR is used as an estimation model due to the uncertainty in the data obtained from the Centers for Disease Control and Prevention (CDC). The proposed HFLR models are used both to estimate COVID-19 deaths and to determine the effects of selected variables on COVID-19 deaths. In this way, countries can estimate the risk of death for individuals given these factors and determine what precautions to take for high-risk groups.
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    The Relationship Between Functional Head Impulse Test And Age In Healthy Individuals
    (2022) Emekci, Tugba; Erbek, Hatice Seyra; 34120922
    BACKGROUND: fHIT is an easily applicable test battery that indirectly evaluates the vestibulo-ocular reflex (VOR) from a functional perspective. AIMS/OBJECTIVES: The aim of this study was to Individuals determine the correlation between age and the percentage of correct answers (%CA) obtained in the functional head impulse test (fHIT) in healthy individuals. MATERIAL AND METHODS: A total of 105 volunteers, 50 males and 55 females, between the ages of 18 and 70 years, participated in the study. A Beon Solution fHIT system (Zero Branco (TV), Italy) was used in the study. RESULTS: In our study, a decrease in the mean %CA was observed in all semicircular canals (SCCs) with increasing age. Between age and mean %CA, a significant negative moderate (-0.311) correlation was observed in lateral SCCs, and a significant negative low (-0.257) correlation was observed in posterior SCCs (p < 0.05). In anterior SCCs, there was no statistically significant relationship between age and mean %CA (p > 0.05). CONCLUSIONS: The present study performed in a healthy population will be helpful in terms of making comparisons in studies to be conducted in various vestibular diseases. It will also be a guide for identifying pathological consequences in vestibular diseases.
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    Investigation of some machine learning algorithms in fish age classification
    (2021) Benzer, Semra; Garabaghi, Farid Hassanbaki; Benzer, Recep; Mehr, Homay Danaei; 0000-0002-5339-0554; A-5050-2014
    Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances in technology and the widespread use of artificial intelligence have revealed the use of traditional observations and techniques in the fishing industry. The aim of this study was to evaluate the effectiveness of three disesteemed machine learning algorithms (NB, J48 DT, RF) in comparison with ANNs which has been widely used in such studies in the literature. In culmination, all three algorithms outperformed ANNs and can be considered as alternatives in case of coming across noisy and non-linear datasets. Moreover, among these three algorithms J48 DT and RF showed exceptional performance where the data for specific fish age groups weren't abundant.