Browsing by Author "Kaya B."

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    Inspection of the integrity of a multi-bolt robotic arm using a scanning laser vibrometer and implementing the surface response to excitation method (SuRE)
    (Prognostics and Health Management Society, 2014) Fekrmandi H.; Rojas J.; Campbell J.; Tansel I.N.; Kaya B.; Taskin S.
    The integrity of a robotic arm was examined remotely via a scanning laser vibrometer (SLV) in order to detect loose bolts. A piezoelectric element (PZT) was bonded on the robot arm for excitation of surface guided waves. A spectrum analyzer generated surface waves within the 20-100 kHz range. The propagation of the waves was monitored with the SLV at the programmed grid points on the robot arm. The surface response to excitation (SuRE) method was used to calculate the spectrums of the signals, and compare the reference scan with the altered scan. Comparisons of before and after the scan showed that after loosening the bolt on the robot arm, spectrums of all the grid points changed to some extent, however, the largest changes occurred in the vicinity of the loosened bolts. The study shows that the SuRE method was capable of detecting the presence and location of loosening bolts using only one PZT element on a complex structure. There are two most important advantages of the SuRE method over the widely used impedance-based technique. The first advantage is the elimination of an expensive impedance analyzer; the second advantage is remotely monitoring capability as long as the surface is excited properly.
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    Microbiological attributes of tahin (sesame) helva sold under market conditions in Manisa
    (Journal of Pure and Applied Microbiology, 2014) Kaya B.; Ergonul B.
    In this study, microbiological attributes of tahin helva samples sold under market conditions in Manisa city center were determined. According to findings obtained, it was observed that Total Mesophylic Aerobic Bacteria counts of the samples were among <1.0 log cfu/g and 2.78 log cfu/g; Mold and Yeast counts of the samples were among <1.0 log cfu/g and 1.95 log cfu/g and Coliform group bacteria, Staphylococcus aureus and Escherichia coli were not found in any of the samples.
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    A Deep Learning Based Prediction Model for Diagnosing Diseases with Similar Symptoms
    (Institute of Electrical and Electronics Engineers Inc., 2021) Aygun I.; Kaya B.
    Diagnosis of diseases with similar symptoms may cause medical errors depending on the transfer of patient complaints. In this study, diseases that are similar to each other in terms of symptoms are primarily examined. In conducted experiments Diabetis Mellitus was the focus of the study and most similar disaeses to Diabetis Mellitus were determined by using statistical data and deep learning methods. Within the scope of the study, a data set containing the symptoms of patients with this disease was created. In experiments using the data of 205 patients, it was seen that the deep learning model produced the same diagnosis with physicians with a rate of over 84%. For nearly 10% of the patients used in the experiment, it was concluded that an alternative disease should also be checked. © 2021 IEEE.
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    Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Aygun I.; Kaya B.; Kaya M.
    Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, U.K., Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT and BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%. © 2013 IEEE.
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    Detection of Customer Opinions with Deep Learning Method for Metaverse Collaborating Brands
    (Institute of Electrical and Electronics Engineers Inc., 2022) Aygun I.; Kaya B.; Kaya M.
    In recent years, metaverse projects have been developed that both increase the number of users and bring a new concept to the use of the internet. With this development, collaborations are frequently established within the business world with metaverse projects that attract the attention of companies. In the study, the gains of the companies operating in the metaverse after these activities were examined. Thanks to the tweets collected before and after the companies participated in the metaverse, it was analyzed how potential users interpreted their participation in the metaverse. In this context, sentiment analysis experiments were conducted for five different clothing, sportswear, and retail companies (Adidas, Balenciaga, H&M, Nike, and Zara) serving in similar fields of activity. The BERT architecture, which is a language representation model, was used in the experiments, and it was seen that the positive shares on Twitter for companies increased greatly. After the companies transitioned to Metaverse, the biggest change in positive Twitter posts was seen in Nike, with 47%, and in second place, positive Twitter posts about Balenciaga increased by 42%. Experiments show that firms' assets in the metaverse create a positive perception within one month. © 2022 IEEE.
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    Identifying patients in need of psychological treatment with language representation models
    (Springer, 2025) Aygün İ.; Kaya B.; Kaya M.
    Early diagnosis of psychological disorders is very important for patients to regain their health. Research shows that many patients do not realize that they have a psychological disorder or apply to different departments for treatment. The detection of hidden psychological disorders in patients will both increase the quality of life of patients and reduce the traffic of patients who apply to the wrong department. This study aimed to determine whether patients who consult a physician for any reason need psychological treatment. For this purpose, the relationships, and similarities between the sentences of previous psychiatric patients and the sentences of newly arrived patients were analyzed. Domain-based trained ELECTRA language model was used to detect sentence similarities semantically. In the study, the dialogues of patients with physicians in 92 different specialties were analyzed using the MedDialog dataset, which consists of online physician applications, and the DAIC-WOZ dataset. As a result of the experiments, 90.49% success was achieved for the MedDialog dataset and 89.36% for the DAIC-WOZ dataset. With the proposed model, patients in need of psychological treatment were identified and the medical departments where psychological problems were revealed the most were determined. These divisions are Neurology, Sexology, Cardiology, and Plastic Surgery, respectively. With the findings obtained, complications caused by psychological problems and types of diseases that are precursors to psychological disorders were determined. To the best of our knowledge, this article is the first study that aims to analyze all psychological illness instead of focusing on any of the psychological problems (depression, OCD, schizophrenia, etc.) and validated by electronic health records. © The Author(s) 2024.