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  <doc>
    <id>1708</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>12</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation</title>
    <abstract language="eng">The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.</abstract>
    <parentTitle language="eng">Electronics</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-17088</identifier>
    <enrichment key="opus.import.data">@Articleelectronics12051202, AUTHOR = Alekseeva, Victoria and Nechyporenko, Alina and Frohme, Marcus and Gargin, Vitaliy and Meniailov, Ievgen and Chumachenko, Dmytro, TITLE = Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation, JOURNAL = Electronics, VOLUME = 12, YEAR = 2023, NUMBER = 5, URL = https://www.mdpi.com/2079-9292/12/5/1202, ISSN = 2079-9292, DOI = 10.3390/electronics12051202</enrichment>
    <enrichment key="opus.import.dataHash">md5:dde9c0d59429ab6301e0153e5588bd24</enrichment>
    <enrichment key="opus.import.date">2023-03-07T11:24:30+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phplPbNMC</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">64071eee430e30.47700591</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/electronics12051202</enrichment>
    <enrichment key="SourceTitle">Alekseeva, V.; Nechyporenko, A.; Frohme, M.; Gargin, V.; Meniailov, I.; Chumachenko, D. Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation. Electronics 2023, 12, 1202. https://doi.org/10.3390/electronics12051202</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Victoriia Alekseeva</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Vitaliy Gargin</author>
    <author>Ievgen Meniailov</author>
    <author>Dmytro Chumachenko</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CT data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>U-Net</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>decision support system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>chronic odontogenic rhinosinusitis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1708/electronics-12-01202.pdf</file>
  </doc>
  <doc>
    <id>1980</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>24</issue>
    <volume>14</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors</title>
    <abstract language="eng">This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19803</identifier>
    <enrichment key="opus.import.date">2025-01-02T07:01:24+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/app142411997</enrichment>
    <enrichment key="SourceTitle">Nechyporenko, A.; Frohme, M.; Strelchuk, Y.; Omelchenko, V.; Gargin, V.; Ishchenko, L.; Alekseeva, V. Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors. Appl. Sci. 2024, 14, 11997. https://doi.org/10.3390/app142411997</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Yaroslav Strelchuk</author>
    <author>Vladyslav Omelchenko</author>
    <author>Vitaliy Gargin</author>
    <author>Liudmyla Ishchenko</author>
    <author>Victoriia Alekseeva</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>galvanic skin response</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>photoplethysmography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stress</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1980/applsci-14-11997.pdf</file>
  </doc>
  <doc>
    <id>1961</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>238</pageFirst>
    <pageLast>244</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3777</issue>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation of Bone Radiodensity in Smokers</title>
    <abstract language="eng">One of the most prevalent harmful habits that affect people daily is smoking. The aim of our study was to investigate changes in the bone density of the human paranasal sinuses under the influence of smoking using uncertainty calculation.&#13;
Material and Methods. The study involved 150 male and female individuals aged 20-40 years, which helps to exclude other factors that could influence the reduction in bone radiodensity (e.g., menopause in women, age-related changes, etc.). The participants were divided into three groups based on the duration of their smoking habits.&#13;
Results. Smoking has almost no effect on the maximum bone density. All groups show nearly identical values. In the first group, consisting of individuals who smoked for up to 5 years, the maximum density is 75.22 ± 37.61 Hu. In the second group, those who smoked for up to 10 years have a density of 84.22 ± 42.11 Hu, while in the third control group, the density is 74.29 ± 37.15 Hu. On the other hand, smoking has a greater impact on the minimum radiological bone density of the upper wall of the maxillary sinus. The lowest values are found in the group of individuals who smoked for 10 or more years, with a minimum density of 23.86 ± 11.93 Hu. For those who smoked for up to 5 years, the radiological bone density was 52.65 ± 26.32 Hu. In the control group, the density values were slightly higher than those of individuals who smoked for up to 5 years, with the highest values being 58.12 ± 29.06 Hu.&#13;
Conclusion. The study investigated changes in the bone density of the human paranasal sinuses under the influence of smoking, utilizing uncertainty calculation. It was found that the most sensitive indicator to nicotine exposure is the minimum radiological density, which can significantly increase the risk of developing complications. Specifically, the lowest radiological density was observed in the group of individuals who smoked for 10 years or more, with a value of 23.86 ± 11.93 Hu.</abstract>
    <parentTitle language="eng">Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024</parentTitle>
    <identifier type="url">https://ceur-ws.org/Vol-3777/short8.pdf</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19616</identifier>
    <enrichment key="opus.import.data">@inproceedingsNechyporenko2024, abstract = "One of the most prevalent harmful habits that affect people daily is smoking. The aim of our study was to investigate changes in the bone density of the human paranasal sinuses under the influence of smoking using uncertainty calculation.", address = "Aachen", author = "Nechyporenko, Alina and Reshetnik, Viktor and Frohme, Marcus and Alekseeva, Victoriia and Lupyr, Andrii and Gargin, Vitaliy", booktitle = "Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024", url = "https://ceur-ws.org/Vol-3777/short8.pdf", editor = "Chumachenko, Dmytro and Golland, Polina and Yakovlev, Sergiy", keywords = "bone radiodensity, computer tomography, paranasal sinuses, uncertainty", language = "eng", pages = "238–244", publisher = "CEUR-WS", title = "Evaluation of Bone Radiodensity in Smokers", year = "2024", note = "ProfIT AI 2024 : 4th International Workshop of IT-professionals on Artificial Intelligence 2024", crossref = "Chumachenko2024"</enrichment>
    <enrichment key="opus.import.dataHash">md5:de256f6a476c9b5dd3da9a9fcfd30ae7</enrichment>
    <enrichment key="opus.import.date">2024-11-08T08:45:52+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpOMHZmh</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">672dcfc04c1581.99350086</enrichment>
    <enrichment key="SourceTitle">Nechyporenko, A., Reshetnik, V., Frohme, M., Alekseeva, V., Lupyr, A., &amp; Gargin, V. (2024). Evaluation of Bone Radiodensity in Smokers. In D. Chumachenko, P. Golland, &amp; S. Yakovlev, Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024 (pp. 238–244). Aachen: CEUR-WS. Retrieved from https://ceur-ws.org/Vol-3777/short8.pdf</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alina Nechyporenko</author>
    <author>Viktor Reshetnik</author>
    <author>Marcus Frohme</author>
    <author>Victoriia Alekseeva</author>
    <author>Andrii Lupyr</author>
    <author>Vitaliy Gargin</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bone radiodensity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computer tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>paranasal sinus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>uncertainty</value>
    </subject>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1961/short8.pdf</file>
  </doc>
  <doc>
    <id>1989</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>196</pageFirst>
    <pageLast>202</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3892</volume>
    <type>conferenceobject</type>
    <publisherName>RWTH Aachen</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detection of stress using photoplethysmography</title>
    <abstract language="eng">Stress is an important factor affecting human health, and its timely detection can significantly improve quality of life. This study addresses the current issue of identifying stress states using photoplethysmography signals obtained from the wearable Shimmer 3 sensor. The purpose of the study was to establish an effective approach for stress detection, specifically through the analysis of heart rate variability changes during different states: rest and stress. A distinctive feature of the research is the use of the air raid siren sound as a stress factor. The research methodology includes the collection and analysis of biosignals, allowing for the assessment of cardiovascular system functioning under the impact of stress factors. The results of the study demonstrated significant differences in heart rhythm indicators depending on the person's state, highlighting the potential of these metrics as a tool for health monitoring and stress detection. The paper proposes a new approach to using photoplethysmography for assessing stress responses, which may contribute to the development of personalized stress management methods.</abstract>
    <parentTitle language="eng">Proceedings of the 7th International Conference on Informatics &amp; Data-Driven Medicine (IDDM 2024)</parentTitle>
    <identifier type="url">https://ceur-ws.org/Vol-3892/short3.pdf</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19896</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:28:49+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="SourceTitle">Strelchuk, Y., Nechyporenko, A., Frohme, M., Gargin, V., Lupyr, A., &amp; Alekseeva, V. (2024). Detection of stress using photoplethysmography. In N. Shakhovska, J. Jiao, I. Izonin, &amp; S. Chretien, Proceedings of the 7th International Conference on Informatics &amp; Data-Driven Medicine (IDDM 2024) (pp. 196–202). Aachen: CEUR-WS. Retrieved from https://ceur-ws.org/Vol-3892/short3.pdf</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Yaroslav Strelchuk</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Vitaliy Gargin</author>
    <author>Andrii Lupyr</author>
    <author>Victoriia Alekseeva</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>air raid signal</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cardiovascular system health</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>early stress detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heart rate variability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>personalized stress management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>photoplethysmography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stress</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable sensors</value>
    </subject>
    <collection role="ddc" number="613">Persönliche Gesundheit und Sicherheit</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1989/short3.pdf</file>
  </doc>
  <doc>
    <id>1856</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>167</pageFirst>
    <pageLast>173</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3609</volume>
    <type>conferenceobject</type>
    <publisherName>CEUR-WS.org</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning</title>
    <abstract language="eng">The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70%.</abstract>
    <parentTitle language="eng">Proceedings of the 6th International Conference on Informatics &amp; Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023</parentTitle>
    <identifier type="urn">urn:nbn:de:0074-3609-1</identifier>
    <identifier type="url">https://ceur-ws.org/Vol-3609/short2.pdf</identifier>
    <enrichment key="opus.import.date">2024-02-20T10:17:50+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="SourceTitle">Nechyporenko, A., Frohme, M., Omelchenko, V., Alekseeva, V., Lupyr, A., &amp; Gargin, V. (2023). Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning. (N. Shakhovska, M. Kovác, I. Izonin, &amp; S. Chrétien), Proceedings of the 6th International Conference on Informatics &amp; Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023. CEUR-WS.org. Retrieved from https://ceur-ws.org/Vol-3609/short2.pdf</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Vladyslav Omelchenko</author>
    <author>Victoriia Alekseeva</author>
    <author>Andrii Lupyr</author>
    <author>Vitaliy Gargin</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>identification of personality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multislice computed tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1856/short2.pdf</file>
  </doc>
</export-example>
