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  1. Article ; Online: Image Captioning for the Visually Impaired and Blind: A Recipe for Low-Resource Languages.

    Arystanbekov, Batyr / Kuzdeuov, Askat / Nurgaliyev, Shakhizat / Varol, Huseyin Atakan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–4

    Abstract: Visually impaired and blind people often face a range of socioeconomic problems that can make it difficult for them to live independently and participate fully in society. Advances in machine learning pave new venues to implement assistive devices for ... ...

    Abstract Visually impaired and blind people often face a range of socioeconomic problems that can make it difficult for them to live independently and participate fully in society. Advances in machine learning pave new venues to implement assistive devices for the visually impaired and blind. In this work, we combined image captioning and text-to-speech technologies to create an assistive device for the visually impaired and blind. Our system can provide the user with descriptive auditory feedback in the Kazakh language on a scene acquired in real-time by a head-mounted camera. The image captioning model for the Kazakh language provided satisfactory results in both quantitative metrics and subjective evaluation. Finally, experiments with a visually unimpaired blindfolded participant demonstrated the feasibility of our approach.
    MeSH term(s) Humans ; Blindness ; Visually Impaired Persons ; Language ; Machine Learning ; Self-Help Devices
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10340575
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation.

    Karabay, Aknur / Kuzdeuov, Askat / Varol, Huseyin Atakan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2021  Volume 2021, Page(s) 1985–1988

    Abstract: Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and ...

    Abstract Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies. In this work, we present an open-source particle-based COVID-19 simulator with a vaccination module capable of taking into account the vaccine hesitancy of the population. To demonstrate the efficacy of the simulator, we conducted extensive simulations for the province of Lecco, Italy. The results indicate that the combination of both high vaccination rate and low hesitancy leads to faster epidemic suppression.
    MeSH term(s) COVID-19 ; COVID-19 Vaccines ; Humans ; SARS-CoV-2 ; Vaccination ; Vaccination Hesitancy
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2021-12-10
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC46164.2021.9630797
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Particle-Based COVID-19 Simulator With Contact Tracing and Testing.

    Kuzdeuov, Askat / Karabay, Aknur / Baimukashev, Daulet / Ibragimov, Bauyrzhan / Varol, Huseyin Atakan

    IEEE open journal of engineering in medicine and biology

    2021  Volume 2, Page(s) 111–117

    Abstract: Goal: ...

    Abstract Goal:
    Language English
    Publishing date 2021-03-08
    Publishing country United States
    Document type Journal Article
    ISSN 2644-1276
    ISSN (online) 2644-1276
    DOI 10.1109/OJEMB.2021.3064506
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation

    Karabay, Aknur / Kuzdeuov, Askat / Varol, Huseyin Atakan

    medRxiv

    Abstract: Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and ...

    Abstract Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies. In this work, we present an open-source particle-based COVID-19 simulator with a vaccination module capable of taking into account the vaccine hesitancy of the population. To demonstrate the efficacy of the simulator, we conducted extensive simulations for the province of Lecco, Italy. The results indicate that the combination of both high vaccination rate and low hesitancy leads to faster epidemic suppression.
    Keywords covid19
    Language English
    Publishing date 2021-09-28
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.09.26.21264153
    Database COVID19

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  5. Article ; Online: SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams.

    Abdrakhmanova, Madina / Kuzdeuov, Askat / Jarju, Sheikh / Khassanov, Yerbolat / Lewis, Michael / Varol, Huseyin Atakan

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 10

    Abstract: We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, ...

    Abstract We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (∼3.8 TB). For technical validation, we demonstrate two baseline examples. The first baseline shows classification by gender, utilizing different combinations of the three data streams in both clean and noisy environments. The second example consists of thermal-to-visual facial image translation, as an instance of domain transfer.
    Language English
    Publishing date 2021-05-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21103465
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases.

    Karabay, Aknur / Kuzdeuov, Askat / Ospanova, Shyryn / Lewis, Michael / Varol, Huseyin Atakan

    IEEE journal of biomedical and health informatics

    2021  Volume 25, Issue 12, Page(s) 4317–4327

    Abstract: In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to ... ...

    Abstract In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates. The tool can be calibrated by region of interest and for different vaccination strategies to enable locality-sensitive virus mitigation policy measures and resource allocation. Moreover, the vaccination policy can be simulated based on the prioritization of certain age groups or randomly vaccinating individuals across all age groups. The results based on the experience of the province of Lecco, Italy, indicate that the simulator can evaluate vaccination strategies in a way that incorporates local circumstances of viral propagation and demographic susceptibilities. Further, the simulator accounts for modeling the distinction between sterilizing immunization, where immunized people are no longer contagious, and effective immunization, where the individuals can transmit the virus even after getting immunized. The parametric simulation results showed that the sterilizing-age-based vaccination scenario results in the least number of deaths. Furthermore, it revealed that older people should be vaccinated first to decrease the overall mortality rate. Also, the results showed that as the vaccination rate increases, the mortality rate between the scenarios shrinks.
    MeSH term(s) Aged ; COVID-19 ; Computer Simulation ; Epidemics ; Humans ; SARS-CoV-2 ; Vaccination
    Language English
    Publishing date 2021-12-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2021.3114180
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A Particle-Based COVID-19 Simulator With Contact Tracing and Testing

    Askat Kuzdeuov / Aknur Karabay / Daulet Baimukashev / Bauyrzhan Ibragimov / Huseyin Atakan Varol

    IEEE Open Journal of Engineering in Medicine and Biology, Vol 2, Pp 111-

    2021  Volume 117

    Abstract: Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable ... ...

    Abstract Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.
    Keywords COVID-19 ; contact tracing ; epidemic simulator ; particle-based simulation ; random testing ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Medical technology ; R855-855.5
    Subject code 612
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher IEEE
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases.

    Karabay, Aknur / Kuzdeuov, Askat / Lewis, Michael / Varol, Atakan Huseyin

    medRxiv

    Abstract: Accurate modeling provides a means by which a complex problem can be examined for informed decision-making. We present a particle-based SEIR epidemic simulator as a tool to assess the impact of vaccination strategies on viral propagation and to model ... ...

    Abstract Accurate modeling provides a means by which a complex problem can be examined for informed decision-making. We present a particle-based SEIR epidemic simulator as a tool to assess the impact of vaccination strategies on viral propagation and to model both sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals as well as epidemiological testing of the general population. The simulator particles are distinguished by age, thus enabling a more accurate representation of the rates of infection and mortality in accordance with differential demographic susceptibilities and medical outcomes. The simulator can be calibrated by region of interest and variable vaccination strategies (i.e. random or prioritized by age) so as to enable locality-sensitive virus mitigation policy measures and resource allocation. The results described, based on the experience of the province of Lecco, Italy, indicate that the tool can be used to evaluate vaccination strategies in a way that incorporates local circumstances of viral propagation and demographic susceptibilities. Further, the simulator accounts for modeling the distinction between sterilizing immunization, in which immunized people are no longer contagious, and that of effective immunization, in which symptoms and mortality outcomes are diminished but individuals can still transmit the virus.
    Keywords covid19
    Language English
    Publishing date 2021-04-04
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.03.28.21254468
    Database COVID19

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  9. Article ; Online: A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 With Region-Specific Policies.

    Kuzdeuov, Askat / Baimukashev, Daulet / Karabay, Aknur / Ibragimov, Bauyrzhan / Mirzakhmetov, Almas / Nurpeiissov, Mukhamet / Lewis, Michael / Atakan Varol, Huseyin

    IEEE journal of biomedical and health informatics

    2020  Volume 24, Issue 10, Page(s) 2743–2754

    Abstract: In this work, we present an open-source stochastic epidemic simulator calibrated with extant epidemic experience of COVID-19. The simulator models a country as a network representing each node as an administrative region. The transportation connections ... ...

    Abstract In this work, we present an open-source stochastic epidemic simulator calibrated with extant epidemic experience of COVID-19. The simulator models a country as a network representing each node as an administrative region. The transportation connections between the nodes are modeled as the edges of this network. Each node runs a Susceptible-Exposed-Infected-Recovered (SEIR) model and population transfer between the nodes is considered using the transportation networks which allows modeling of the geographic spread of the disease. The simulator incorporates information ranging from population demographics and mobility data to health care resource capacity, by region, with interactive controls of system variables to allow dynamic and interactive modeling of events. The single-node simulator was validated using the thoroughly reported data from Lombardy, Italy. Then, the epidemic situation in Kazakhstan as of 31 May 2020 was accurately recreated. Afterward, we simulated a number of scenarios for Kazakhstan with different sets of policies. We also demonstrate the effects of region-based policies such as transportation limitations between administrative units and the application of different policies for different regions based on the epidemic intensity and geographic location. The results show that the simulator can be used to estimate outcomes of policy options to inform deliberations on governmental interdiction policies.
    MeSH term(s) Betacoronavirus ; COVID-19 ; Computational Biology ; Computer Simulation ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Epidemics/prevention & control ; Epidemics/statistics & numerical data ; Health Policy ; Humans ; Italy/epidemiology ; Kazakhstan/epidemiology ; Models, Biological ; Models, Statistical ; Pandemics/prevention & control ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission ; SARS-CoV-2 ; Stochastic Processes ; Transportation
    Keywords covid19
    Language English
    Publishing date 2020-06-26
    Publishing country United States
    Document type Journal Article ; Validation Study
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2020.3005160
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5483: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: Particle-Based COVID-19 Simulator with Contact Tracing and Testing

    Kuzdeuov, Askat / Karabay, Aknur / Baimukashev, Daulet / Ibragimov, Bauyrzhan / Varol, Huseyin Atakan

    medRxiv

    Abstract: Goal: COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of December 2020, there are more than 60 million cases and 1.4 million deaths. For informed decision making, reliable statistical data and capable ... ...

    Abstract Goal: COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of December 2020, there are more than 60 million cases and 1.4 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with position, velocity and epidemic status states on a 2D map and runs a SEIR epidemic model with contact tracing and testing modules. The simulator is available in GitHub under MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing and contact tracing, for epidemic mitigation and suppression.
    Keywords covid19
    Language English
    Publishing date 2020-12-08
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.12.07.20245043
    Database COVID19

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