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  1. Article ; Online: Ukraine: thousands sign plea for scientific sanctions against Russia.

    Chumachenko, Dmytro

    Nature

    2022  Volume 603, Issue 7901, Page(s) 393

    MeSH term(s) Armed Conflicts ; Russia ; Science ; Ukraine
    Language English
    Publishing date 2022-03-09
    Publishing country England
    Document type News
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/d41586-022-00695-2
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 26: Show issues Location:
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  2. Article ; Online: Ukraine war: The humanitarian crisis in Kharkiv.

    Chumachenko, Dmytro / Chumachenko, Tetyana

    BMJ (Clinical research ed.)

    2022  Volume 376, Page(s) o796

    MeSH term(s) Altruism ; Humans ; Ukraine ; Warfare
    Language English
    Publishing date 2022-03-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.o796
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  3. Article: Impact of war on the dynamics of COVID-19 in Ukraine.

    Chumachenko, Dmytro / Chumachenko, Tetyana

    BMJ global health

    2022  Volume 7, Issue 4

    MeSH term(s) COVID-19 ; Humans ; SARS-CoV-2 ; Ukraine/epidemiology
    Language English
    Publishing date 2022-03-30
    Publishing country England
    Document type Editorial
    ISSN 2059-7908
    ISSN 2059-7908
    DOI 10.1136/bmjgh-2022-009173
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  4. Article ; Online: Impact of war on the dynamics of COVID-19 in Ukraine

    Dmytro Chumachenko / Tetyana Chumachenko

    BMJ Global Health, Vol 7, Iss

    2022  Volume 4

    Keywords Medicine (General) ; R5-920 ; Infectious and parasitic diseases ; RC109-216
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: A novel approach to fake news classification using LSTM-based deep learning models.

    Padalko, Halyna / Chomko, Vasyl / Chumachenko, Dmytro

    Frontiers in big data

    2024  Volume 6, Page(s) 1320800

    Abstract: The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection ... ...

    Abstract The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.
    Language English
    Publishing date 2024-01-08
    Publishing country Switzerland
    Document type News
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2023.1320800
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Assessing the impact of the russian war in Ukraine on COVID-19 transmission in Spain

    Dmytro Chumachenko / Tetiana Dudkina / Tetyana Chumachenko

    Радіоелектронні і комп'ютерні системи, Vol 0, Iss 1, Pp 5-

    a machine learning-based study

    2023  Volume 22

    Abstract: COVID-19 pandemic has significantly impacted the world, with millions of infections and deaths, healthcare systems overwhelmed, economies disrupted, and daily life changed. Simulation has been recognized as a valuable tool in combating the pandemic, ... ...

    Abstract COVID-19 pandemic has significantly impacted the world, with millions of infections and deaths, healthcare systems overwhelmed, economies disrupted, and daily life changed. Simulation has been recognized as a valuable tool in combating the pandemic, helping to model the spread of the virus, evaluate the impact of interventions, and inform decision-making processes. The accuracy and effectiveness of simulations depend on the quality of the underlying data, assumptions, and modeling techniques. Ongoing efforts to improve and refine simulation approaches can enhance their value in addressing future public health emergencies. The Russian full-scale military invasion of Ukraine on February 24, 2022, has created a significant humanitarian and public health crisis, with disrupted healthcare services, shortages of medical supplies, and increased demand for emergency care. The ongoing conflict has displaced millions of people, with Spain ranking 5th in the world for the number of registered refugees from Ukraine. The research aims to estimate the impact of the Russian war in Ukraine on COVID-19 transmission in Spain using means of machine learning. The research is targeted at COVID-19 epidemic process during the war. The research subjects are methods and models of epidemic process simulation based on machine learning. To achieve the study's aim, we used forecasting methods and built a model of COVID-19 epidemic process based on the XGBoost method. As a result of the experiments, the accuracy of forecasting new cases of COVID-19 in Spain for 30 days was 99.79 %, and the death cases of COVID-19 in Spain – were 99.86 %. The model was applied to data on the incidence of COVID-19 in Spain for the first 30 days of the war escalation (24.02.2022 – 25.03.2022). The calculated forecasted values showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain. Conclusions. The paper ...
    Keywords epidemic model ; epidemic process ; epidemic simulation ; simulation ; covid-19 ; xgboost ; war ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 900
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher National Aerospace University «Kharkiv Aviation Institute»
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article: On intelligent agent-based simulation of COVID-19 epidemic process in Ukraine.

    Chumachenko, Dmytro / Meniailov, Ievgen / Bazilevych, Kseniia / Chumachenko, Tetyana / Yakovlev, Sergiy

    Procedia computer science

    2022  Volume 198, Page(s) 706–711

    Abstract: COVID-19 has impacted all areas of human activity around the world. Modern society has not faced such a challenge. Affordable travel and flights between continents allowed the virus to rapidly spread to all corners of the world. An effective tool for the ...

    Abstract COVID-19 has impacted all areas of human activity around the world. Modern society has not faced such a challenge. Affordable travel and flights between continents allowed the virus to rapidly spread to all corners of the world. An effective tool for the development of anti-epidemic measures is mathematical modeling. The paper proposes a simulation model of COVID-19 propagation based on an agent-based approach. The case of the spread of the epidemic process before vaccination is considered. To verify the model, we used the data of official statistics on the incidence of COVID-19 in Ukraine, provided by the Center for Public Health of the Ministry of Health of Ukraine. The constructed model makes it possible to identify the factors influencing the development of the COVID-19 epidemic in a certain area.
    Language English
    Publishing date 2022-01-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2557358-5
    ISSN 1877-0509
    ISSN 1877-0509
    DOI 10.1016/j.procs.2021.12.310
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  8. Article ; Online: Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach

    Dmytro Chumachenko / Pavlo Piletskiy / Marya Sukhorukova / Tetyana Chumachenko

    Applied Sciences, Vol 12, Iss 4282, p

    2022  Volume 4282

    Abstract: Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective ... ...

    Abstract Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective analysis of the Lyme disease cases reported in the Kharkiv region, East Ukraine, between 2010 and 2017 was performed. To develop the neural network model of the Lyme disease epidemic process, a multilayered neural network was used, and the backpropagation algorithm or the generalized delta rule was used for its learning. The adequacy of the constructed forecast was tested on real statistical data on the incidence of Lyme disease. The learning of the model took 22.14 s, and the mean absolute percentage error is 3.79%. A software package for prediction of the Lyme disease incidence on the basis of machine learning has been developed. Results of the simulation have shown an unstable epidemiological situation of Lyme disease, which requires preventive measures at both the population level and individual protection. Forecasting is of particular importance in the conditions of hostilities that are currently taking place in Ukraine, including endemic territories.
    Keywords epidemic model ; Ixodes tick-borne borreliosis ; Lyme disease ; epidemic process simulation ; machine learning ; artificial intelligence ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Ensemble machine learning approaches for fake news classification

    Halyna Padalko / Vasyl Chomko / Sergiy Yakovlev / Dmytro Chumachenko

    Радіоелектронні і комп'ютерні системи, Vol 0, Iss 4, Pp 5-

    2023  Volume 19

    Abstract: In today’s interconnected digital landscape, the proliferation of fake news has become a significant challenge, with far-reaching implications for individuals, institutions, and societies. The rapid spread of misleading information undermines the ... ...

    Abstract In today’s interconnected digital landscape, the proliferation of fake news has become a significant challenge, with far-reaching implications for individuals, institutions, and societies. The rapid spread of misleading information undermines the credibility of genuine news outlets and threatens informed decision-making, public trust, and democratic processes. Recognizing the profound relevance and urgency of addressing this issue, this research embarked on a mission to harness the power of machine learning to combat fake news menace. This study develops an ensemble machine learning model for fake news classification. The research is targeted at spreading fake news. The research subjects are machine learning methods for misinformation classification. Methods: we employed three state-of-the-art algorithms: LightGBM, XGBoost, and Balanced Random Forest (BRF). Each model was meticulously trained on a comprehensive dataset curated to encompass a diverse range of news articles, ensuring a broad representation of linguistic patterns and styles. A distinctive feature of the proposed approach is the emphasis on token importance. By leveraging specific tokens that exhibited a high degree of influence on classification outcomes, we enhanced the precision and reliability of the developed models. The empirical results were both promising and illuminating. The LightGBM model emerged as the top performer among the three, registering an impressive F1-score of 97.74% and an accuracy rate of 97.64%. Notably, all three of the proposed models consistently outperformed several existing models previously documented in academic literature. This comparative analysis underscores the efficacy and superiority of the proposed ensemble approach. In conclusion, this study contributes a robust, innovative, and scalable solution to the pressing challenge of fake news detection. By harnessing the capabilities of advanced machine learning techniques, the research findings pave the way for enhancing the integrity and veracity of information in ...
    Keywords fake news ; classification ; misinformation ; disinformation ; balanced random forest ; xgboost ; lightgbm ; welfake ; machine learning ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher National Aerospace University «Kharkiv Aviation Institute»
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Impact of war on COVID-19 pandemic in Ukraine

    Dmytro Chumachenko / Pavlo Pyrohov / Ievgen Meniailov / Tetyana Chumachenko

    Радіоелектронні і комп'ютерні системи, Vol 0, Iss 2, Pp 6-

    the simulation study

    2022  Volume 23

    Abstract: The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of ... ...

    Abstract The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of predicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered statistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic process model in Ukraine based on the polynomial regression method. The ...
    Keywords epidemic model ; epidemic process ; epidemic simulation ; simulation ; covid-19 ; polynomial regression ; war ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 612
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher National Aerospace University «Kharkiv Aviation Institute»
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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