TY - JOUR A1 - Bilous, Nataliya A1 - Malko, Vladyslav A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Comparison of CNN-Based Architectures for Detection of Different Object Classes JF - AI N2 - (1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models. KW - EfficientDet KW - SSD KW - YOLO KW - deep learning KW - neural network KW - object detection Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19634 VL - 5 IS - 4 SP - 2300 EP - 2320 PB - MDPI ER - TY - JOUR A1 - Przymus, Piotr A1 - Rykaczewski, Krzysztof A1 - Martín-Segura, Adrián A1 - Truu, Jaak A1 - De Santa Pau, Enrique Carrillo A1 - Kolev, Mikhail A1 - Naskinova, Irina A1 - Gruca, Aleksandra A1 - Sampri, Alexia A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models JF - Frontiers in Microbiology N2 - Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward. KW - clasiffication KW - clustering KW - deep learning KW - natural language processing KW - microbiome Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19889 SN - 1664-302X VL - 15 PB - Frontiers ER - TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - 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. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER - TY - JOUR A1 - Chumachenko, Dmytro A1 - Butkevych, Mykola A1 - Lode, Daniel A1 - Frohme, Marcus A1 - Schmailzl, Kurt J. G. A1 - Nechyporenko, Alina T1 - Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data JF - Sensors N2 - Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. KW - myocardial infraction KW - heart rate variability KW - 10-second heart rate variability KW - diagnostics KW - machine learning KW - k-nearest neighbor classifier KW - radial basis function KW - decision tree KW - random forest Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16521 SN - 1424-8220 VL - 22 IS - 18 PB - MDPI ER - TY - JOUR A1 - Pfeil, Juliane A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Schulze, Katja T1 - Examination of blood samples using deep learning and mobile microscopy JF - BMC Bioinformatics N2 - Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15802 SN - 1471-2105 VL - 23 PB - BioMed Central ER - TY - JOUR A1 - D'Elia, Domenica A1 - Truu, Jaak A1 - Lahti, Leo A1 - Berland, Magali A1 - Papoutsoglou, Georgios A1 - Ceci, Michelangelo A1 - Zomer, Aldert A1 - Lopes, Marta B. A1 - Ibrahimi, Eliana A1 - Gruca, Aleksandra A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Klammsteiner, Thomas A1 - Carrillo de Santa Pau, Enrique A1 - Marcos-Zambrano, Laura Judith A1 - Hron, Karel A1 - Pio, Gianvito A1 - Simeon, Andrea A1 - Suharoschi, Ramona A1 - Moreno-Indias, Isabel A1 - Temko, Andriy A1 - Nedyalkova, Miroslava A1 - Apostol, Elena-Simona A1 - Truică, Ciprian-Octavian A1 - Shigdel, Rajesh A1 - Telalović, Jasminka Hasić A1 - Bongcam-Rudloff, Erik A1 - Przymus, Piotr A1 - Jordamović, Naida Babić A1 - Falquet, Laurent A1 - Tarazona, Sonia A1 - Sampri, Alexia A1 - Isola, Gaetano A1 - Pérez-Serrano, David A1 - Trajkovik, Vladimir A1 - Klucar, Lubos A1 - Loncar-Turukalo, Tatjana A1 - Havulinna, Aki S. A1 - Jansen, Christian A1 - Bertelsen, Randi J. A1 - Claesson, Marcus Joakim T1 - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JF - Frontiers in Microbiology N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. KW - microbiome KW - machine learning KW - artificial intelligence KW - standard KW - best practice Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18004 SN - 1664-302X VL - 14 PB - Frontiers ER - TY - CHAP A1 - Alekseeva, Victoriia A1 - Reshetnik, Viktor A1 - Frohme, Marcus A1 - Kachailo, Irina A1 - Murizyna, Irina A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Kaur, Jasleen ED - Yakovlev, Sergiy ED - Morita, Plinio P. T1 - Investigation of the Impact of Insulin Resistance on the Bone Density of the Upper Wall of the Maxillary Sinus T2 - Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023 N2 - The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications. KW - bone density KW - multispiral computer tomography KW - uncertainty KW - paranasal sinus KW - resistance to insulin Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0074-3641-3 UR - https://ceur-ws.org/Vol-3641/short1.pdf VL - 3641 SP - 216 EP - 223 PB - CEUR-WS.org ER - TY - JOUR A1 - Gavrylenko, Svitlana A1 - Poltoratskyi, Vadym A1 - Nechyporenko, Alina T1 - Intrusion Detection Model Based on Improved Transformer JF - Advanced Information Systems N2 - The object of the study is the process of identifying the state of a computer network. The subject of the study are the methods of identifying the state of computer networks. The purpose of the paper is to improve the efficacy of intrusion detection in computer networks by developing a method based on transformer models. The results obtained. The work analyzes traditional machine learning algorithms, deep learning methods and considers the advantages of using transformer models. A method for detecting intrusions in computer networks is proposed. This method differs from known approaches by utilizing the Vision Transformer for Small-size Datasets (ViTSD) deep learning algorithm. The method incorporates procedures to reduce the correlation of input data and transform data into a specific format required for model operations. The developed methods are implemented using Python and the GOOGLE COLAB cloud service with Jupyter Notebook. Conclusions. Experiments confirmed the efficiency of the proposed method. The use of the developed method based on the ViTSD algorithm and the data preprocessing procedure increases the model's accuracy to 98.7%. This makes it possible to recommend it for practical use, in order to improve the accuracy of identifying the state of a computer system. KW - data preprocessing KW - machine learning KW - neural network KW - RNN KW - SVM KW - KNN KW - transformer model KW - vision transformer Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18750 SN - 2522-9052 VL - 8 IS - 1 SP - 94 EP - 99 PB - National Technical University Kharkiv Polytechnic Institute ER - TY - JOUR A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Strelchuk, Yaroslav A1 - Omelchenko, Vladyslav A1 - Gargin, Vitaliy A1 - Ishchenko, Liudmyla A1 - Alekseeva, Victoriia T1 - Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors JF - Applied Sciences N2 - 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. KW - galvanic skin response KW - machine learning KW - photoplethysmography KW - stress Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19803 VL - 14 IS - 24 PB - MDPI ER - TY - JOUR A1 - Nechyporenko, Alina A1 - Nazaryan, Rosana A1 - Semko, Galyna A1 - Lupyr, Andrii A1 - Yurevych, Nadiia A1 - Fomenko, Yuliia A1 - Kostiukov, Eduard A1 - Alekseeva, Victoriia T1 - Application of spiral computed tomography for determination of the minimal bone density variability of the maxillary sinus walls in chronic odontogenic and rhinogenic sinusitis JF - Ukrainian Journal of Radiology and Oncology N2 - Background. There are many questions regarding the peculiarities of the course of odontogenic maxillary sinusitis, the likelihood of complications and the correct dosage of load during dental implantation. Purpose – to identify changes in minimal bone density in chronic odontogenic maxillary sinusitis and to compare it with chronic rhinogenic catarrhal maxillary sinusitis and physiological state of maxillary sinus. Materials and Methods. Our study involved 10 patients with chronic odontogenic maxillary sinusitis, 10 patients with chronic maxillary non-polyposis sinusitis of rhinogenic etiology, combined with hyperplasia of the sinus mucosa up to 1 cm. The control group comprised 10 patients with physiological condition of maxillary sinuses. In all subjects minimal bone density of superior, medial and inferior walls of maxillary sinus was analyzed. Results. The study showed that the maximum in physiological conditions is the minimal density of the upper wall of the maxillary sinus, the minimum is the lower wall. The minimal density of the lower wall was shown to undergo a statistically significant reduction in chronic catarrhal maxillary sinusitis only by 2%, the upper by 5%, the medial by 4% compared with the intact sinus, but with the odontogenic nature of maxillary sinusitis, this figure was 31% in the lower wall, 27% in the medial region. Only the density of the upper wall of the maxillary sinus remained quite stable, it decreased relative to the physiological one only by 6%. Conclusions. Minimal physiological densitometric parameters of lower, medial and upper walls were calculated. The reduction of minimal density of bone tissue in the cases of rhinogenic chronic catarrhal maxillary sinusitis. In the cases of chronic odontogenic maxillary sinusitis, maximum significant decrease of minimal density of both lower and medial walls is determined. KW - bone density KW - computed tomography KW - maxillary sinus KW - odontogenic maxillary sinusitis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19121 SN - 2708-7166 VL - 29 IS - 4 SP - 65 EP - 75 ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Reshetnik, Viktor A1 - Frohme, Marcus A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Chumachenko, Dmytro ED - Golland, Polina ED - Yakovlev, Sergiy T1 - Evaluation of Bone Radiodensity in Smokers T2 - Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024 N2 - 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. 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. 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. 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. KW - bone radiodensity KW - computer tomography KW - paranasal sinus KW - uncertainty Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19616 UR - https://ceur-ws.org/Vol-3777/short8.pdf IS - 3777 SP - 238 EP - 244 ER - TY - CHAP A1 - Reshetnik, Viktor A1 - Alekseeva, Victoriia A1 - Devos, Anastasiia A1 - Nazaryan, Rosana A1 - Gargin, Vitaliy A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Kaur, Jasleen ED - Yakovlev, Sergiy ED - Morita, Plinio P. T1 - Implementation of the Uncertainty Calculation for the Detection of Negative Effect of Smoking on the Bone Density of Paranasal Sinuses T2 - Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023 N2 - The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull. KW - bone density KW - multispiral computer tomography KW - uncertainty KW - paranasal sinus KW - smoking Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19140 UR - https://ceur-ws.org/Vol-3641/short9.pdf IS - 3641 SP - 276 EP - 283 ER - TY - CHAP A1 - Reshetnik, Viktor A1 - Muryzina, Irina A1 - Frohme, Marcus A1 - Alekseeva, Victoriia A1 - Dzyza, Alla A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Golland, Polina ED - Yakovlev, Sergiy T1 - Uncertainty Estimation Method for Determining Bone Density in Patients with Infiltrating Intraductal Carcinoma Undergoing Anti-Cancer Therapy T2 - Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024 N2 - Identification of a bone density is crucial in all branches of medicine. Assessing bone density is a labor-intensive process. Bone density is quite unstable and depends on many factors, both physiological (aging, hormonal balance) and pathological (medication use, various underlying conditions). The aim of our study is to determine bone density in patients with malignant breast tumors undergoing anti-cancer therapy. Material and Methods. The study included 50 women aged 60-70 years who were diagnosed with infiltrating intraductal carcinoma. According to established protocols, MSCT is recommended for this category of women at intervals of once every six months. The first MSCT scan was performed immediately after the diagnosis was made, before the start of treatment, and the second scan was conducted six months later. Results. The maximum radiological density was 75.8954±37.9477 Hu in the group of women who had been receiving treatment for six months, compared to 93.9388±46.9694 Hu in the group of patients who did not take the drug. Meanwhile, the minimum density showed a slight increase from 29.7295±14.8647 Hu to 38.6919±19.3460 Hu, which can be attributed to the compensatory mechanisms of the body. Conclusions. In the course of this study, bone density in patients with infiltrating intraductal carcinoma undergoing anti-cancer therapy was determined using uncertainty estimation. It was found that after six months, the first to respond to changes in density was the maximum bone density. KW - infiltrarive intraductal adenocarcinoma KW - multislice computer tomography KW - radiological bone density Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19628 UR - https://ceur-ws.org/Vol-3777/short6.pdf IS - 3777 SP - 222 EP - 228 ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Hubarenko, Yevhen A1 - Hubarenko, Maryna A1 - Kalnytska, Violeta A1 - Alekseeva, Victoriia A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%. KW - neural network KW - mask KW - microscopic image KW - epithelium KW - inflammatory change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19024 UR - https://ceur-ws.org/Vol-3302/paper6.pdf VL - 3302 SP - 108 EP - 119 PB - RWTH Aachen ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Reshetnik, Viktor A1 - Dzyza, Alla A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Kovác, Michal ED - Izonin, Ivan ED - Chrétien, Stéphane T1 - The Peculiarities of Measuring Bone Density in Males and Females Using Uncertainty Calculation T2 - Proceedings of the 6th International Conference on Informatics & Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023 N2 - The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%. KW - bone density KW - multispiral computer tomography KW - uncertainty Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19116 UR - https://ceur-ws.org/Vol-3609/short6.pdf VL - 3609 SP - 249 EP - 254 PB - RWTH Aachen ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Reshetnik, Viktor A1 - Shyian, Denys A1 - Alekseeva, Victoriia A1 - Radutny, Radiy A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Campos, Jaime ED - Melnykova, Nataliia ED - Izonin, Ivan T1 - Solutions to the 3D Model Problem of Pressure Measurement in the Area of Maxillary Sinus Anastomosis T2 - Proceedings of the 3rd International Conference on Informatics & Data-Driven Medicine N2 - The ostiomeatal complex (OMC) is a key area that determines the occurrence of inflammatory processes in the paranasal sinuses (PNSs). The aim of our work was to develop a procedure for studying the OMC components in the preoperative period that allows for identification the impact of anatomical peculiarities on change of physiological pressure in the maxillary sinus. Materials and methods: The study was carried out on the basis of the otorhinolaryngological department of Kharkiv Regional Clinical Hospital in 2019-2020. It involved 100 patients of both sexes aged 20-59 years with chronic non-polyposis maxillary sinusitis. Results: The sizes of the uncinate process, the middle turbinate and the natural anastomosis were determined using the calculation of uncertainty. Basing on the data obtained, all the patients were divided into three groups. Conclusions: Changes in the size of the natural anastomosis (both an increase and its narrowing) lead to changes in pressure in the area of the anastomosis, and a decrease in ventilation in the paranasal sinuses. SCT study with subsequent 3D modeling is an informative, accurate and effective method for assessment of OMC and PNSs condition. It allows surgeons to presume the method and volume of surgery as early as at the preoperative stage, without resorting to invasive research methods. KW - ostiomeatal comlex KW - pressure KW - 3D model Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19007 UR - https://ceur-ws.org/Vol-2753/paper20.pdf SP - 275 EP - 284 PB - RWTH Aachen ER - TY - CHAP A1 - Strelchuk, Yaroslav A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Lupyr, Andrii A1 - Alekseeva, Victoriia ED - Shakhovska, Nataliya ED - Jiao, Jianbo ED - Izonin, Ivan ED - Chretien, Stephane T1 - Detection of stress using photoplethysmography T2 - Proceedings of the 7th International Conference on Informatics & Data-Driven Medicine (IDDM 2024) N2 - 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. KW - air raid signal KW - cardiovascular system health KW - early stress detection KW - health monitoring KW - heart rate variability KW - personalized stress management KW - photoplethysmography KW - stress KW - wearable sensors Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19896 UR - https://ceur-ws.org/Vol-3892/short3.pdf VL - 3892 SP - 196 EP - 202 PB - RWTH Aachen ER - TY - CHAP A1 - Radutny, Radiy A1 - Nechyporenko, Alina A1 - Alekseeva, Victoriia A1 - Yurevych, Nadiia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Salazar, Addisson ED - Izonin, Ivan ED - Campos, Jaime T1 - Complex Automatic Evaluation of the Medical Images of the Paranasal Sinuses T2 - Proceedings of the 4th International Conference on Informatics & Data-Driven Medicine, Valencia, Spain, November 19 - 21, 2021 N2 - Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology. The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses. Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared. In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal. KW - automated measurement KW - computed tomography KW - maxillary sinus KW - bone thickness KW - bone density Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19010 UR - https://ceur-ws.org/Vol-3038/short7.pdf SP - 182 EP - 189 PB - RWTH Aachen ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Omelchenko, Vladyslav A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Kovác, Michal ED - Izonin, Ivan ED - Chrétien, Stéphane T1 - Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning T2 - Proceedings of the 6th International Conference on Informatics & Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023 N2 - 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%. KW - identification of personality KW - multislice computed tomography KW - deep learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0074-3609-1 UR - https://ceur-ws.org/Vol-3609/short2.pdf VL - 3609 SP - 167 EP - 173 PB - CEUR-WS.org ER - TY - CHAP A1 - Chumachenko, Dmytro A1 - Nechyporenko, Alina A1 - Frohme, Marcus ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content. KW - epidemic model KW - machine learning KW - polynomial regression KW - war KW - COVID-19 KW - infectious disease simulation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19074 UR - https://ceur-ws.org/Vol-3302/short4.pdf VL - 3302 SP - 78 EP - 85 PB - RWTH Aachen ER - TY - INPR A1 - Hollmann, Susanne A1 - Kremer, Andreas A1 - Baebler, Špela A1 - Trefois, Christophe A1 - Gruden, Kristina A1 - Rudnicki, Witold R. A1 - Tong, Weida A1 - Gruca, Aleksandra A1 - Bongcam-Rudloff, Erik A1 - Evelo, Chris T. A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Šafránek, David A1 - Regierer, Babette A1 - D'Elia, Domenica T1 - The need for standardisation in life science research - an approach to excellence and trust. [version 1; peer review: 3 approved] T2 - F1000Research N2 - Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation. KW - Open Data KW - Open Access KW - Open Science KW - FAIR Principles KW - standardisation KW - education KW - quality management Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-13891 SN - 2046-1402 VL - 9 ER - TY - JOUR A1 - Hollmann, Susanne A1 - Frohme, Marcus A1 - Endrullat, Christoph A1 - Kremer, Andreas A1 - D'Elia, Domenica A1 - Regierer, Babette A1 - Nechyporenko, Alina T1 - Ten simple rules on how to write a standard operating procedure JF - PLoS Computational Biology N2 - Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation. Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-13598 SN - 1553-7358 VL - 16 IS - 9 SP - e1008095 ER - TY - JOUR A1 - Marcos-Zambrano, Laura Judith A1 - López-Molina, Víctor Manuel A1 - Bakir-Gungor, Burcu A1 - Frohme, Marcus A1 - Karaduzovic-Hadziabdic, Kanita A1 - Klammsteiner, Thomas A1 - Ibrahimi, Eliana A1 - Lahti, Leo A1 - Loncar-Turukalo, Tatjana A1 - Dhamo, Xhilda A1 - Simeon, Andrea A1 - Nechyporenko, Alina A1 - Pio, Gianvito A1 - Przymus, Piotr A1 - Sampri, Alexia A1 - Trajkovik, Vladimir A1 - Lacruz-Pleguezuelos, Blanca A1 - Aasmets, Oliver A1 - Araujo, Ricardo A1 - Anagnostopoulos, Ioannis A1 - Aydemir, Önder A1 - Berland, Magali A1 - Calle, M. Luz A1 - Ceci, Michelangelo A1 - Duman, Hatice A1 - Gündoğdu, Aycan A1 - Havulinna, Aki S. A1 - Kaka Bra, Kardokh Hama Najib A1 - Kalluci, Eglantina A1 - Karav, Sercan A1 - Lode, Daniel A1 - Lopes, Marta B. A1 - May, Patrick A1 - Nap, Bram A1 - Nedyalkova, Miroslava A1 - Paciência, Inês A1 - Pasic, Lejla A1 - Pujolassos, Meritxell A1 - Shigdel, Rajesh A1 - Susín, Antonio A1 - Thiele, Ines A1 - Truică, Ciprian-Octavian A1 - Wilmes, Paul A1 - Yilmaz, Ercument A1 - Yousef, Malik A1 - Claesson, Marcus Joakim A1 - Truu, Jaak A1 - Carrillo de Santa Pau, Enrique T1 - A toolbox of machine learning software to support microbiome analysis JF - Frontiers in Microbiology N2 - The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis. KW - microbiome KW - machine learning KW - software KW - feature generation KW - feature analysis KW - data integration KW - microbial gene prediction KW - microbial metabolic modeling Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18271 UR - https://www.frontiersin.org/articles/10.3389/fmicb.2023.1250806/ SN - 1664-302X VL - 14 PB - Frontiers ER - TY - JOUR A1 - Pfeil, Juliane A1 - Siptroth, Julienne A1 - Pospisil, Heike A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Moskalenko, Olga A1 - Yateem, Murad A1 - Nechyporenko, Alina T1 - Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition JF - Big Data and Cognitive Computing N2 - Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. KW - human intestinal microbiome KW - next-generation sequencing KW - type 2 diabetes KW - deep learning KW - image classification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17184 SN - 2504-2289 VL - 7 IS - 1 PB - MDPI ER - TY - JOUR A1 - Khrutba, Viktoriia A1 - Nikitchenko, Yuliia A1 - Balykin, Ivan A1 - Nechyporenko, Alina A1 - Frohme, Marcus T1 - Academic & educational networking as a mechanism for the resilience of university cooperation in the digital era JF - European Journal of Interdisciplinary Issues N2 - The article investigates the evolution of digital educational networks as instruments of resilience and sustainability within German-Ukrainian higher education cooperation. Its primary objective is to analyze how academic networking contributes to maintaining educational continuity, fostering innovation, and strengthening institutional adaptability under crisis conditions. The study focuses on two representative initiatives, the Digital Teaching Network (DTN) and the Wildau-Kharkiv IT Bridge, which exemplify the transition from traditional digital platforms to interconnected network ecosystems. Methodologically, the research combines quantitative and qualitative approaches, including an online survey, focus group discussions, and content analysis of responses from 185 participants representing 21 universities in both countries. This mixed-method design enables a comprehensive understanding of the motivational, organizational, and social determinants of participation in educational networks. The analysis identifies four main categories of network actors, initiators, active collaborators, occasional participants, and observers, whose interaction defines the internal dynamics and resilience of such networks. The study’s findings demonstrate that networking serves as a key mechanism for sustaining academic interaction, enhancing educators’ digital competencies, and facilitating interdisciplinary collaboration. It further shows that niche educational networks, those focused on specific thematic or professional domains, play an increasingly important role in advancing specialized expertise and fostering trust-based partnerships beyond the lifespan of individual projects. The scientific novelty of the article lies in conceptualizing the shift from digital platforms as technical tools to educational networks as socio-institutional ecosystems. Practically, the results highlight the potential of network-based cooperation to support the sustainable development, modernization, and internationalization of Ukrainian higher education in the digital era. KW - digital competencies KW - digitalization of education KW - educational networks KW - international cooperation KW - networking KW - niche networks KW - university resilience Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20957 UR - https://www.eujini.org.pl/index.php/journal/article/view/35 VL - 2 IS - 2 SP - 84 EP - 97 PB - East European Association of Scientists CY - Warszaw ER -