@article{BilousMalkoFrohmeetal.2024, author = {Bilous, Nataliya and Malko, Vladyslav and Frohme, Marcus and Nechyporenko, Alina}, title = {Comparison of CNN-Based Architectures for Detection of Different Object Classes}, series = {AI}, volume = {5}, journal = {AI}, number = {4}, publisher = {MDPI}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19634}, pages = {2300 -- 2320}, year = {2024}, abstract = {(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.}, language = {en} } @article{PrzymusRykaczewskiMartinSeguraetal.2025, author = {Przymus, Piotr and Rykaczewski, Krzysztof and Mart{\´i}n-Segura, Adri{\´a}n and Truu, Jaak and De Santa Pau, Enrique Carrillo and Kolev, Mikhail and Naskinova, Irina and Gruca, Aleksandra and Sampri, Alexia and Frohme, Marcus and Nechyporenko, Alina}, title = {Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models}, series = {Frontiers in Microbiology}, volume = {15}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19889}, year = {2025}, abstract = {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.}, language = {en} } @article{AlekseevaNechyporenkoFrohmeetal.2023, author = {Alekseeva, Victoriia 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}, series = {Electronics}, volume = {12}, journal = {Electronics}, number = {5}, publisher = {MDPI}, issn = {2079-9292}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17088}, year = {2023}, abstract = {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.}, language = {en} } @article{ChumachenkoButkevychLodeetal.2022, author = {Chumachenko, Dmytro and Butkevych, Mykola and Lode, Daniel and Frohme, Marcus and Schmailzl, Kurt J. G. and Nechyporenko, Alina}, title = {Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {18}, publisher = {MDPI}, issn = {1424-8220}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-16521}, year = {2022}, abstract = {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.}, language = {en} } @article{PfeilNechyporenkoFrohmeetal.2022, author = {Pfeil, Juliane and Nechyporenko, Alina and Frohme, Marcus and Hufert, Frank T. and Schulze, Katja}, title = {Examination of blood samples using deep learning and mobile microscopy}, series = {BMC Bioinformatics}, volume = {23}, journal = {BMC Bioinformatics}, publisher = {BioMed Central}, issn = {1471-2105}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-15802}, year = {2022}, abstract = {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.}, language = {en} } @article{D'EliaTruuLahtietal.2023, author = {D'Elia, Domenica and Truu, Jaak and Lahti, Leo and Berland, Magali and Papoutsoglou, Georgios and Ceci, Michelangelo and Zomer, Aldert and Lopes, Marta B. and Ibrahimi, Eliana and Gruca, Aleksandra and Nechyporenko, Alina and Frohme, Marcus and Klammsteiner, Thomas and Carrillo de Santa Pau, Enrique and Marcos-Zambrano, Laura Judith and Hron, Karel and Pio, Gianvito and Simeon, Andrea and Suharoschi, Ramona and Moreno-Indias, Isabel and Temko, Andriy and Nedyalkova, Miroslava and Apostol, Elena-Simona and Truică, Ciprian-Octavian and Shigdel, Rajesh and Telalović, Jasminka Hasić and Bongcam-Rudloff, Erik and Przymus, Piotr and Jordamović, Naida Babić and Falquet, Laurent and Tarazona, Sonia and Sampri, Alexia and Isola, Gaetano and P{\´e}rez-Serrano, David and Trajkovik, Vladimir and Klucar, Lubos and Loncar-Turukalo, Tatjana and Havulinna, Aki S. and Jansen, Christian and Bertelsen, Randi J. and Claesson, Marcus Joakim}, title = {Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18004}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{AlekseevaReshetnikFrohmeetal.2023, author = {Alekseeva, Victoriia and Reshetnik, Viktor and Frohme, Marcus and Kachailo, Irina and Murizyna, Irina and Nechyporenko, Alina}, title = {Investigation of the Impact of Insulin Resistance on the Bone Density of the Upper Wall of the Maxillary Sinus}, series = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, volume = {3641}, booktitle = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, editor = {Chumachenko, Dmytro and Kaur, Jasleen and Yakovlev, Sergiy and Morita, Plinio P.}, publisher = {CEUR-WS.org}, url = {http://nbn-resolving.de/urn:nbn:de:0074-3641-3}, pages = {216 -- 223}, year = {2023}, abstract = {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.}, language = {en} } @article{GavrylenkoPoltoratskyiNechyporenko2024, author = {Gavrylenko, Svitlana and Poltoratskyi, Vadym and Nechyporenko, Alina}, title = {Intrusion Detection Model Based on Improved Transformer}, series = {Advanced Information Systems}, volume = {8}, journal = {Advanced Information Systems}, number = {1}, publisher = {National Technical University Kharkiv Polytechnic Institute}, issn = {2522-9052}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18750}, pages = {94 -- 99}, year = {2024}, abstract = {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.}, language = {en} } @article{NechyporenkoFrohmeStrelchuketal.2024, author = {Nechyporenko, Alina and Frohme, Marcus and Strelchuk, Yaroslav and Omelchenko, Vladyslav and Gargin, Vitaliy and Ishchenko, Liudmyla and Alekseeva, Victoriia}, title = {Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors}, series = {Applied Sciences}, volume = {14}, journal = {Applied Sciences}, number = {24}, publisher = {MDPI}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19803}, year = {2024}, abstract = {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.}, language = {en} } @article{NechyporenkoNazaryanSemkoetal.2021, author = {Nechyporenko, Alina and Nazaryan, Rosana and Semko, Galyna and Lupyr, Andrii and Yurevych, Nadiia and Fomenko, Yuliia and Kostiukov, Eduard and Alekseeva, Victoriia}, title = {Application of spiral computed tomography for determination of the minimal bone density variability of the maxillary sinus walls in chronic odontogenic and rhinogenic sinusitis}, series = {Ukrainian Journal of Radiology and Oncology}, volume = {29}, journal = {Ukrainian Journal of Radiology and Oncology}, number = {4}, issn = {2708-7166}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19121}, pages = {65 -- 75}, year = {2021}, abstract = {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.}, language = {en} }