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 -