1. Intelligent recognition of ethnic costumes using YOLOv11 : a deep learning framework for cultural heritage preservationYunwu He, Lan Thi Nguyen, Wirapong Chansanam, 2026, izvirni znanstveni članek Povzetek: The rapid digitization of cultural heritage creates new opportunities to preserve the visual and symbolic richness of ethnic traditions. However, accurate recognition of ethnic costumes remains challenging due to complex textures, overlapping patterns, and high inter-group similarity. This study proposes an intelligent recognition framework based on the YOLOv11 architecture for the digital preservation of multi-ethnic attire in Lijiang, China. By integrating a C2PSA spatial attention mechanism with multi-scale feature fusion, the model enhances discrimination of fine- -grained textile structures under complex visual conditions. A large-scale dataset containing 4,974 images from 55 ethnic branches was constructed for evaluation. Experimental results demonstrate that the proposed method achieves an mAP@0.5 of 98.416% and a recall of 95.923%, significantly outperforming YOLOv5 and YOLOv4 (p < 0.001). With only 5.8 million parameters and 16.2 GFLOPs, the model enables efficient real- -time deployment, contributing a robust AI-driven solution for cultural heritage informatics.
Ključne besede: intangible cultural heritage, deep learning, YOLOv11, ethnic costume recognition, spatial attention mechanism
Objavljeno v DiRROS: 21.07.2026; Ogledov: 235; Prenosov: 110
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3. Peak amplitude of the normalized power spectrum of the electromyogram of the uterus in the low frequency band is an effective predictor of premature birthŽiga Pirnar, Franc Jager, Ksenija Geršak, 2024, izvirni znanstveni članek Ključne besede: preterm birth, pregnancy, electromyogram of the uterus, biomarkers, signal processing, power spectrum, deep learning
Objavljeno v DiRROS: 22.06.2026; Ogledov: 214; Prenosov: 119
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4. Coati optimized hybrid neural network for efficient network slicing in 5 generation networkAyya Dhurai Suceelal Sindhu, Chellappan Agees Kumar, 2025, izvirni znanstveni članek Povzetek: Network slicing (NS) divides the physical network into many logical networks in order to support the variety of new applications with higher performance and flexibility needs. As a result of these applications, a massive amount of data has been generated with a huge number of mobile phones. Due to this, NS performance has been greatly impacted and extreme challenges have been created. To efficiently handle the challenges, this paper proposes a novel Optimal Network slice Classification Using Deep learning (ONE-CLOUD) technique, which integrates the Coati Optimization Algorithm (COA), GhostNet, and Gated Dilated Convolutional Neural Network (CNN). COA optimizes features such as user device type, packet loss ratio, and delay rate, employing GhostNet model, and Gated Dilated CNN for network slice classification. The proposed method classifies network slices into enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). The effectiveness of the suggested approach has been evaluated using the 5G-SliciNdd dataset, utilizing evaluation criteria like accuracy, precision, recall, sensitivity, specificity, throughput, and reduced latency. The overall accuracy of the proposed method is 5.78%, 2.78% and 4.70% higher than the existing DQN-E2E, DRL, and AAA techniques respectively.
Ključne besede: network slicing, deep learning, GhostNet, gated dilated, CNN, Coati optimization
Objavljeno v DiRROS: 18.06.2026; Ogledov: 219; Prenosov: 237
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6. Training and test datasets, pretrained weights and predictions for HIDRA3 : version v1Marko Rus, Hrvoje Mihanović, Matjaž Ličer, Matej Kristan, 2024, zaključena znanstvena zbirka raziskovalnih podatkov Povzetek: HIDRA3 is a state-of-the-art deep neural model for multi-point sea-level prediction based on past sea level observations and future tidal and geophysical forecasts. Published data contain HIDRA3 pretrained weights, predictions for all 50 ensembles, geophysical training and evaluation data and SSH observations from Koper (Slovenia). The structure of the data is described in README.md.
Ključne besede: sea level modeling, deep learning, storm surges
Objavljeno v DiRROS: 13.04.2026; Ogledov: 233; Prenosov: 273
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7. Machine learning and deep learning in diabetology : revolutionizing diabetes careSalvatore Corrao, Miodrag Janić, Viviana Maggio, Manfredi Rizzo, 2025, drugi znanstveni članki Ključne besede: machine learning, deep learning, artificial intelligence, diabetes management, challenges
Objavljeno v DiRROS: 09.03.2026; Ogledov: 420; Prenosov: 255
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8. Dataset for "Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetles"Valerio Caruso, Davide Rassati, Pierfilippo Cerretti, Gianfranco Curletti, Maarten De Groot, Eva Groznik, Jerzy M. Gutowski, Christian Pylatiuk, Alain Roques, Aurélien Sallé, 2025, zaključena znanstvena zbirka raziskovalnih podatkov Ključne besede: Agrilus anxius, Agrilus planipennis, bronze birch borer, deep learning, early-detection, emerald ash b4orer, Entomoscope
Objavljeno v DiRROS: 27.02.2026; Ogledov: 490; Prenosov: 389
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9. Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetlesValerio Caruso, Hossein Shirali, Christophe Bouget, Pierfilippo Cerretti, Gianfranco Curletti, Maarten De Groot, Eva Groznik, Jerzy M. Gutowski, Christian Pylatiuk, Radosław Plewa, 2026, izvirni znanstveni članek Povzetek: The genus Agrilus includes two species, Agrilus planipennis and A. anxius, that are of particular phytosanitary concern and that are regulated by the European Union legislation. This implies that phytosanitary agencies of all EU countries are obliged to establish specific surveillance programmes to verify the absence of these species from their territory. These activities commonly consist of the use of green-coloured traps, which are, however, attractive not only for A. planipennis and A. anxius, but also for a wide range of other Agrilus species. For this reason, much time and expertise is required to sort and identify specimens to species, impeding an efficient rapid response. In this study, we tested the efficacy of the Entomoscope, a low-cost, open-source photomicroscope that uses high-resolution digital imaging and allows a pre-trained Convolutional Neural Networks (CNN) model to accurately detect, image and classify insect specimens, for automatic identification of 13 Agrilus species, including A. planipennis and A. anxius. We benchmarked models from three different CNN architectures and selected YOLOv8l as the most robust performer; this model achieved a Top-1 accuracy of 90.2% on a “real-world” test set (i.e. a dataset simulating real surveillance conditions). For most species, including A. planipennis and A. anxius, either no errors or only a few errors were made, whereas for a few native species, misidentifications were more common. These results provided proof of concept for an AI-driven surveillance system that can strongly aid in surveillance activities of Agrilus species.
Ključne besede: Agrilus anxius, Agrilus planipennis, bronze birch borer, deep learning, early-detection, emerald ash b4orer, Entomoscope
Objavljeno v DiRROS: 27.02.2026; Ogledov: 429; Prenosov: 196
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10. A study of the intelligent recognition of concrete-structure cracks based on YOLOv7 and C2E-NetLinbin Li, Jianfeng Li, Yong Luo, 2026, izvirni znanstveni članek Ključne besede: YOLOv7 (object detection), C2E-Net, inner-CIOU loss, concrete cracks, deep learning, computer vision
Objavljeno v DiRROS: 26.02.2026; Ogledov: 552; Prenosov: 299
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