Background: Gliomas are among the most complex and lethal primary brain tumors, necessitating precise evaluation of both anatomical subregions and molecular alterations for effective clinical management. Methods: To find a solution to the disconnected nature of current bioimage analysis pipelines, where anatomical segmentation based on MRI and molecular biomarker prediction are done as separate tasks, we use here Molecular-Genomic and Multi-Task (MGMT-Net), a one deep learning scheme that carries out the task of the multi-modal MRI data without any conversion. MGMT-Net incorporates a novel Cross-Modality Attention Fusion (CMAF) module that dynamically integrates diverse imaging sequences and pairs them with a hybrid Transformer–Convolutional Neural Network (CNN) encoder to capture both global context and local anatomical detail. This architecture supports dual-task decoders, enabling concurrent voxel-wise tumor delineation and subject-level classification of key genomic markers, including the IDH gene mutation, the 1p/19q co-deletion, and the TERT gene promoter mutation. Results: Extensive validation on the Brain Tumor Segmentation (BraTS 2024) dataset and the combined Cancer Genome Atlas/Erasmus Glioma Database (TCGA/EGD) datasets demonstrated high segmentation accuracy and robust biomarker classification performance, with strong generalizability across external institutional cohorts. Ablation studies further confirmed the importance of each architectural component in achieving overall robustness. Conclusions: MGMT-Net presents a scalable and clinically relevant solution that bridges radiological imaging and genomic insights, potentially reducing diagnostic latency and enhancing precision in neuro-oncology decision-making. By integrating spatial and genetic analysis within a single model, this work represents a significant step toward comprehensive, AI-driven glioma assessment.

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles.

This study proposes an integrated software model for automatic document classification and metadata generation based on the Dublin Core standard to address the issue of rapid and consistent management of archival documents in a digital environment. This approach combines the stages of receiving incoming documents, converting them to text using optical character recognition (OCR), image preprocessing (binarization, deskew, noise reduction), and text cleaning and vectorization (TF–IDF) into a single pipeline. In the document classification stage, the Bidirectional Encoder Representations from Transformers (BERT) model with a context-sensitive transformer architecture is used, along with classical machine learning models (Logistic Regression, Naive Bayes, Support Vector Machine) and an ensemble approach (LightGBM), to increase the accuracy by modeling the document content at a deep semantic level. Experiments were conducted on the RVL-CDIP dataset, and the OCR efficiency was evaluated using the Character Error Rate (CER) indicator, and the classification results were evaluated using the accuracy, precision, recall and F1-score metrics. The results confirmed the high stability and generalization ability of the BERT (accuracy, 95.1%; F1, 95.0%) and LightGBM (accuracy, 93.2%; F1, 93.2%) models. In the final stage, OCR, NER, and classification outputs are automatically organized into Dublin Core metadata elements (Title, Creator, Date, Description, Subject, Type, Format, Language) and exported in JSON/XML formats. This automation significantly reduces manual cataloging effort and improves indexing and retrieval efficiency in digital archival systems.

The increasing flow of documents in digital libraries, archives and electronic document management systems makes the standardization, adaptation and automation of the process of creating metadata an urgent scientific problem. Metadata directly affects the efficiency of document search, identification, semantic interpretation, long-term storage and intersystem exchange. However, while standardized description based on MARC21, a flexible approach to creating a dynamic field, and intelligent methods based on deep learning, cover these requirements separately, the issue of their full integration into a single methodological system has not been sufficiently resolved. In this study, an integrated hybrid model for describing electronic documents based on standardized, flexible, and intelligent metadata was proposed. A mixed electronic document corpus of 1500 documents was formed for evaluation. The corpus consisted of books, dissertations, scientific articles, archival documents, and heterogeneous electronic documents, with 300 samples selected from each group. Key metadata elements for each document were manually identified and used as ground truth. According to experimental results, the MARC21-based constructor achieved 96.8% structural compatibility and 95.6% metadata completeness, but the average description time was 6.8 min. The dynamic field approach achieved 93.4% structural compatibility and 94.1% metadata completeness, and reduced the description time to 4.1 min. The deep learning-based intelligent module achieved a structural matching score of 91.7%, a metadata extraction score of 93.8% F1, and reduced the processing time to 1.9 min. The proposed hybrid model achieved a structural matching score of 95.9%, a metadata F1 score of 95.1%, and an average description time of 2.3 min. The results showed that the hybrid model is a balanced solution between metadata quality, flexibility, and automation.

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning models can take out global and local features, it is still difficult to find a good balance between semantic context and fine boundary precision, especially when nuclei are overlapping or have changed shapes. In this paper, we put forward a novel deep learning model named Dual-Stream HyperFusionNet (DS-HFN), which is capable of explicitly representing the global contextual and boundary-sensitive features for the robust nuclei segmentation task by first decoupling and then fusing them. The dual-stream encoder in DS-HFN can simultaneously acquire the semantic and edge-focused features, which can be later combined with the help of the attention-driven HyperFeature Embedding Module (HFEM). Additionally, the dual-decoder concept, together with the Gradient-Aligned Loss Function, facilitates structural precision by making the segmentation gradients that are predicted consistent with the ground-truth contours. On various benchmark datasets like TNBC and MoNuSeg, DS-HFN not only achieves better results than other 30 state-of-the-art models in all evaluation metrics but also is less computationally expensive. These findings indicate that DS-HFN provides a capability for accurate nuclei segmentation, which is essential for clinical diagnosis and biomarker analysis, across a wide range of tissues in digital pathology.

Automatic extraction of genealogical information from historical archival-genealogical documents in Uzbek is an understudied problem for low-resource languages. Multi-layer NLP benchmarks are not sufficient to automatically identify individuals, family relationships, dates, place names, and archival identifiers in such texts. Also, the same people are mentioned in various forms: full name, pronoun (18.8%), initial, surname-name order, indirect expression (9.4%), and title. Existing NER and relation extraction corpora are mainly focused on high-resource languages or general domain texts and do not sufficiently cover the FAMILY_ROLE signals, historical spelling variants, and fond–opis–delos identifiers specific to Uzbek archival-genealogical texts. Proposed resource: We present the ArchiveGene Corpus, a controlled, fully synthetic, and reproducible five-layer resource consisting of 1000 Uzbek archival-genealogical-style documents, divided into 700 training, 150 validation, and 150 test documents. The corpus contains 8366 named entities, 10,625 person mentions, 2000 coreference chains, and 1000 genealogical relation triples. The dataset was generated using a deterministic template-based pipeline and a lexicon of Uzbek names, and is fully reproducible. Inter-annotator agreement values were 0.847 for NER, 0.793 for coreference, and 0.821 for RE, according to Cohen’s κ. Comparative results are presented with four baseline models (rule-based, BiLSTM-CRF, mBERT, and XLM-RoBERTa). The dataset is openly hosted on the Zenodo platform under the CC BY 4.0 license; concept DOI: 10.5281/zenodo.20670360, v1.1.1 version DOI: 10.5281/zenodo. 21429998. Scientific significance: To the best of our knowledge, ArchiveGene is among the first openly released, controlled synthetic resources for Uzbek that integrates named-entity recognition, person-mention detection, coreference resolution, genealogical relation extraction, and final tuple generation within a single annotation framework. The baseline analysis provides three main conclusions: (1) on the clean synthetic test set, the transformer models already reach 100.00 Micro-F1 for NER and 100.00 Macro-F1 for coreference-aware relation extraction, so coreference aggregation adds little on synthetic data (+2.25 for mBERT and +0.04 for XLM-RoBERTa) but its contribution is expected to grow on real archival text; (2) the rule-based and heuristic baselines lag far behind (Macro-F1 50.28 and 70.73) and fail entirely on spouse_of, showing the limits of lexical rules; and (3) a zero-shot evaluation on a real-document pilot reduces NER Micro-F1 from 100.00 to 22.17, indicating that the synthetic corpus is trivially learnable and that real-archival validation is essential.