Rahib H. Abiyev

Clinical photographs and patient metadata are complementary sources of information for automated skin lesion classification. In this study, a multimodal framework is proposed that combines Swin Transformer Tiny (Swin-T), Hyper-Feature Fusion (HFF), and Attention-Gated Clinical Metadata Fusion (AGCMF). HFF combines four levels of hierarchical representations from the Swin Transformer into a 256-dimensional visual embedding. A multilayer perceptron encodes 20 clinical metadata variables into a 64-dimensional representation. The features are concatenated, adaptively gated and projected for six-class classification. Five-fold patient-grouped cross-validation was performed on PAD-UFES-20, which consists of 2298 clinical photographs of 1373 patients. The proposed model obtained 83.55% accuracy without test-time augmentation (TTA), 83.29% with TTA, 82.92% weighted F1, 77.29% macro F1, 75.75% balanced accuracy, 93.12% macro-ROC-AUC, 94.59% weighted ROC-AUC, and Cohen’s kappa of 0.7702. In the ablation experiments, HFF increased the accuracy of the Swin-T model from 78.64% to 80.91%. Clinical feature concatenation increased it to 81.76%, and the complete HFF + AGCMF framework achieved 83.29%. These results indicate that hierarchical visual fusion and adaptive multimodal integration provide complementary benefits. The proposed framework achieved 3.89 percentage points higher accuracy than the image-plus-metadata baseline under the same TTA evaluation setting. A biopsy-exclusion sensitivity analysis also revealed that the performance of the model was only slightly affected by the removal of the diagnostic-verification variable, with the 19-feature model maintaining 82.91% accuracy, 82.53% weighted F1, and 94.21% weighted ROC-AUC. The most difficult category was still squamous cell carcinoma with 41.67% recall. The framework provided effective multimodal representation and improved class discrimination. However, independent external validation is required to assess its clinical generalizability.

Alzheimer’s Disease (AD) necessitates high-resolution transcriptomic biomarkers for early detection, yet current computational methods are hampered by high-dimensional search space and publication bias regarding imbalanced datasets. We propose the Hybrid Filter–Wrapper Nomadic People Optimizer, a three-stage pipeline integrating a tri-criterion filter, an enhanced NPO wrapper with adaptive Lévy-scale anti-stagnation mechanism, and a five-member soft-voting ensemble. The system was evaluated using a dual-paradigm protocol; Scenario A (balance brain tissue; GEO dataset GSE 33000, GSE 132903, GSE122063) and Scenario B (imbalanced peripheral blood: GSE 63060 + GSE 636061). In scenario A, HFW-NPO outperformed 13 published methods, achieving balanced accuracy of 85.28%, 87.16%, and 96.67% while identifying compact panels of 29–32 probes per fold (observed range: 24–38). Scenario B, evaluated on a merged 478-samples peripheral blood cohort (GSE63060 + GSE 636061 imbalanced 1.48:1) with z-score batch harmonization and RSKF (5 × 10) cross-validation, achieved a balanced accuracy of 59.53% and MCI Recall of 63.50 ± 14.02%, providing the first reproducible baseline for this clinically challenging task, while acknowledging that 59.53% balanced accuracy does not yet reach clinically actionable levels. By providing transparent reporting across both balanced and severely imbalanced datasets, this study establishes a state-of-the-art, reproducible framework for AD biomarker discovery and provides a critical baseline for the challenging task of transcriptomic-based classification in peripheral blood samples. Result is currently scoped to Illumina HumanHT-12 microarray data, and cross-platform validation on RNA-seq cohorts is identified as a priority future extension.

This paper proposes the integration of the phase-field method (PFM) with deep neural networks (DNNs) for UAV navigation in smart city environments. Using the proposed approach, simulations of an intelligent navigation and obstacle avoidance framework for drones in complex urban environments have been presented. Within the unified PFM-DNN model, phase-field modeling provides a continuous spatial representation, allowing for highly accurate characterization of boundaries between free space and obstacles. In parallel, the deep neural network component offers semantic perception and intelligent classification of environmental features. The proposed model was tested using the 3DCity dataset, which comprises 50,000 urban scenes under diverse environmental conditions, including fog, low light, and motion blur. The results demonstrated that the proposed system achieves high performance in classification and segmentation tasks, outperforming modern models such as DeepLabV3+, Mask R-CNN, and HRNet, while exhibiting high robustness to sensor noise and partial obstructions. The framework was evaluated within a simulated environment, and no real-world UAV drone tests were performed. This framework proves its effectiveness as a promising solution for intelligent drone navigation in future cities thanks to its ability to adapt and respond in dynamic environments.