In recent decades, skin cancer has emerged as a significant global health concern, demanding timely detection and effective therapeutic interventions. Automated image classification via computational algorithms holds substantial promise in significantly improving the efficacy of clinical diagnoses. This study is committed to mitigating the challenge of diagnostic accuracy in the classification of multiclass skin lesions. This endeavor is inherently formidable owing to the resemblances among various lesions and the constraints associated with extracting precise global and local image features within diverse dimensional spaces using conventional convolutional neural network methodologies. Consequently, this study introduces the SkinSwinViT methodology for skin lesion classification, a pioneering model grounded in the Swin Transformer framework featuring a global attention mechanism. Leveraging the inherent cross-window attention mechanism within the Swin Transformer architecture, the model adeptly captures local features and interdependencies within skin lesion images while additionally incorporating a global self-attention mechanism to discern overarching features and contextual information effectively. The evaluation of the model’s performance involved the ISIC2018 challenge dataset. Furthermore, data augmentation techniques augmented training dataset size and enhanced model performance. Experimental results highlight the superiority of the SkinSwinViT method, achieving notable metrics of accuracy, recall, precision, specificity, and F1 score at 97.88%, 97.55%, 97.83%, 99.36%, and 97.79%, respectively.

With the rapid proliferation of the Internet, network security issues that threaten users have become increasingly severe, despite the widespread benefits of Internet access. Most existing intrusion detection systems (IDS) suffer from suboptimal performance due to data imbalance and feature redundancy, while also facing high computational complexity in areas such as feature selection and optimization. To address these challenges, this study proposes a novel network intrusion detection method based on an improved binary simulated annealing algorithm (IBSA) and TPE-FL-LightGBM. First, by integrating Focal Loss into the loss function of the LightGBM classifier, we introduce cost-sensitive learning, which effectively mitigates the impact of class imbalance on model performance and enhances the model’s ability to learn difficult-to-classify samples. Next, significant improvements are made to the simulated annealing algorithm, including adaptive adjustments of the initial temperature and Metropolis criterion, the incorporation of multi-neighborhood search strategies, and the integration of an S-shaped transfer function. These improvements enable the IBSA method to achieve efficient optimal feature selection with fewer iterations. Finally, the Tree-structured Parzen Estimator (TPE) algorithm is employed to optimize the structure of the FL-LightGBM classifier, further enhancing its performance. Through comprehensive visual analysis, ablation studies, and comparative experiments on the NSL-KDD and UNSW-NB15 datasets, the reliability of the proposed network intrusion detection method is validated.

Conventional waste monitoring relies heavily on manual inspection, while most detection models are trained on close-range, simplified datasets, limiting their applicability for real-world surveillance. Even with surveillance imagery, challenges such as cluttered backgrounds, scale variation, and small object sizes often lead to missed detections and reduced robustness. To address these challenges, this study introduces EcoDetect-YOLOv2, a lightweight and high-efficiency object detection model developed using the Intricate Environment Waste Exposure Detection (IEWED) dataset. Building upon the YOLOv8s architecture, EcoDetect-YOLOv2 incorporates a small object detection P2 detection layer to enhance sensitivity to small objects. The integration of an efficient multi-scale attention (EMA) mechanism prior to the P2 head further improves the model’s capacity to detect small-scale targets, while bolstering robustness against cluttered backgrounds and environmental noise, as well as generalizability across scale variations. In the feature fusion stage, a Dynamic Upsampling Module (Dysample) replaces traditional nearest-neighbor upsampling to yield higher-quality feature maps, thereby facilitating improved discrimination of overlapping and degraded waste particles. To reduce computational overhead and inference latency without sacrificing detection accuracy, Ghost Convolution (GhostConv) replaces conventional convolution layers within the neck. Based on this, a GhostResBottleneck structure is proposed, along with a novel ResGhostCSP module—designed via a one-shot aggregation strategy—to replace the original C2f module. Experiments conducted on the IEWED dataset, which features multi-object, multi-class, and highly complex real-world scenes, demonstrate that EcoDetect-YOLOv2 outperforms the baseline YOLOv8s by 1.0%, 4.6%, 4.8%, and 3.1% in precision, recall, mAP₅₀, and mAP_50:95, respectively, while reducing the parameter count by 19.3%. These results highlight the model’s effectiveness in real-time, multi-object waste detection, providing a scalable and efficient tool for automated urban and digital governance.

Assume that $(Y,\rho )$ is a nontrivial complete metric space, and that $(Y,g_{1,\infty })$ is a time-varying discrete dynamical system (T-VDDS), which is given by sequences ${\left(g_l\right)}_{l=1}^{\infty }$ of continuous selfmaps $g_l:Y\to Y$. In this paper, for a given Furstenberg family $\mathcal{G}$ and a given T-VDDS $(Y,g_{1,\infty })$, $\mathcal{G}$-scrambled pairs of points of the system $(Y,g_{1,\infty })$ (which contains the well-known scrambled pairs) are provided. Some properties of the set of $\mathcal{G}$-scrambled pairs of a given T-VDDS $(Y,g_{1,\infty })$ are studied. Moreover, the generically $\mathcal{G}$-chaotic T-VDDS and the generically strongly $\mathcal{G}$-chaotic T-VDDS are defined. A sufficient condition for a given T-VDDS to be generically strongly $\mathcal{G}$-chaotic is also presented.