This paper proposes a Diffusion Model-Optimized Neural Radiance Field (DT-NeRF) method, aimed at enhancing detail recovery and multi-view consistency in 3D scene reconstruction. By combining diffusion models with Transformers, DT-NeRF effectively restores details under sparse viewpoints and maintains high accuracy in complex geometric scenes. Experimental results demonstrate that DT-NeRF significantly outperforms traditional NeRF and other state-of-the-art methods on the Matterport3D and ShapeNet datasets, particularly in metrics such as PSNR, SSIM, Chamfer Distance, and Fidelity. Ablation experiments further confirm the critical role of the diffusion and Transformer modules in the model's p... More >

Chronic Pelvic Inflammatory Disease (CPID) poses significant challenges to women's health, necessitating advanced management strategies. This paper provides a comprehensive review of intelligent system architectures applied to PID health management, with emphasis on algorithmic design, cross-domain model adaptation, and system-level integration for diagnosis, treatment personalization, and long-term monitoring. By synthesizing Bayesian probabilistic frameworks with ensemble Machine Learning architectures and deep neural network models, we systematically evaluate AI-driven solutions for PID pathophysiology analysis, multimodal diagnostic imaging, therapeutic efficacy prediction, and patient-s... More >

Multimodal intelligent systems that integrate natural language processing with generative visual synthesis represent a frontier in intelligent information processing. This work addresses the design and evaluation of such a pipeline, using poetic content as a stress-test domain due to its high density of figurative language and abstract semantics. Building upon the PoemSum dataset, we construct a two-stage multimodal pipeline: first employing transformer-based models (BART and T5) for abstractive summarization, then leveraging Stable Diffusion for visual synthesis from the generated summaries. The summarization stage focuses on figurative interpretation that captures metaphorical and symbolic... More >

To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereb... More >

Consumer satisfaction in automotive spare parts e-commerce is shaped by multidimensional factors that are difficult to quantify through conventional survey methods. This paper presents an intelligent text mining framework that integrates multiple machine learning and natural language processing techniques to systematically extract and analyze consumer sentiment from 1,236 validated Taobao reviews. Specifically, TF-IDF (Term Frequency-Inverse Document Frequency) is employed for discriminative keyword extraction, semantic network analysis is applied to model inter-word relational structures, and LDA (Latent Dirichlet Allocation) topic modeling is used to discover latent thematic patterns, ide... More >