Fall detection (FD) systems are an important part of healthcare monitoring, especially for elderly populations, where quick intervention can prevent serious injuries. This paper introduces an optimized YOLOV5-based framework that combines dual attention mechanisms for improved FD in real-time edge deployment situations. The proposed design integrates the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) blocks within the YOLOv5 backbone, along with an improved Focus module that uses slice-based feature extraction. These enhancements allow the model to effectively capture both spatial and channel-wise dependencies, which are essential for distinguishing fall events f... More >

The proliferation of deepfake technology poses significant threats to digital media authenticity, necessitating robust intelligent detection systems to combat manipulated content. This paper presents a novel attention-based framework for deepfake detection that systematically integrates multiple complementary attention mechanisms to enhance discriminative feature learning. Our approach combines spatial attention, multi-head self-attention, and channel attention modules with a VGG-16 backbone to capture comprehensive representations across different feature spaces. The spatial attention mechanism focuses on discriminative facial regions, while multi-head self-attention captures long-range spa... More >

Cucumber cultivation is a vital component of Pakistan's agricultural economy and is a key vegetable in the national diet. However, crop yield and quality are severely threatened by diseases like powdery mildew and downy mildew. Early and accurate disease detection is critical for implementing targeted treatment and preventing widespread infection. This study proposes a deep learning-based framework for the automated recognition of cucumber leaf diseases. We designed and trained a custom Convolutional Neural Network (CNN) from scratch and compared its performance against powerful pre-trained transfer learning models, including VGG16 and InceptionV3. The models were evaluated on a dataset of c... More >

Falls represent a significant global health concern, particularly among older adults, with delayed detection often leading to severe medical complications. Although computer vision-based fall detection systems offer promising solutions, they usually struggle with diverse real-world scenarios and computational efficiency. This paper introduces a novel lightweight cascaded feature reweighting approach that enhances YOLOv8 for reliable fall detection through a context-aware architecture. We strategically integrate three complementary attention mechanisms: Squeeze-and-Excitation blocks in the early stages, Spatial Attention modules in the later stages, and Efficient Channel Attention in the neck... More >

Hyperspectral imaging (HSI) has become a powerful remote sensing and material analysis tool because it can capture detailed spectral information in hundreds of adjacent bands. Nevertheless, the high dimensionality and redundancy in HSI data make precise and efficient classification challenging. This paper presents an extensive comparative study of both traditional and state-of-the-art Machine Learning algorithms for HSI classification. Classical classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) are compared with state-of-the-art methods like Collaborative and Sparse Representation-based approaches, Convolutional Recurrent Neural Networks (CRNN), Classification and... More >

With the rapid development of multimodal large language models (MLLMs), structured event extraction (EE) has emerged as a critical intelligent information processing task, with increasing demand across multilingual and multimodal application scenarios. However, significant challenges remain in zero-shot multimodal and cross-language scenarios, including inconsistent cross-language outputs and the high computational cost of full-parameter fine-tuning. This study takes VideoLLaMA2 (VL2) and its improved version VL2.1 as the core models, and builds a multimodal annotated dataset covering English, Chinese, Spanish, and Russian (including 5,728 EE samples). It systematically evaluates the... More >

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 >