Fire detection in smart cities requires intelligent visual recognition systems capable of distinguishing fire from visually similar phenomena while maintaining real-time performance under diverse environmental conditions. Existing deep learning approaches employ attention mechanisms that aggregate spatial information isotropically, failing to capture the inherently directional characteristics of fire and smoke patterns. This paper presents DirFireNet, a novel fire detection framework that exploits directional fire dynamics through Strip Pooling Coordinate Attention (SPCA). Unlike conventional attention mechanisms, DirFireNet explicitly models vertical flame propagation and horizontal smoke d... More >

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 >

Accurate and real-time polyp segmentation plays a vital role in the early detection of colorectal cancer. However, existing methods often rely on computationally expensive backbones, single attention mechanisms, and suboptimal feature fusion strategies, limiting their practicality in real-world scenarios. In this work, we propose a lightweight yet effective deep learning framework that strikes a balance between precision and efficiency through a carefully designed architecture. Specifically, we adopt a MobileNetV4-based hybrid backbone to extract rich multi-scale features with significantly fewer parameters than conventional backbones, making the model well-suited for resource-constrained cl... More >

This systematic review and meta-analysis assesses the transformative effect of artificial intelligence (AI) on forensic odontology, concentrating on gains in identification accuracy and workflow efficiency. Traditionally, human identification in this specialty depends on meticulous comparison of dental charts and radiographs. The integration of AI-driven technologies—including machine-learning algorithms and image-recognition networks—has begun to expedite core tasks such as bite-mark interpretation, dental-age estimation and record reconciliation, while also limiting examiner bias and clerical error. Following PRISMA guidelines to ensure methodological rigour, we searched PubMed, Scienc... More >