The unpredictable nature of rip currents makes them a leading cause of coastal drowning incidents globally. Traditional methods fall short, necessitating an advanced surveillance system that can prioritize critical threats, enable autonomous decision-making with adaptive network control, and optimize resource allocation for enhanced coastal safety. Intent-based networking (IBN) plays a pivotal role in converting high-level intents into automated processes, enabling dynamic control and intelligent resource allocation in critical applications such as the Internet of Things (IoT) and unmanned aerial vehicle (UAV)-based coastal surveillance. This study proposes an artificial intelligence (AI)-powered, IBN-driven framework for coastal surveillance that leverages UAVs and IoT devices to enable real-time rip current analysis through advanced segmentation techniques. In our framework, UAVs with AI-powered IoT systems perform initial rip current analysis using lightweight deep-learning models. High-risk detections are prioritized through the closed-loop feedback mechanism of the IBN and transmitted to control rooms for validation and response, ensuring efficient resource utilization and adaptive surveillance. We expanded the rip current dataset to enhance the segmentation accuracy by incorporating additional samples from open-source platforms and applying diverse environmental conditions. We trained YOLO models and Mask R-CNN, which are suitable for real-time rip current analysis. In addition, we introduced a modified YOLOv11n-seg model, replacing the C3K2 block with C2F and optimizing the channels to reduce the parameters while maintaining accuracy. The best-performing models were tested on edge devices to evaluate the time complexity and reliability.

Timely and accurate crop damage classification (CDC) is vital for informed decision-making in the industry of precision agriculture. Traditional manual methods are slow and unreliable, whereas recent deep learning models, although accurate, are often too computationally intensive for resource-constrained environments. In this study, we present LNetCDC, a lightweight attention-based convolutional neural network tailored for CDC. The architecture integrates an EchoBlock for efficient feature extraction, combined with residual pathways enhanced by“Channelwise Refine”and“Dual Gate Attention”modules to emphasize critical spatial and channelwise features. Also, dilated convolutions are incorporated into deeper layers to capture multiscale contextual patterns. We evaluated our LNetCDC on a benchmark crop damage dataset, where it outperformed existing state-of-the-art (SOTA) models in terms of both accuracy and efficiency. Notably, it achieves around 2.3% gain in accuracy with only 0.86 million parameters compared with 1.13 million in the prior SOTA model for CDC. These results demonstrate the effectiveness and suitability of LNetCDC for real-time deployment on industrial edge devices.

Home security and surveillance systems are rapidly evolving, with Artificial Intelligence (AI) playing a transformative role in enhancing safety and threat detection. While several AI methods and datasets for intruder-related risk assessment exist, they predominantly focus on face detection and recognition, leaving a significant gap in addressing high-risk scenarios involving malicious intent, such as theft or harm. The lack of dedicated datasets for recognizing complex intruder activities, such as carrying weapons or engaging in destructive actions like kicking doors or breaking locks, limits the development of robust solutions. This work bridges this gap by introducing the Intruder Activity Recognition Dataset (IARD), a video dataset specifically designed to recognize four critical intruder activities: Armed Intruder, Door Kick, Intruder Inside and Lock Breaking. Leveraging IARD, we thoroughly benchmark various state-of-the-art methods, among which a Vision Transformer is found to achieve an impressive 93.3% accuracy in recognizing intruder actions. Our contribution highlights the potential of IARD in advancing AI-driven surveillance systems, providing a foundational dataset and benchmark for recognizing complex intruder activities.

Detecting hazardous activities is essential for ensuring safety. However, existing datasets often lack coverage of the nuanced and diverse hazards present in indoor environments, which hinders the development of a specialized model. To address this, we introduce the Real-World Hazardous Activities Dataset (RHAD), a novel and diverse video dataset specifically curated for recognizing hazardous activities in real-world indoor settings. Leveraging RHAD, we introduce HazardNet, a hybrid deep-learning architecture designed for hazardous activity recognition. HazardNet integrates local and global spatial-temporal representation modules to effectively capture complex patterns, enabling a robust understanding of the activity. We perform comprehensive evaluations by benchmarking against a range of state-of-the-art activity recognition models. Experimental results show that our proposed model performs significantly better, surpassing the latest model, VideoMamba, with a 9.2% accuracy gain. Moreover, by providing the dataset and an effective recognition model, our work lays the foundation for further research, paving the way for enhanced safety measures and preventive interventions. The dataset and code are available at https://github.com/ShehzadCS18/RHAD.

Facial emotion recognition (FER) plays a critical role in understanding human behavior, especially for individuals suffering from neurological disorders (NDs) like Parkinson’s disease (PD), Multiple Sclerosis (MS), and Stroke. Early and accurate detection of emotions is crucial for both the diagnosis of associated mood disorders and continuous monitoring. However, traditional methods often fall short in providing noninvasive, real-time solutions and lack the clinical expertise necessary to identify the specific emotion types associated with each ND category. In response, this research conducted under the ALAMEDA consortium presents an Internet of Things-based FER AI Toolkit designed to enhance early diagnosis and treatment for brain diseases. The toolkit is in line with the consortium’s clinical guidelines and provides a personalized, patient-focused solution that supports the goals of Industry 5.0 in healthcare. In line with Industry 5.0 principles, the FER AI Toolkit uses edge devices to collect real-time facial data while deep learning models running on cloud servers process this data. The recognized emotions are uploaded to the Semantic Knowledge Graph (SemKG) server. This allows healthcare professionals to make informed decisions based on real-time data. Additionally, the toolkit integrates seamlessly with key components of the ALAMEDA, including the Identity Authentication Manager (IAM) for secure access and the ALAMEDA Innovation Hub (AIH) for efficient resource management. By offering continuous and personalized healthcare insights, the FER AI Toolkit helps bridge the gap between diagnosis and patient well-being, ultimately advancing healthcare systems. Training materials and video demonstrations are available athttps://drive.google.com/drive/folders/1-iUz7FE2IrKHt5nMl3oGjsrCtk7Ps2bM?usp=sharingfor further learning.