The application of artificial intelligence (AI) in civil engineering presents a transformative approach to enhancing design quality and safety. This paper investigates the potential of the advanced LLM GPT4 Turbo vision model in detecting architectural flaws during the design phase, with a specific focus on identifying missing doors and windows. The study evaluates the model's performance through metrics such as precision, recall, and F1 score, demonstrating AI's effectiveness in accurately detecting flaws compared to human-verified data. Additionally, the research explores AI's broader capabilities, including identifying load-bearing issues, material weaknesses, and ensuring compliance with building codes. The findings highlight how AI can significantly improve design accuracy, reduce costly revisions, and support sustainable practices, ultimately revolutionizing the civil engineering field by ensuring safer, more efficient, and aesthetically optimized structures.
The integration of Unmanned Aerial Vehicles (UAVs) into existing cloud-based fire safety systems provides a transformative approach to fire detection, monitoring, and emergency response. This paper proposes an extension of the existing ALIKE system to incorporate UAV-based 3D mapping and hotspot detection capabilities. The enhanced system aims to provide real-time situational awareness, reduce response times, and improve the safety and effectiveness of firefighting operations. The implementation of UAV technology, combined with cloud computing and AI, represents a significant advancement in fire safety management. Utilizing UAVs in fire safety offers benefits such as high-resolution aerial imagery, thermal imaging for precise hotspot detection, and the ability to access hazardous areas without endangering human lives. This integration facilitates continuous monitoring and data collection, enabling predictive analytics for fire spread and risk assessment. By leveraging the strengths of UAVs, cloud computing, and AI, the proposed system aims to enhance the overall resilience and responsiveness of fire safety operations.
Pedestrian safety is a topic that has been tackled by many researchers in the past using machine learning and artificial intelligence approaches. One of the main reasons for this is the development of self-driving vehicles, which would require systems to detect pedestrians to ensure their safety. Many approaches utilize onboard camera systems which work great for most situations but there are certain shortcomings to this approach. One case is with blind right turns where the onboard cameras cannot see the entire crosswalk due to obstruction of a parked vehicle or building close to the corner. An alternative approach is to use cameras that already exist at intersections to create a warning system that detects pedestrians at each crosswalk. There are many methods of implementation, such as integration with self-driving vehicles using a vehicle-to-infrastructure (V2I) approach or using a caution light to warn drivers in advance. This paper will discuss approaches and considerations for using object detection for intersection safety using cameras at intersections. Results using different pre-trained YOLOv8 models provided by Ultralytics for three different intersection datasets from Urban Tracker: Multiple Object Tracking in Urban Mixed Traffic show that the YOLOv8 pre-trained models perform extremely well for this task.
Autism Spectrum Disorder (ASD) is a neurological and developmental condition that presents considerable social, behavioral, and communicative challenges to those diagnosed with it. Recent advances in music therapy have shown promising results in addressing these challenges. This review examines studies on music therapy for ASD from 2020 to 2024, covering both biomedical and interdisciplinary repositories. Our analysis reveals that music therapy can effectively enhance communication and social skills in people with ASD. Based on the results from 18 selected studies, the most significant improvements are achieved in social-emotional reciprocity and adaptive behaviors, contrasting with only modest improvements in social relationship deficits. Among the intervention approaches assessed, improvising and playing an instrument achieved slightly better outcomes. However, while promising, the robustness of such conclusions is bound by small sample sizes, insufficient longitudinal studies, and limited age diversity among participants. These limitations underscore the need for largerscale, more diverse, and more in-depth research to confirm the findings and extend their generalizability. Previous reviews have discussed the application of technologies such as robots, virtual reality (VR), and wearable devices in music therapy for ASD. This review expands on these discussions by exploring the integration of artificial intelligence (AI), which offers significant potential to enhance the personalization, adaptiveness, and efficacy of traditional music interventions for ASD. We explore promising avenues for multifaceted AI applications in music selection and generation, and automatic emotion and response detection, while also discussing the challenges of implementing AI in music therapy for ASD.
Data exhibit the distribution of the problem space, and the efficacy of machine learning models is contingent upon the availability of quality datasets. Additionally, in traditional machine learning models, data are required to be collected on a centralized server for training and testing, which raises privacy and security concerns. Federated Learning (FL) was designed with the aim of moving the computation to the data source (clients) instead of bringing the data to the central server. While addressing data accessibility and privacy issues, the eligibility of clients and the quality of their data should be considered in a secure FL framework. It is difficult to verify the authenticity of the clients participating in the FL environment. Various solutions have been proposed, including statistical analysis of client updates, hardware-based isolation, Differential Privacy (DP), Homomorphic Encryption (HE), and others. However, these solutions still pose limitations and suffer significantly from trade-offs, such as the privacy-utility tradeoff. In this research, we propose an approach to fortify the FL environment with continuous verification of client updates to prevent model poisoning attacks and use a filter ensemble to detect the data poisoning attacks. Our empirical experiments demonstrated improved performance against these attacks and alleviated the limitations in existing solutions.
