## 3 Results
### Analysis of AI and Smart Technology Applications Across Industry Sectors
#### 3.2.8 Integration of AI and Smart Technologies in Safety Management for Other Sectors  
In the \"other sectors\", AI and smart technologies significantly enhance safety management by addressing critical factors such as accident prevention and risk management, \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Safety Management Factor** & **AI and Smart Technology Research Methods** & **Research Number** \\ \hline
**Accident Prevention** & - Developed ontology-based systems using OWL and SWRL for risk recognition. - Implemented intelligent gas meters and automated fire suppression systems. - Utilized NIALM with machine learning algorithms for appliance monitoring and control. & [69; 70] \\ \hline
**Legal Requirements** & - Designed web-based safety management frameworks to ensure compliance with safety regulations. - Integrated AI governance frameworks to protect data privacy and ensure ethical AI implementation. & [69] \\ \hline
**Human Life Protection** & - Developed AI-powered emergency response systems and health monitoring devices. - Integrated wearable sensors for real-time health data collection and anomaly detection. - Utilized predictive models for timely health interventions. & [71] \\ \hline
**Property Protection** & - Employed AI security systems with computer vision and anomaly detection to monitor for unauthorized access. - Implemented smart surveillance systems to detect unusual activities and prevent theft or damage. & [70] \\ \hline
**Improvement of Operational Efficiency** & - Utilized AI-driven resource optimization systems for efficient energy management. - Implemented smart meters and automated power control systems to reduce energy consumption and prevent system failures. - Integrated sustainable development (SD) principles with IoT and KM for robust management. & [70; 71] \\ \hline
**Fire and CO Poisoning Prevention** & - Designed smart home IAQ monitoring systems with CO sensors and predictive models. - Utilized digital twin models for immersive fire and CO poisoning prevention. - Conducted scenario-based performance assessments and system tests for reliability. & [70] \\ \hline
**Energy Management** & - Developed AI-powered predictive models to analyze and optimize energy consumption. - Implemented energy-efficient solutions through smart meters and automated control systems. - Employed digital twin simulations to enhance energy efficiency strategies. & [71] \\ \hline
**Health Management** & - Integrated AI and IoT sensors in wearable devices for continuous health monitoring. - Developed mobile applications to display health metrics and provide personalized recommendations. - Utilized predictive analytics for early detection of health issues. & [71] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: AI and smart technology research methods for safety management factors in the smart home and living sector. workplace and facility safety management, quality assurance and reliability management, and system-based safety enhancement. AI-driven solutions leverage sensor networks, location-based services (LBSs), and natural language processing to proactively identify and mitigate potential hazards. For instance, the u-Food System utilizes sensor tags and LBS to monitor and ensure the quality of food distribution, while predictive modeling combines public and internal recall data to forecast and prevent food-related risks. Workplace safety and facility safety are improved through AI-powered monitoring systems that employ IoT sensors and computer vision to detect equipment malfunctions and unsafe behaviors in real time. Quality assurance is enhanced by anomaly detection algorithms that identify defects early in the production process, ensuring compliance with safety standards and increasing product reliability. Additionally, system-based safety enhancements are achieved through AI-driven automation and smart control systems, which optimize operations and reduce human exposure to hazardous tasks.