⚡ Quick Summary
This research explores the application of fuzzy graph theory to enhance security in electromagnetic radiation therapy systems. The study reveals significant improvements in security metrics, including a 2.5% false acceptance rate and 95% intrusion detection accuracy, showcasing the potential of this innovative approach.
🔍 Key Details
- 📊 Focus: Security in electromagnetic radiation therapy
- 🧩 Methodology: Fuzzy graph theory and fuzzy cognitive maps
- ⚙️ Key Technologies: Fuzzy graph-based architectures
- 🏆 Performance Metrics: False acceptance rate, intrusion detection accuracy, confidentiality, and integrity rates
🔑 Key Takeaways
- 🔒 Enhanced Security: Fuzzy graph theory significantly improves security measures in therapy systems.
- 📉 False Acceptance Rate: Achieved a remarkable 2.5% compared to 7.8% in traditional systems.
- 🔍 Intrusion Detection: Accuracy improved to 95% with only 3% false positives.
- 🔐 Secure Communication: Protocols demonstrated 98% confidentiality and 96% integrity rates.
- 📈 Risk Assessment: Coverage increased to 92% with reduced false positives.
- ⏱️ Processing Time: Maintained linear scaling from 180 ms at 1,000 records to 320 ms at 100,000 records.
- 💻 CPU Utilization: Remained stable between 65% and 72%.
- 🔮 Future Directions: Integration of machine learning, blockchain, and scalability optimization are suggested for further research.
📚 Background
The increasing reliance on electromagnetic radiation therapy in clinical settings necessitates robust security measures to protect sensitive patient data and ensure safe treatment protocols. Traditional security systems often fall short in addressing the unique challenges posed by these advanced technologies. This study aims to bridge that gap by applying fuzzy graph theory to enhance security frameworks.
🗒️ Study
Conducted by researchers including Lal R, Singh RK, Nishad DK, and Khalid S, this study involved a comprehensive theoretical analysis and experimental validation of fuzzy graph-based architectures. The focus was on developing innovative solutions for access control, intrusion detection, secure communication, and risk assessment in electromagnetic radiation therapy systems.
📈 Results
The findings indicate that the fuzzy graph-based access control model significantly outperformed traditional systems, achieving a 2.5% false acceptance rate and a 95% accuracy in intrusion detection. Additionally, secure communication protocols demonstrated 98% confidentiality and 96% integrity rates, highlighting the effectiveness of the proposed methodologies.
🌍 Impact and Implications
The implications of this research are profound, as it underscores the potential of fuzzy graph theory to revolutionize security in healthcare technologies. By enhancing the safety and privacy of electromagnetic radiation therapy systems, this study lays the groundwork for future advancements in clinical practices and patient care. The integration of these technologies could lead to more secure and efficient healthcare environments.
🔮 Conclusion
This study showcases the transformative potential of fuzzy graph theory in enhancing security measures within electromagnetic radiation therapy systems. The significant improvements in key performance metrics suggest a promising avenue for future research and clinical adoption. As we look ahead, the integration of advanced technologies such as machine learning and blockchain could further optimize these systems, paving the way for safer healthcare practices.
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Enhancing security in electromagnetic radiation therapy using fuzzy graph theory.
Abstract
This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems. Through comprehensive theoretical analysis and experimental validation, we introduce novel approaches leveraging fuzzy cognitive maps and fuzzy graph-based architectures for access control, intrusion detection, secure communication, and risk assessment. The study demonstrates significant improvements over traditional security measures across multiple performance metrics. The fuzzy graph-based access control model achieved a 2.5% false acceptance rate compared to 7.8% in traditional systems, while intrusion detection accuracy improved to 95% with only 3% false positives. Secure communication protocols demonstrated 98% confidentiality and 96% integrity rates, surpassing conventional methods. Risk assessment coverage increased to 92% with reduced false positives. The system maintained linear scaling in processing time from 180 ms at 1000 to 320 ms at 100,000 records, with CPU utilization remaining between 65 and 72%. These findings underscore the immense potential of fuzzy graph theory in strengthening the safety and privacy of electromagnetic radiation therapy systems, providing a foundation for future research and clinical adoption. The study also identifies key directions for future research, including machine learning integration, blockchain implementation, and scalability optimization.
Author: [‘Lal R’, ‘Singh RK’, ‘Nishad DK’, ‘Khalid S’]
Journal: Sci Rep
Citation: Lal R, et al. Enhancing security in electromagnetic radiation therapy using fuzzy graph theory. Enhancing security in electromagnetic radiation therapy using fuzzy graph theory. 2025; 15:13139. doi: 10.1038/s41598-025-98110-z