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- Vol. 6 Issue 5
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AI Based Radiation Emission Risk, Environmental Impact, and Mitigation Planning System
Authors
1 Assistant Professor, Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davangere, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davangere, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 5 (2026)
- Published Online
- September-October 2026
- Pages
- 226-230
- Article Type
- Original Article
- Check for updates
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References
1. H. N. Saleh, E. A. Mohammed, and A. M. Zeki, “IoT-Based Real-Time Radiation Monitoring System for Cardiac Catheterization Rooms,” Instrumentation Mesure Metrologie, vol. 25, no. 4, pp. 153–163, Aug. 2026. doi: 10.18280/i2m.250401.
2. Purwantiningsih, “Design and Development of a Radiation Dose Measurement Tool Using the Geiger Muller Sensor With ESP-32 Based on the Internet of Things (IoT),” Navigation Physics: Journal of Physics Education, vol. 7, no. 2, pp. 148–163, Dec. 29, 2025. doi: 10.30998/n28p7v86.
3. Polanco-Zepeda, O. O. Flores-Cortez, C. P. Jimenez, and F. Arevalo, “A Low-Cost IoT System for Radiation Monitoring and Visualization Using a Geiger-Müller Sensor: A Case Study at the Dosimetric Calibration Laboratory in El Salvador,” in Proc. IEEE Central America and Panama Convention (CONCAPAN), 2025.doi: 10.1109/concapan66820.2025.11512623.
4. P. Rindhatayathon, K. Sukyaprot, W. Sudchai, P. Sang-ondee, S. Theirrattanakul, T. Rungseesumran, and V. Pungkun, “Machine Learning Approaches for Improving Dosimeter Reading Accuracy Conform with New Operational Quantities Recommended by the ICRU 95,” Applied Radiation and Isotopes, vol. 225, Art.no. 112013, Nov. 2025. doi: 10.1016/j.apradiso.2025.112013.
5. P. Bhalerao, V. Chaudhari, S. Ganvir, and P. Bhoir, “Radiation Exposure Pattern Analyzer Using Machine Learning,” SSRN Electronic Journal, posted Jan. 7, 2026. doi: 10.2139/ssrn.5826962.
6. M. Elhaie, A. Koozari, and D. Shahbazi-Gahrouei, “Machine Learning and Neural Network Approaches for Enhanced Measuring and Prediction of Radiation Doses,” Journal of Radiation Research and Applied Sciences, vol. 18, no. 1, Art.no. 101252, Mar. 2025.doi: 10.1016/j.jrras.2024.101252.
7. G. M. Kendall, J. D. Appleton, P. Chernyavskiy, A. Arsham, and M. P. Little, “Machine Learning Techniques for the Prediction of Indoor Gamma-Ray Dose Rates—Strengths, Weaknesses and Implications for Epidemiology,” Journal of Environmental Radioactivity, vol. 282, Art.no. 107595, Feb. 2025. doi: 10.1016/j.jenvrad.2024.107595.
2. Purwantiningsih, “Design and Development of a Radiation Dose Measurement Tool Using the Geiger Muller Sensor With ESP-32 Based on the Internet of Things (IoT),” Navigation Physics: Journal of Physics Education, vol. 7, no. 2, pp. 148–163, Dec. 29, 2025. doi: 10.30998/n28p7v86.
3. Polanco-Zepeda, O. O. Flores-Cortez, C. P. Jimenez, and F. Arevalo, “A Low-Cost IoT System for Radiation Monitoring and Visualization Using a Geiger-Müller Sensor: A Case Study at the Dosimetric Calibration Laboratory in El Salvador,” in Proc. IEEE Central America and Panama Convention (CONCAPAN), 2025.doi: 10.1109/concapan66820.2025.11512623.
4. P. Rindhatayathon, K. Sukyaprot, W. Sudchai, P. Sang-ondee, S. Theirrattanakul, T. Rungseesumran, and V. Pungkun, “Machine Learning Approaches for Improving Dosimeter Reading Accuracy Conform with New Operational Quantities Recommended by the ICRU 95,” Applied Radiation and Isotopes, vol. 225, Art.no. 112013, Nov. 2025. doi: 10.1016/j.apradiso.2025.112013.
5. P. Bhalerao, V. Chaudhari, S. Ganvir, and P. Bhoir, “Radiation Exposure Pattern Analyzer Using Machine Learning,” SSRN Electronic Journal, posted Jan. 7, 2026. doi: 10.2139/ssrn.5826962.
6. M. Elhaie, A. Koozari, and D. Shahbazi-Gahrouei, “Machine Learning and Neural Network Approaches for Enhanced Measuring and Prediction of Radiation Doses,” Journal of Radiation Research and Applied Sciences, vol. 18, no. 1, Art.no. 101252, Mar. 2025.doi: 10.1016/j.jrras.2024.101252.
7. G. M. Kendall, J. D. Appleton, P. Chernyavskiy, A. Arsham, and M. P. Little, “Machine Learning Techniques for the Prediction of Indoor Gamma-Ray Dose Rates—Strengths, Weaknesses and Implications for Epidemiology,” Journal of Environmental Radioactivity, vol. 282, Art.no. 107595, Feb. 2025. doi: 10.1016/j.jenvrad.2024.107595.
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