REFERENCES

[1] Liu, Y., Zhang, W., Liu, W., Lu, Y., Tao, X., Jia, S., & Shi, D. (2024). Towards Noninvasive Glucose Monitoring Based on Bioimpedance Grid Sampling Topology. IEEE Transactions on Industrial Electronics, 71, 14916-14925.

[2] Pal, D., Kumar, A., Avraham, N., Eisenbach, Y., Beiderman, Y., Agdarov, S., Beiderman, Y., & Zalevsky, Z. (2023). Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses. Journal of Biomedical Optics, 28.

[3] Satyanarayana S, & Krishna Prasad K. (2024). Advancing Healthcare: Integrating A Deep Neural Network With The Bio-Inspired Puffer Fish Optimization Algorithm For Early Detection And Prediction Of Chronic Kidney Disease. International Journal of Management, Technology and Social Sciences (IJMTS), 9(4), 69–87. https://doi.org/10.47992/IJMTS.2581.6012.0367

[4] M Shokrekhodaei and S Quinones, "Review of Non-Invasive Glucose Sensing Techniques: Optical Electrical and Breath Acetone", Sensors., vol. 20, no. 5, pp. 1251, 2020, [online] Available: https://doi.org/10.3390/s20051251.

[5] Tayar, Yerremsetty, R. Siva Ram Prasad, and S. Satayanarayana. "An accurate classification of imbalanced streaming data using deep convolutional neural network." International Journal of Mechanical Engineering and Technology 9, no. 3 (2018): 770-783.

[6] WD Arnold, K Kupfer, M Hvidsten Swensen, KS Fortner, HE Bays, M Davis, et al., "Fingerstick Precision and Total Error of a Point-of-Care HbA1c Test", J Diabetes Sci Technol., vol. 14, no. 5, pp. 890-895, Sep 2020.

[7] Satyanarayana, S., Thayyaba Khatoon, and NV Madhu Bindu. "Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction." International Journal of Applied and Natural Sciences 1, no. 1 (2023): 22-41.

[8] Al-Baik, O., Alomari, S., Alssayed, O., Gochhait, S., Leonova, I., Dutta, U. & Dehghani, M. (2024). Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics, 9(2), 65. Available from: https://doi.org/10.3390/biomimetics9020065

[9] Satyanarayana, S., Yerremsetty Tayar, and R. Siva Ram Prasad. "Efficient DANNLO classifier for multi-class imbalanced data on Hadoop." International Journal of Information Technology 11 (2019): 321-329.

[10] Jeong, J., Lim, S., Choi, K., & Kim, H. (2024). A study on the Non-invasive Type Glucose Measurement System Combining Arrayed Waveguide Grating and Artificial Intelligence Technology. 2024 8th International Conference on Biomedical Engineering and Applications (ICBEA), 141-145.

[11] Bindu, N. M., & Satyanarayana, S. (2025). Designing of GAN for a real-time image processing in neuromorphic system. In Primer to Neuromorphic Computing (pp. 21-44). Academic Press.

[12] B Alsunaidi, M Althobaiti, M Tamal, W Albaker and I Al-Naib, "A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring", Sensors., vol. 21, no. 20, pp. 6820, 2021, [online] Available: https://doi.org/10.3390/s21206820.

[13] Satyanarayana, S., Khatoon, T., & Bindu, N. M. (2025). Neomorphic home automation systems. In Primer to Neuromorphic Computing (pp. 97-125). Academic Press.

[14] Chan PZ, Jin E, Jansson M, Chew HSJ. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review. J Med Internet Res. 2024 Nov 19;26:e58892. doi: 10.2196/58892. PMID: 39561353; PMCID: PMC11615544.

[15] Yen, C.-T., Chen, U.-H., Wang, G.-C., & Chen, Z.-X. (2022). Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring. Sensors, 22(12), 4452. https://doi.org/10.3390/s22124452

Cite this Article as: Singamsetty S (2025), “AI-Optimized Bio-Impedance Sensing with Puffer Fish Algorithm: A Breakthrough in Non-Invasive Glucose Monitoring”, International Journal of Education & Applied Sciences Research, Volume 12, Issue 1, 2025, pp 09-20  DOI : https://doi.org/10.5281/zenodo.15033940

REFERENCES

[1] Liu, Y., Zhang, W., Liu, W., Lu, Y., Tao, X., Jia, S., & Shi, D. (2024). Towards Noninvasive Glucose Monitoring Based on Bioimpedance Grid Sampling Topology. IEEE Transactions on Industrial Electronics, 71, 14916-14925.

[2] Pal, D., Kumar, A., Avraham, N., Eisenbach, Y., Beiderman, Y., Agdarov, S., Beiderman, Y., & Zalevsky, Z. (2023). Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses. Journal of Biomedical Optics, 28.

[3] Satyanarayana S, & Krishna Prasad K. (2024). Advancing Healthcare: Integrating A Deep Neural Network With The Bio-Inspired Puffer Fish Optimization Algorithm For Early Detection And Prediction Of Chronic Kidney Disease. International Journal of Management, Technology and Social Sciences (IJMTS), 9(4), 69–87. https://doi.org/10.47992/IJMTS.2581.6012.0367

[4] M Shokrekhodaei and S Quinones, "Review of Non-Invasive Glucose Sensing Techniques: Optical Electrical and Breath Acetone", Sensors., vol. 20, no. 5, pp. 1251, 2020, [online] Available: https://doi.org/10.3390/s20051251.

[5] Tayar, Yerremsetty, R. Siva Ram Prasad, and S. Satayanarayana. "An accurate classification of imbalanced streaming data using deep convolutional neural network." International Journal of Mechanical Engineering and Technology 9, no. 3 (2018): 770-783.

[6] WD Arnold, K Kupfer, M Hvidsten Swensen, KS Fortner, HE Bays, M Davis, et al., "Fingerstick Precision and Total Error of a Point-of-Care HbA1c Test", J Diabetes Sci Technol., vol. 14, no. 5, pp. 890-895, Sep 2020.

[7] Satyanarayana, S., Thayyaba Khatoon, and NV Madhu Bindu. "Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction." International Journal of Applied and Natural Sciences 1, no. 1 (2023): 22-41.

[8] Al-Baik, O., Alomari, S., Alssayed, O., Gochhait, S., Leonova, I., Dutta, U. & Dehghani, M. (2024). Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics, 9(2), 65. Available from: https://doi.org/10.3390/biomimetics9020065

[9] Satyanarayana, S., Yerremsetty Tayar, and R. Siva Ram Prasad. "Efficient DANNLO classifier for multi-class imbalanced data on Hadoop." International Journal of Information Technology 11 (2019): 321-329.

[10] Jeong, J., Lim, S., Choi, K., & Kim, H. (2024). A study on the Non-invasive Type Glucose Measurement System Combining Arrayed Waveguide Grating and Artificial Intelligence Technology. 2024 8th International Conference on Biomedical Engineering and Applications (ICBEA), 141-145.

[11] Bindu, N. M., & Satyanarayana, S. (2025). Designing of GAN for a real-time image processing in neuromorphic system. In Primer to Neuromorphic Computing (pp. 21-44). Academic Press.

[12] B Alsunaidi, M Althobaiti, M Tamal, W Albaker and I Al-Naib, "A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring", Sensors., vol. 21, no. 20, pp. 6820, 2021, [online] Available: https://doi.org/10.3390/s21206820.

[13] Satyanarayana, S., Khatoon, T., & Bindu, N. M. (2025). Neomorphic home automation systems. In Primer to Neuromorphic Computing (pp. 97-125). Academic Press.

[14] Chan PZ, Jin E, Jansson M, Chew HSJ. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review. J Med Internet Res. 2024 Nov 19;26:e58892. doi: 10.2196/58892. PMID: 39561353; PMCID: PMC11615544.

[15] Yen, C.-T., Chen, U.-H., Wang, G.-C., & Chen, Z.-X. (2022). Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring. Sensors, 22(12), 4452. https://doi.org/10.3390/s22124452