隨著物聯網與人工智慧技術的迅速發展，醫療產業對資料安全傳輸與智慧診斷的需求日益攀升。為因應此趨勢，本研究提出一套前瞻醫療影像安全傳輸暨心律不整診斷輔助系統，兼具高安全性與高準確率。在資訊安全方面，設計基於分數階 Lorenz 混沌系統與 SHA-256 的動態金鑰產生機制，並結合 AES-CFB 模式實現影像加密。經 NIST SP 800-22、ENT 及 DIEHARD 等測試驗證，生成的亂數具備優異的隨機性與不可預測性，且加密影像於直方圖、資訊熵與相關分析等指標表現均接近理想值，並以同步控制技術解決傳統對稱式加密中金鑰分配的問題。在診斷輔助方面，提出以進化規劃演算法為基礎的自動超參數最佳化演算法，應用於心律不整分類模型。該方法同時考量正整數型、連續型與特殊型的超參數，可有效提升準確率與效率。實驗結果顯示，僅耗時 0.58 小時即可搜索出 F1-Score 高達 97.47% 的診斷輔助模型，並於 Precision、Recall、Accuracy 及混淆矩陣等多項指標上展現穩定的表現，大幅降低傳統調參過程的時間與人力成本。本研究成果結合實際臨床資料集，完成模型建立與性能評估。此系統不僅展現創新性與可行性，更為智慧醫療領域提供實用的參考架構，對未來相關技術發展具深遠貢獻。

With the rapid advancement of IoT and AI technologies, the medical industry has witnessed a growing demand for secure data transmission and intelligent diagnostic systems. In response to this trend, this study proposes a prospective medical image secure transmission and arrhythmia diagnosis-aided system that integrates high security with high diagnostic accuracy. A dynamic key generation mechanism based on a fractional-order Lorenz chaotic system and SHA-256, combined with AES-CFB encryption, ensures strong security. Randomness tests, including NIST SP 800-22, ENT, and DIEHARD, confirm high unpredictability, and encrypted images achieve near-ideal results in histogram analysis, entropy, and correlation. A synchronization control technique addresses the key distribution problem inherent in traditional symmetric encryption. For diagnosis, an automated hyperparameter optimization algorithm based on evolutionary programming is applied. It efficiently tunes integer, continuous, and special parameters, achieving an F1-score of 97.47% in 0.58 hours. The model also performs consistently across precision, recall, accuracy, and confusion matrix metrics. This research utilizes real clinical data for model development and performance evaluation. The proposed system is innovative, feasible, and offers a practical framework that advances smart healthcare technologies.

[1] A. Daissaoui, A. Boulmakoul, L. Karim, and A. Lbath, "IoT and Big Data Analytics for Smart Buildings: A Survey," Procedia Computer Science, vol. 170, pp. 161-168, 2020/01/01/ 2020, doi: https://doi.org/10.1016/j.procs.2020.03.021.
[2] Z. Ji, J. Brown, and J. Zhang, "True Random Number Generator (TRNG) for Secure Communications in the Era of IoT," 2020: IEEE, pp. 1-5, doi: 10.1109/cstic49141.2020.9282535. [Online]. Available: https://dx.doi.org/10.1109/cstic49141.2020.9282535
[3] X.-Q. Fu, B.-C. Liu, Y.-Y. Xie, W. Li, and Y. Liu, "Image Encryption-Then-Transmission Using DNA Encryption Algorithm and The Double Chaos," IEEE Photonics Journal, vol. 10, no. 3, pp. 1-15, 2018, doi: 10.1109/jphot.2018.2827165.
[4] K. Lee, S.-Y. Lee, C. Seo, and K. Yim, "TRNG (True Random Number Generator) Method Using Visible Spectrum for Secure Communication on 5G Network," IEEE Access, vol. 6, pp. 12838-12847, 2018, doi: 10.1109/access.2018.2799682.
[5] Y. Ma, T. Chen, J. Lin, J. Yang, and J. Jing, "Entropy Estimation for ADC Sampling-Based True Random Number Generators," IEEE Transactions on Information Forensics and Security, vol. 14, no. 11, pp. 2887-2900, 2019, doi: 10.1109/tifs.2019.2908798.
[6] S. Araki, J.-H. Wu, and J.-J. Yan, "A Novel Design of Random Number Generators Using Chaos-Based Extremum Coding," IEEE Access, vol. 12, pp. 24039-24047, 2024, doi: 10.1109/access.2024.3365638.
[7] N. Charalampidis, C. K. Volos, L. Moysis, H. E. Nistazakis, and I. N. Stouboulos, "A Novel Piecewise Chaotic Map for Image Encryption," 2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp. 1-4, 2022.
[8] Y. Xu and M. Tang, "Color Image Encryption Algorithm Using DNA Encoding and Fuzzy Single Neurons," IEEE Access, vol. 10, pp. 127770-127782, 2022, doi: 10.1109/ACCESS.2022.3221804.
[9] Q. He, P. Li, and Y. Wang, "A Color Image Encryption Algorithm Based on Compressive Sensing and Block-Based DNA Coding," IEEE Access, vol. 12, pp. 77621-77638, 2024, doi: 10.1109/ACCESS.2024.3406766.
[10] Y. Luo, X. Ouyang, J. Liu, and L. Cao, "An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems," IEEE Access, vol. 7, pp. 38507-38522, 2019, doi: 10.1109/ACCESS.2019.2906052.
[11] S. T. Kamal, K. M. Hosny, T. M. Elgindy, M. M. Darwish, and M. M. Fouda, "A New Image Encryption Algorithm for Grey and Color Medical Images," IEEE Access, vol. 9, pp. 37855-37865, 2021, doi: 10.1109/ACCESS.2021.3063237.
[12] T. L. Liao, P. Y. Wan, and J. J. Yan, "Design and Synchronization of Chaos-Based True Random Number Generators and Its FPGA Implementation," IEEE Access, vol. 10, pp. 8279-8286, 2022, doi: 10.1109/ACCESS.2022.3142536.
[13] İ. Koyuncu and A. Turan Özcerit, "The design and realization of a new high speed FPGA-based chaotic true random number generator," Computers & Electrical Engineering, vol. 58, pp. 203-214, 2017/02/01/ 2017, doi: https://doi.org/10.1016/j.compeleceng.2016.07.005.
[14] Z. Yang, Y. Liu, Y. Wu, Y. Qi, F. Ren, and S. Li, "A high speed pseudo-random bit generator driven by 2D-discrete hyperchaos," Chaos, Solitons & Fractals, vol. 167, p. 113039, 2023/02/01/ 2023, doi: https://doi.org/10.1016/j.chaos.2022.113039.
[15] J. L. Echenausía-Monroy, C. A. Rodríguez-Martíne, L. J. Ontañón-García, J. Alvarez, and J. Pena Ramirez, "Synchronization in Dynamically Coupled Fractional‐Order Chaotic Systems: Studying the Effects of Fractional Derivatives," Complexity, vol. 2021, no. 1, pp. 1-12, 2021, doi: 10.1155/2021/7242253.
[16] N. Sene, "Introduction to the fractional-order chaotic system under fractional operator in Caputo sense," Alexandria Engineering Journal, vol. 60, no. 4, pp. 3997-4014, 2021/08/01/ 2021, doi: https://doi.org/10.1016/j.aej.2021.02.056.
[17] E. İnce, B. Karakaya, and M. Türk, "Fractional and Integer Order Chaotic System-based Pseudo Random Bit Generator for Secure Image Encryption," in 2022 13th National Conference with International Participation (ELECTRONICA), 19-20 May 2022 2022, pp. 1-4, doi: 10.1109/ELECTRONICA55578.2022.9874397.
[18] F. Ozkaynak, "A Novel Random Number Generator Based on Fractional Order Chaotic Chua System," Elektronika ir Elektrotechnika, vol. 26, no. 1, pp. 52-57, 2020, doi: 10.5755/j01.eie.26.1.25310.
[19] W. Alexan, M. Gabr, E. Mamdouh, R. Elias, and A. Aboshousha, "Color Image Cryptosystem Based on Sine Chaotic Map, 4D Chen Hyperchaotic Map of Fractional-Order and Hybrid DNA Coding," IEEE Access, vol. 11, pp. 54928-54956, 2023, doi: 10.1109/ACCESS.2023.3282160.
[20] S. Xu, X. Wang, and X. Ye, "A new fractional-order chaos system of Hopfield neural network and its application in image encryption," Chaos, Solitons & Fractals, vol. 157, p. 111889, 2022/04/01/ 2022, doi: https://doi.org/10.1016/j.chaos.2022.111889.
[21] A. Mincholé, J. Camps, A. Lyon, and B. Rodríguez, "Machine learning in the electrocardiogram," Journal of Electrocardiology, vol. 57, pp. S61-S64, 2019/11/01/ 2019, doi: https://doi.org/10.1016/j.jelectrocard.2019.08.008.
[22] S. Hong, Y. Zhou, J. Shang, C. Xiao, and J. Sun, "Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review," Computers in Biology and Medicine, vol. 122, p. 103801, 2020/07/01/ 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103801.
[23] F. Murat, O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya, "Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review," Computers in Biology and Medicine, vol. 120, p. 103726, 2020/05/01/ 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103726.
[24] Z. Wang, K. Wang, X. Chen, Y. Zheng, and X. Wu, "A deep learning approach for inter-patient classification of premature ventricular contraction from electrocardiogram," Biomedical Signal Processing and Control, vol. 94, p. 106265, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.bspc.2024.106265.
[25] W. Zhang, L. Yu, L. Ye, W. Zhuang, and F. Ma, "ECG Signal Classification with Deep Learning for Heart Disease Identification," in 2018 International Conference on Big Data and Artificial Intelligence (BDAI), 22-24 June 2018 2018, pp. 47-51, doi: 10.1109/BDAI.2018.8546681.
[26] M. A. Ahamed, K. A. Hasan, K. F. Monowar, N. Mashnoor, and M. A. Hossain, "ECG Heartbeat Classification Using Ensemble of Efficient Machine Learning Approaches on Imbalanced Datasets," in 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), 28-29 Nov. 2020 2020, pp. 140-145, doi: 10.1109/ICAICT51780.2020.9333534.
[27] B. H. Shekar and G. Dagnew, "Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data," in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 25-28 Feb. 2019 2019, pp. 1-8, doi: 10.1109/ICACCP.2019.8882943.
[28] J. Bergstra and Y. Bengio, "Random Search for Hyper-Parameter Optimization," The Journal of Machine Learning Research, vol. 13, pp. 281-305, 03/01 2012.
[29] F. S. Baños, N. H. Romero, J. C. S. T. Mora, J. M. Marín, I. B. Vite, and G. E. A. Fuentes, "A Novel Hybrid Model Based on Convolutional Neural Network With Particle Swarm Optimization Algorithm for Classification of Cardiac Arrhythmias," IEEE Access, vol. 11, pp. 55515-55532, 2023, doi: 10.1109/ACCESS.2023.3282315.
[30] M. Munsarif, M. Sam'An, and A. Fahrezi, "Convolution neural network hyperparameter optimization using modified particle swarm optimization," Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 1268-1275, 2024, doi: 10.11591/eei.v13i2.6112.
[31] S. Loussaief and A. Abdelkrim, "Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms," International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018, doi: 10.14569/ijacsa.2018.091031.
[32] F. Itano, M. A. d. A. d. Sousa, and E. Del-Moral-Hernandez, "Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm," in 2018 International Joint Conference on Neural Networks (IJCNN), 8-13 July 2018 2018, pp. 1-8, doi: 10.1109/IJCNN.2018.8489520.
[33] P. G. Baines, "Lorenz, E.N. 1963: Deterministic nonperiodic flow. Journal of the Atmospheric Sciences 20, 130–41.1," Progress in Physical Geography: Earth and Environment, vol. 32, no. 4, pp. 475-480, 2008/08/01 2008, doi: 10.1177/0309133308091948.
[34] "Secure hash standard," National Institute of Standards and Technology (U.S.), 2015. [Online]. Available: https://dx.doi.org/10.6028/nist.fips.180-4
[35] L. E. Bassham et al., "A statistical test suite for random and pseudorandom number generators for cryptographic applications," National Institute of Standards and Technology, 2010. [Online]. Available: https://dx.doi.org/10.6028/nist.sp.800-22r1a
[36] J. Walker. "ENT: A Pseudorandom Number Sequence Test Program." https://www.fourmilab.ch/random/ (accessed.
[37] G. Marsaglia. "The Diehard Battery of Tests of Randomness " http://stat.fsu.edu/pub/diehard/ (accessed.
[38] Q. Fan, F. Ran, and L. Yan, "Multi-bit per cycle true random number generator based on XOR-XNOR ring oscillator unit," Microelectronics Journal, vol. 146, p. 106142, 2024/04/01/ 2024, doi: https://doi.org/10.1016/j.mejo.2024.106142.
[39] Y. Chen, H. Liang, L. Zhang, L. Yao, and Y. Lu, "High throughput dynamic dual entropy source true random number generator based on FPGA," Microelectronics Journal, vol. 145, p. 106113, 2024/03/01/ 2024, doi: https://doi.org/10.1016/j.mejo.2024.106113.
[40] A. Akgul, B. Gurevin, I. Pehlivan, M. Yildiz, M. C. Kutlu, and E. Guleryuz, "Development of micro computer based mobile random number generator with an encryption application," Integration, vol. 81, pp. 1-16, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.vlsi.2021.04.010.
[41] H. E. Kiran, A. Akgul, O. Yildiz, and E. Deniz, "Lightweight encryption mechanism with discrete-time chaotic maps for Internet of Robotic Things," Integration, vol. 93, p. 102047, 2023/11/01/ 2023, doi: https://doi.org/10.1016/j.vlsi.2023.06.001.
[42] N. Hayati, S. Windarta, M. Suryanegara, B. Pranggono, and K. Ramli, "A Novel Session Key Update Scheme for LoRaWAN," IEEE Access, vol. 10, pp. 89696-89713, 2022, doi: 10.1109/ACCESS.2022.3200397.
[43] S. Behnia, A. Akhavan, A. Akhshani, and A. Samsudin, "A novel dynamic model of pseudo random number generator," Journal of Computational and Applied Mathematics, vol. 235, no. 12, pp. 3455-3463, 2011/04/15/ 2011, doi: https://doi.org/10.1016/j.cam.2011.02.006.
[44] A. Akhshani, A. Akhavan, A. Mobaraki, S. C. Lim, and Z. Hassan, "Pseudo random number generator based on quantum chaotic map," Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 1, pp. 101-111, 2014/01/01/ 2014, doi: https://doi.org/10.1016/j.cnsns.2013.06.017.
[45] B. Paul, G. Trivedi, P. Jan, and Z. Nemec, "Efficient PRNG Design and Implementation for Various High Throughput Cryptographic and Low Power Security Applications," 2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 1-6, 2019.
[46] K. Chand Gupta and I. Ghosh Ray, "On Constructions of Involutory MDS Matrices," Springer Berlin Heidelberg, 2013, pp. 43-60.
[47] USC-SIPI. The USC-SIPI Image Database [Online] Available: https://sipi.usc.edu/database/
[48] Z. Li, C. Peng, W. Tan, and L. Li, "A Novel Chaos-Based Color Image Encryption Scheme Using Bit-Level Permutation," Symmetry, vol. 12, no. 9, p. 1497, 2020, doi: 10.3390/sym12091497.
[49] X. Wang, X. Chen, S. Feng, and C. Liu, "Color image encryption scheme combining cross-plane Zigzag scrambling and pseudo-random combination RGB component diffusion," Optik, vol. 269, p. 169933, 2022/11/01/ 2022, doi: https://doi.org/10.1016/j.ijleo.2022.169933.
[50] N. Khalil, A. Sarhan, and M. A. M. Alshewimy, "An efficient color/grayscale image encryption scheme based on hybrid chaotic maps," Optics & Laser Technology, vol. 143, p. 107326, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.optlastec.2021.107326.
[51] F.-Q. Meng and G. Wu, "A color image encryption and decryption scheme based on extended DNA coding and fractional-order 5D hyper-chaotic system," Expert Systems with Applications, vol. 254, p. 124413, 2024/11/15/ 2024, doi: https://doi.org/10.1016/j.eswa.2024.124413.
[52] X. Chai, X. Fu, Z. Gan, Y. Lu, and Y. Chen, "A color image cryptosystem based on dynamic DNA encryption and chaos," Signal Processing, vol. 155, pp. 44-62, 2019/02/01/ 2019, doi: https://doi.org/10.1016/j.sigpro.2018.09.029.
[53] D. Ding, H. Zhu, H. Zhang, Z. Yang, and D. Xie, "An n-dimensional polynomial modulo chaotic map with controllable range of Lyapunov exponents and its application in color image encryption," Chaos, Solitons & Fractals, vol. 185, p. 115168, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.chaos.2024.115168.
[54] B. Ge, Z. Shen, and X. Wang, "Symmetric Color Image Encryption Using a Novel Cross–Plane Joint Scrambling–Diffusion Method," Symmetry, vol. 15, no. 8, doi: 10.3390/sym15081499.
[55] M. G. A. Malik, Z. Bashir, N. Iqbal, and M. A. Imtiaz, "Color Image Encryption Algorithm Based on Hyper-Chaos and DNA Computing," IEEE Access, vol. 8, pp. 88093-88107, 2020, doi: 10.1109/access.2020.2990170.
[56] S. Tang, X. Xu, Z. Jiang, D. Meng, and K. Sun, "An image encryption scheme without additional key transmission based on an N-dimensional closed-loop coupled triangular wave model," Chaos, Solitons & Fractals, vol. 185, p. 115039, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.chaos.2024.115039.
[57] M. A. Elmenyawi, N. M. Abdel Aziem, and A. M. Bahaa-Eldin, "Efficient and Secure Color Image Encryption System with Enhanced Speed and Robustness Based on Binary Tree," Egyptian Informatics Journal, vol. 27, p. 100487, 2024/09/01/ 2024, doi: https://doi.org/10.1016/j.eij.2024.100487.
[58] S. Yan, D. Jiang, Y. Cui, H. Zhang, L. Li, and J. Jiang, "A fractional-order hyperchaotic system that is period in integer-order case and its application in a novel high-quality color image encryption algorithm," Chaos, Solitons & Fractals, vol. 182, p. 114793, 2024/05/01/ 2024, doi: https://doi.org/10.1016/j.chaos.2024.114793.
[59] N.-R. Zhou, L.-L. Hu, Z.-W. Huang, M.-M. Wang, and G.-S. Luo, "Novel multiple color images encryption and decryption scheme based on a bit-level extension algorithm," Expert Systems with Applications, vol. 238, p. 122052, 2024/03/15/ 2024, doi: https://doi.org/10.1016/j.eswa.2023.122052.
[60] M. Krichen, "Convolutional Neural Networks: A Survey," Computers, vol. 12, no. 8, p. 151, 2023. [Online]. Available: https://www.mdpi.com/2073-431X/12/8/151.
[61] I. Purwono et al., "Understanding of Convolutional Neural Network (CNN): A Review," International Journal of Robotics and Control Systems, vol. 2, pp. 739-748, 01/15 2023, doi: 10.31763/ijrcs.v2i4.888.
[62] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017, doi: 10.1145/3065386.
[63] C. Szegedy et al., "Going deeper with convolutions," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7-12 June 2015 2015, pp. 1-9, doi: 10.1109/CVPR.2015.7298594.
[64] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.
[65] V. Nair and G. Hinton, Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair. 2010, pp. 807-814.
[66] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998, doi: 10.1109/5.726791.
[67] X. Glorot, A. Bordes, and Y. Bengio, "Deep Sparse Rectifier Neural Networks," presented at the Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, 2011. [Online]. Available: https://proceedings.mlr.press/v15/glorot11a.html.
[68] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," CoRR, vol. abs/1409.1556, 2014.
[69] J.-J. Yan, J. S.-H. Tsai, and F.-C. Kung, "Robust stability analysis of interval systems with multiple time-varying delays: Evolutionary programming approach," Journal of the Franklin Institute, vol. 336, no. 4, pp. 711-720, 1999/05/01/ 1999, doi: https://doi.org/10.1016/S0016-0032(98)00048-9.
[70] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., 1989.
[71] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, 27 Nov.-1 Dec. 1995 1995, vol. 4, pp. 1942-1948 vol.4, doi: 10.1109/ICNN.1995.488968.
[72] G. B. Moody and R. G. Mark, "The impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45-50, 2001, doi: 10.1109/51.932724.
[73] ANSI/AAMI EC57:2012. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms., A. f. t. A. o. M. I. (AAMI). USA, 2012.
[74] G. Roland, J. D. S, S. N. Padhi, S. Kayalvili, S. Cloudin, and A. Kumar, "An Automated System for Arrhythmia Detection using ECG records from MITDB," in 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), 13-15 Dec. 2022 2022, pp. 26-33, doi: 10.1109/ICACRS55517.2022.10029289.
[75] Y. K. Kim, M. Lee, H. S. Song, and S. W. Lee, "Automatic Cardiac Arrhythmia Classification Using Residual Network Combined With Long Short-Term Memory," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-17, 2022, doi: 10.1109/TIM.2022.3181276.
[76] B. Murugesan et al., "ECGNet: Deep Network for Arrhythmia Classification," in 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 11-13 June 2018 2018, pp. 1-6, doi: 10.1109/MeMeA.2018.8438739.
[77] M. Bukhari et al., "A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM," Computers, Materials and Continua, vol. 77, no. 1, pp. 1251-1279, 2023/10/31/ 2023, doi: https://doi.org/10.32604/cmc.2023.040329.
[78] A. A. Samir, A. R. Rashwan, K. M. Sallam, R. K. Chakrabortty, M. J. Ryan, and A. A. Abohany, "Evolutionary algorithm-based convolutional neural network for predicting heart diseases," Computers & Industrial Engineering, vol. 161, p. 107651, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.cie.2021.107651.
[79] E. Maghawry, T. F. Gharib, R. Ismail, and M. J. Zaki, "An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model," IEEE Access, vol. 9, pp. 153266-153275, 2021, doi: 10.1109/ACCESS.2021.3128134.
[80] D. K. Atal and M. Singh, "Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network," Computer Methods and Programs in Biomedicine, vol. 196, p. 105607, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105607.
[81] M. R. I. T. K. G. M. A. H. A.-U.-A. A. I. M. E. Ahmed, "PSO-ML: Heartbeat Classifier Using Particle Swarm Optimization And Machine Learning Techniques," IOSR Journal of Dental and Medical Sciences (IOSR-JDMS) vol. 22, no. 2279-0853, pp. 50-58, 2023.
[82] H. Bechinia, D. Benmerzoug, and N. Khlifa, "Approach Based Lightweight Custom Convolutional Neural Network and Fine-Tuned MobileNet-V2 for ECG Arrhythmia Signals Classification," IEEE Access, vol. 12, pp. 40827-40841, 2024, doi: 10.1109/ACCESS.2024.3378730.
[83] M. Zubair, S. Woo, S. Lim, and D. Kim, "Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification," IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 5, pp. 2461-2472, 2024, doi: 10.1109/JBHI.2023.3325540.
[84] M. R. Islam, M. Qaraqe, K. Qaraqe, and E. Serpedin, "CAT-Net: Convolution, attention, and transformer based network for single-lead ECG arrhythmia classification," Biomedical Signal Processing and Control, vol. 93, p. 106211, 2024/07/01/ 2024, doi: https://doi.org/10.1016/j.bspc.2024.106211.
[85] F. Zabihi, F. Safara, and B. Ahadzadeh, "An electrocardiogram signal classification using a hybrid machine learning and deep learning approach," Healthcare Analytics, vol. 6, p. 100366, 2024/12/01/ 2024, doi: https://doi.org/10.1016/j.health.2024.100366.
[86] J. Liu et al., "A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification," Medical Engineering & Physics, vol. 130, p. 104209, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.medengphy.2024.104209.
[87] G. Clifford et al., "AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017," 2017: Computing in Cardiology, doi: 10.22489/cinc.2017.065-469. [Online]. Available: https://dx.doi.org/10.22489/cinc.2017.065-469
[88] kinguistics. "Heartbeat Sounds Dataset " https://www.kaggle.com/datasets/kinguistics/heartbeat-sounds (accessed Sep 19, 2024).
[89] B. Mishra and A. Kertesz, "The Use of MQTT in M2M and IoT Systems: A Survey," IEEE Access, vol. 8, pp. 201071-201086, 2020, doi: 10.1109/ACCESS.2020.3035849.
[90] W. A. Jabbar, H. K. Shang, S. N. I. S. Hamid, A. A. Almohammedi, R. M. Ramli, and M. A. H. Ali, "IoT-BBMS: Internet of Things-Based Baby Monitoring System for Smart Cradle," IEEE Access, vol. 7, pp. 93791-93805, 2019, doi: 10.1109/ACCESS.2019.2928481.
[91] E. Shahri, P. Pedreiras, and L. Almeida, "A Scalable Real-Time SDN-Based MQTT Framework for Industrial Applications," IEEE Open Journal of the Industrial Electronics Society, vol. 5, pp. 215-235, 2024, doi: 10.1109/OJIES.2024.3373232.
[92] S. A. P and S. M. D. Kumar, "Analysis and Evaluation of MQTT Brokers for e-Healthcare Applications," IEEE Transactions on Industrial Informatics, vol. 21, no. 2, pp. 1289-1298, 2025, doi: 10.1109/TII.2024.3476538.
[93] A. Morchid, R. Jebabra, H. Qjidaa, R. El Alami, and M. O. Jamil, "Agri-tech innovations for sustainability: A fire detection system based on MQTT broker and IoT to improve environmental risk management," Results in Engineering, vol. 24, p. 103683, 2024/12/01/ 2024, doi: https://doi.org/10.1016/j.rineng.2024.103683.
[94] J. D. Rodríguez-Muñoz, E. Tlelo-Cuautle, and L. G. de la Fraga, "Chaos-based authentication of encrypted images under MQTT for IoT protocol," Integration, vol. 102, p. 102378, 2025/05/01/ 2025, doi: https://doi.org/10.1016/j.vlsi.2025.102378.
[95] A. J. Hintaw, "Performance Analysis of MQTT Protocol in IoT Environments: Impact of Payload Size and QoS on Key Metrics," Technium: Romanian Journal of Applied Sciences and Technology, vol. 28, pp. 43-57, 03/22 2025, doi: 10.47577/technium.v28i.12649.
[96] A. Kozminski, "Windows Presentation Foundation (WPF) technology meets the challenges of operator interface design in automatic test systems," in 2012 IEEE AUTOTESTCON Proceedings, 10-13 Sept. 2012 2012, pp. 80-83, doi: 10.1109/AUTEST.2012.6334585.
[97] D. Fortunato and J. Bernardino, "Progressive web apps: An alternative to the native mobile Apps," in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), 13-16 June 2018 2018, pp. 1-6, doi: 10.23919/CISTI.2018.8399228.
[98] C. Anderson, "The Model-View-ViewModel (MVVM) Design Pattern," in Pro Business Applications with Silverlight 5, C. Anderson Ed. Berkeley, CA: Apress, 2012, pp. 461-499.