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Enhancing Diabetic Retinopathy Detection: A Deep Learning Approach with Advanced Image Preprocessing
Siyah, Youssef ; El Belghiti, Younes ; Minaoui, Khalid ; et al.
2025 5th International Conference on Emerging Smart Technologies and Applications (eSmarTA) Emerging Smart Technologies and Applications (eSmarTA), 2025 5th International Conference on. :1-5 Aug, 2025

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Advanced Machine Learning and Deep Learning Techniques for Gujarati Sign Language Recognition
Moliya, Vivek ; Suthar, Om Prakash ; Pankhaniya, Nikhil ; et al.
2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) Emerging Technologies and Applications (MPSec ICETA), 2025 IEEE International Conference on. :1-6 Feb, 2025

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Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases
ZakirHossain, Md ; Khan, Md Munsur ; Thapa, Sanjog ; et al.
2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) Emerging Technologies and Applications (MPSec ICETA), 2025 IEEE International Conference on. :1-6 Feb, 2025

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Advanced Classification of Diabetic Foot Ulcers Using Custom and Deep Learning Models
ZakirHossain, Md ; Khan, Md Munsur ; Rahman, Sowad ; et al.
2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) Emerging Technologies and Applications (MPSec ICETA), 2025 IEEE International Conference on. :1-6 Feb, 2025

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Advanced Digital Technologies in Financial and Business Management: Unleashing the Power of Artificial Intelligence, Machine Learning, Blockchain, and the Internet of Things
Arora, Jyoti Batra, (DE-588)1101474602, (DE-627)859730972, (DE-576)469908068, edt, HerausgeberIn ; Pathak, Nitish, edt, HerausgeberIn ; Sharma, Neelam, (DE-588)1379184487, (DE-627)1938668030, edt, HerausgeberIn
2025

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Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures
Yuting Zhang
Science and Technology of Engineering, Chemistry and Environmental Protection. 1

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10

Leveraging Digital Data and Soft Computing Techniques for Advanced Mental Health Analysis and Intervention Strategies
Akkur, Malatesh ; Paikaray, Bijay Kumar ; Mukuntharaj, C. ; et al.
2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) Emerging Technologies and Applications (MPSec ICETA), 2025 IEEE International Conference on. :1-5 Feb, 2025

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11

Machine learning applications for mobile devices
Simacek, Dominik ; Panus, Jan ; Papan, Jozef ; et al.
2025 International Conference on Emerging eLearning Technologies and Applications (ICETA) Emerging eLearning Technologies and Applications (ICETA), 2025 International Conference on. :737-742 Nov, 2025

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12

1. Baytas, I. M., Xiao, C., Zhang, X., Wang, F., Jain, A. K., & Zhou, J. (2017). Patient subtyping via time-aware LSTM networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 65–74). ACM. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301–318). PMLR. 6. Esteban, C., Hyland, S. L., & Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633. 7. Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., & Galstyan, A. (2019). Multitask learning and benchmarking with clinical time series data. Scientific Data, *6*(1), 96. 8. Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020). BEHRT: Transformer for electronic health records. Scientific Reports, *10*(1), 7155. 9. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. In 4th International Conference on Learning Representations (ICLR). 10. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (pp. 3111–3119). 11. Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, *6*(1), 26094. 12. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., & Litsch, K. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 13. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589–1604. 14. Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What clinicians want: Contextualizing explainable machine learning for clinical end use. In Proceedings of the 4th Machine Learning for Healthcare Conference (pp. 359–380). PMLR. 15. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008). 16. Wang, S., McDermott, M. B. A., Chauhan, G., Ghassemi, M., Hughes, M. C., & Naumann, T. (2020). MIMIC-Extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III. In Proceedings of the ACM Conference on Health, Inference, and Learning (pp. 222–235). 17. Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(19), 5078–5092
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1. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12. Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15. Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... & Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16. Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17. Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492
Billy, Elly

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14

1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras
Okunola, Abiodun

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1. Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2021). Multimodal biomedical AI. Nature Medicine, *28*(9), 1773–1784. https://doi.org/10.1038/s41591-022-01981-2 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Grote, T., & Berens, P. (2020). On the ethics of algorithmic decision-making in healthcare. Journal of Medical Ethics, *46*(3), 205–211. https://doi.org/10.1136/medethics-2019-105586 5. Huang, S.-C., Pareek, A., Zamanian, R., Banerjee, I., & Lungren, M. P. (2022). Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: A case-study in pulmonary embolism detection. Scientific Reports, *12*, 214. https://doi.org/10.1038/s41598-021-03878-5 6. Li, Y., Keel, S., Liu, C., He, M. (2023). A multimodal deep learning system for cardiovascular disease risk assessment by combining retinal imaging and clinical data. NPJ Digital Medicine, *6*(1), 45. https://doi.org/10.1038/s41746-023-00788-w 7. Sengupta, P. P., Huang, Y.-M., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., Gall, W., & Dudley, J. T. (2020). Cognitive machine-learning algorithm for cardiac imaging: A pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circulation: Cardiovascular Imaging, *9*(6), e004330. https://doi.org/10.1161/CIRCIMAGING.115.004330 8. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, *25*(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7 9. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, *28*(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0 10. Litjens, G., Ciompi, F., Wolterink, J. M., de Vos, B. D., Leiner, T., Teuwen, J., & Išgum, I. (2019). State-of-the-art deep learning in cardiovascular image analysis. JACC: Cardiovascular Imaging, *12*(8), 1549–1565. https://doi.org/10.1016/j.jcmg.2019.06.009 11. Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D., Shrestha, S., Sengupta, P. P., & Marwick, T. H. (2019). Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology, *73*(11), 1317–1335. https://doi.org/10.1016/j.jacc.2018.12.054 12. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, *375*(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181 13. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, *69*(21), 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571 14. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, *25*(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Zeiberg, D., Prahlad, T., Nallamothu, B. K., Iwashyna, T. J., Wiens, J., & Sjoding, M. W. (2022). Machine learning for patient risk stratification in acute care: A systematic review. JAMA Network Open, *5*(4), e227085. https://doi.org/10.1001/jamanetworkopen.2022.7085 17. Vokinger, K. N., Feuerriegel, S., & Kesselheim, A. S. (2021). Mitigating bias in machine learning for medicine. Communications Medicine, *1*, 25. https://doi.org/10.1038/s43856-021-00028-w
Okunola, Abiodun

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16

A Comprehensive Review of Machine Learning-Based Approaches for Malware Detection
Ahmed, Faruk ; Hasan, Md. Khaled ; Alvi, Syada Tasmia
2025 5th International Conference on Emerging Smart Technologies and Applications (eSmarTA) Emerging Smart Technologies and Applications (eSmarTA), 2025 5th International Conference on. :1-8 Aug, 2025

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17

12 The Ethics of Security and Privacy of Cyber-physical Systems: Emerging Trends, Technology, and Applications
Security and Privacy of Cyber-physical Systems: Emerging Trends, Technologies, and Applications. Jul 15, 2025

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18

Edge AI in 6G: Vision, Technologies, and Applications
Centeno, Vanessa Azucena Monzon ; Caballero, Jesus Emilio Agustin Padilla ; Garcia, Claudia Rossana Poma ; et al.
2025 International Conference on Pervasive Computational Technologies (ICPCT) Pervasive Computational Technologies (ICPCT), 2025 International Conference on. :706-709 Feb, 2025

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19

The 9th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA’25), Volume 2
Aboul Ella Hassanien ; Eman Karam El-Sayed ; Ashraf Darwish ; et al.

Machine learning--Congre...
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20

Traffic Sign Recognition Using Hybrid Deep Ensemble Learning for Advanced Driving Assistance Systems
Utane, Akshay S. ; Mohod, S. W.
2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA) Emerging Smart Technologies and Applications (eSmarTA), 2022 2nd International Conference on. :1-5 Oct, 2022

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