Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 02021 | |
| Number of page(s) | 9 | |
| Section | Machine Learning Applications in Vision, Security, and Healthcare | |
| DOI | https://doi.org/10.1051/itmconf/20257802021 | |
| Published online | 08 September 2025 | |
- Ozmen, M.M., Ozmen, A., Koç, Ç.K.: 'Artificial Intelligence for Next-Generation Medical Robotics', in Atallah, S. (Ed.): Digital Surgery, Springer, Cham, 2021 [Google Scholar]
- Alatabani, L.E., Ali, E.S., Saeed, R.A.: 'Machine Learning and Deep Learning Approaches for Robotics Applications', in Azar, A.T., Koubaa, A. (Eds.): Artificial Intelligence for Robotics and Autonomous Systems Applications, Studies in Computational Intelligence, vol. 1093, Springer, Cham, 2023 [Google Scholar]
- Ali, R., Cui, H.: 'Unleashing the potential of AI in modern healthcare: Machine learning algorithms and intelligent medical robots', Res. Intell. Manuf. Assem., 2024, 3, (1), pp. 100–108 [Google Scholar]
- Dupont, P.E., et al.: 'A decade retrospective of medical robotics research from 2010 to 2020', Sci. Robot., 2021, 6, eabi8017 [Google Scholar]
- Esteva, A., Kuprel, B., Novoa, R.A., et al.: 'Dermatologist-level classification of skin cancer with deep neural networks', Nature, 2017, 542, (7639), pp. 115–118 [CrossRef] [PubMed] [Google Scholar]
- Rajkomar, A., Dean, J., Kohane, I.: 'Machine learning in medicine', N. Engl. J. Med., 2019, 380, (14), pp. 1347–1358 [Google Scholar]
- Mishra, S., Radhakrishnan, G., Gupta, D., Sudarshan, T.S.B.: 'Acquisition and Analysis of Robotic Data Using Machine Learning Techniques', in Jain, L., Behera, H., Mandal, J., Mohapatra, D. (Eds.): Computational Intelligence in Data Mining - Volume 3, Smart Innovation, Systems and Technologies, vol. 33, Springer, New Delhi, 2015 [Google Scholar]
- Mazurek, P., Wagner, J., Morawski, R.Z.: 'Acquisition and preprocessing of data from infrared depth sensors to be applied for patients monitoring', Proc. IEEE 8th Int. Conf. Intell. Data Acquis. Adv. Comput. Syst. (IDAACS), Warsaw, Poland, Sept. 2015, pp. 705–710 [Google Scholar]
- Deevi, D.P., Allur, N.S., Dondapati, K., et al.: 'AI-Integrated Probabilistic Neuro-Fuzzy TemporalFusionNet for Robotic IoMT Automation in Chronic Kidney Disease Detection and Prediction', Proc. Int. Conf. Emerg. Res. Comput. Sci. (ICERCS), Coimbatore, India, 2024, pp. 1–7 [Google Scholar]
- Li, K., Cheng, Z., Zeng, J., et al.: 'Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis', Sci. Rep., 2023, 13, 15504 [Google Scholar]
- Peuchpen, P., Liu, H., Ma, J.: 'Real-Time Teleoperation in Motion Mapping for Medical Robot Based on Robot Manipulator and Haptic Device', Proc. IEEE Int. Conf. Robot. Biomimetics (ROBIO), Bangkok, Thailand, 2024, pp. 2221–2226 [Google Scholar]
- Nandhini, R.S., Lakshmanan, R.: 'A novel ensemble learning approach for fault detection of sensor data in cyber-physical system', J. Intell. Fuzzy Syst., 2023, 45, (6), pp. 12111–12122 [Google Scholar]
- Song, J., Chen, Z., Li, W.: 'Real-Time Diagnosis of Abrupt and Incipient Faults in IMU Using a Lightweight CNN-Transformer Hybrid Model', IEEE Sens. J., 2025, 25, (7), pp. 12496–12510 [Google Scholar]
- Atif, A.u.R., Su, J.: 'Review of gesture recognition technique using cloud-assisted wearable devices for real-time healthcare', Proc. Int. Conf. Innov. Technol. Intell. Syst. Ind. Appl. (CITISIA), Sydney, Australia, 2020, pp. 1–11 [Google Scholar]
- Clark, N., Sandor, E., Walden, C., et al.: 'A wearable ECG monitoring system for real-time arrhythmia detection', Proc. IEEE 61st Int. Midwest Symp. Circuits Syst. (MWSCAS), Windsor, ON, Canada, 2018, pp. 787–790 [Google Scholar]
- Al-Maawali, Z.A., Noronha, H., Kumar, U.P.: 'Big data acquisition, preprocessing and analysis to Develop and Implement Effective Database System with High Security Standards', Proc. MEC Int. Conf. Big Data Smart City (ICBDSC), Muscat, Oman, 2019, pp. 1–4 [Google Scholar]
- Matam, B.R., Duncan, H.: 'Technical challenges related to implementation of a formula one real time data acquisition and analysis system in a paediatric intensive care unit', J. Clin. Monit. Comput., 2018, 32, pp. 559–569 [Google Scholar]
- He, X., Li, C., Liu, Z.: 'A Real-Time Adaptive Fault Diagnosis Scheme for Dynamic Systems with Performance Degradation', IEEE Trans. Reliab., 2024, 73, (2), pp. 1231–1244 [Google Scholar]
- de Chaves, S.A., Benitti, F.: 'User-Centred Privacy and Data Protection: An Overview of Current Research Trends and Challenges for the Human-Computer Interaction Field', ACM Comput. Surv., 2025, 57, (7), pp. 1–36 [Google Scholar]
- Sadeghi Milani, A., Cecil-Xavier, A., Gupta, A., Cecil, J., Kennison, S.: 'A Systematic Review of Human-Computer Interaction (HCI) Research in Medical and Other Engineering Fields', Int. J. Hum.-Comput. Interact., 2022, 40, (3), pp. 515–536 [Google Scholar]
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