Developing a Scalable and Efficient Cloud-Based Framework for Distributed Machine Learning

Authors

  • Siddhant Benadikar, Rishabh Rajesh Shanbhag, Ugandhar Dasi, Nikhil Singla, Rajkumar Balasubramanian

Keywords:

Cloud computing, artificial intelligence, machine learning, personalized healthcare, remote patient monitoring, predictive analytics, telemedicine, wearable devices, clinical outcomes, healthcare innovation

Abstract

This comprehensive research paper evaluates the effectiveness of cloud-based artificial intelligence (AI) and machine learning (ML) techniques in personalized healthcare and remote patient monitoring. The study analyses various applications, including predictive analytics, natural language processing, computer vision, and wearable device integration. It examines the impact of these technologies on treatment plan optimization, drug discovery, risk stratification, and patient engagement. The research also investigates remote patient monitoring systems, focusing on real-time data analysis, anomaly detection, telemedicine integration, and chronic disease management. Through a rigorous evaluation framework, the study assesses clinical outcomes, cost-effectiveness, patient satisfaction, and healthcare provider feedback. Case studies in cardiovascular disease, diabetes, mental health, and post-operative care provide practical insights. The paper concludes by addressing challenges, limitations, and future directions for cloud-based AI and ML in healthcare, offering valuable recommendations for researchers, practitioners, and policymakers.

Downloads

Download data is not yet available.

References

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & 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).

Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., ... & Ng, A. Y. (2012). Large scale distributed deep networks. In Advances in neural information processing systems (pp. 1223-1231).

Jia, Z., Zaharia, M., & Aiken, A. (2018). Beyond data and model parallelism for deep neural networks. In Proceedings of the 2nd SysML Conference.

Krizhevsky, A. (2014). One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997.

Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., ... & Su, B. Y. (2014). Scaling distributed machine learning with the parameter server. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14) (pp. 583-598).

Lian, X., Zhang, C., Zhang, H., Hsieh, C. J., Zhang, W., & Liu, J. (2017). Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent. In Advances in Neural Information Processing Systems (pp. 5330-5340).

Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., ... & Talwalkar, A. (2016). MLlib: Machine learning in Apache Spark. The Journal of Machine Learning Research, 17(1), 1235-1241.

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (pp. 8026-8037).

Ribeiro, M., Grolinger, K., & Capretz, M. A. (2015). MLaaS: Machine learning as a service. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 896-902). IEEE.

Sergeev, A., & Del Balso, M. (2018). Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799.

Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2020). A survey on distributed machine learning. ACM Computing Surveys (CSUR), 53(2), 1-33.

Zhang, H., Zheng, Z., Xu, S., Dai, W., Ho, Q., Liang, X., ... & Xing, E. P. (2017). Poseidon: An efficient communication architecture for distributed deep learning on GPU clusters. In 2017 USENIX Annual Technical Conference (USENIX ATC 17) (pp. 181-193).

Downloads

Published

26.12.2021

How to Cite

Siddhant Benadikar. (2021). Developing a Scalable and Efficient Cloud-Based Framework for Distributed Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 288 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6761

Issue

Vol. 9 No. 4 (2021)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.