# Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction

arXiv: https://arxiv.org/abs/2410.12642 · HTML: https://mlsanity.com/paper/2410.12642/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation](https://mlsanity.com/paper/2409.09324/) (2024)
- [ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs](https://mlsanity.com/paper/2305.10649/) (2023)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (2024)
- [Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm](https://mlsanity.com/paper/2405.10762/) (2024)

## Built on by (most cited first, 5 of 5)

- [Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data](https://mlsanity.com/paper/2505.22521/) (2025)
- [In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates](https://mlsanity.com/paper/2410.15612/) (2024)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms](https://mlsanity.com/paper/2412.18202/) (2024)
- [Mitigating Knowledge Conflicts in Language Model-Driven Question Answering](https://mlsanity.com/paper/2411.11344/) (2024)

## Abstract (arXiv)

This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk assessment. The system utilizes EC2 p3.8xlarge GPU instances to accelerate model training, reducing training time by 93.2% while maintaining a prediction accuracy of 94.2%. With an automated data processing and model training pipeline built using Apache Airflow, the system can complete end-to-end updates within 18.7 hours. In clinical applications, the system demonstrates a prediction accuracy of 89.8%, sensitivity of 92.3%, and specificity of 95.1%. Early interventions based on predictions lead to a 37.5% reduction in diabetes incidence among the target population. The system's high performance and scalability provide strong support for large-scale diabetes prevention and management, showcasing significant public health value.