Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing
সংরক্ষণ করুন:
| প্রকাশিত: | EPJ Web of Conferences vol. 326 (2025) |
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| প্রধান লেখক: | |
| অন্যান্য লেখক: | , |
| প্রকাশিত: |
EDP Sciences
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text - PDF |
| ট্যাগগুলো: |
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| সার সংক্ষেপ: | This paper investigates the impact of data preprocessing on the performance, efficiency, and environmental footprint of AI models in cloud-based applications, focusing on a case study involving healthcare applications such as chronic disease detection. We analyze how preprocessing techniques affect some of the most commonly used Machine Learning (ML) algorithms, namely K-means, SVM, and KNN, emphasizing their role in reducing computational load, energy consumption, and carbon emissions in data centers. Our results demonstrate that the impact of preprocessing on both accuracy and processing speed varies depending on the algorithm and the type of preprocessing applied. Notable improvements in precision and processing time reductions of up to 35% were observed, highlighting the potential of preprocessing to enhance the performance and sustainability of ML algorithms. |
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| আইএসএসএন: | 2101-6275 2100-014X |
| ডিওআই: | 10.1051/epjconf/202532605005 |
| সম্পদ: | Advanced Technologies & Aerospace Database |