Semantic Optimization in Lossless Data Compression for IoT-Driven Smart Healthcare Solutions

In this study, we explore the potential of the Internet of Things (IoT) within the domain of smart healthcare management applications, an area of growing importance in Computer Science, Artificial Intelligence (AI), Machine Learning (ML), and Data Mining. IoT's capacity to efficiently amass, pr...

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Bibliographic Details
Published in:2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) pp. 224 - 228
Main Authors: K, Manju Bargavi S., Hashmi, Hina
Format: Conference Proceeding
Language:English
Published: IEEE 23.11.2023
Subjects:
Efficient Data Storage for Medical IoT Systems
Internet of Things
IoT in Healthcare
Lossless Compression Algorithms
Medical services
Memory
Semantic Data Compression
Semantics
Smart Healthcare Data Management
Time measurement
Transforms
Vehicular and wireless technologies
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Summary:In this study, we explore the potential of the Internet of Things (IoT) within the domain of smart healthcare management applications, an area of growing importance in Computer Science, Artificial Intelligence (AI), Machine Learning (ML), and Data Mining. IoT's capacity to efficiently amass, process, and transfer data is revolutionizing smart city initiatives, healthcare, energy distribution, and vehicular technology. Our primary aim is to construct a robust framework for smart healthcare management that not only harnesses the power of IoT but also integrates ontological models for enhanced data compression without compromising data quality. Our proposed theoretical framework is structured around three integral components: the pre-processing module, the ontological module, and the data compression module. Leveraging a comprehensive dataset from a Kaggle repository, we applied this tripartite approach to smart healthcare data, seeking to refine the data transformation process into a digital format-a process traditionally fraught with challenges due to the voluminous nature of healthcare data. We introduce a novel compression method and evaluate its performance against traditional compression algorithms such as Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), and Huffman coding. The efficacy of our approach is measured using metrics like compression factor (CF), compression ratio (CR), and compression time, providing a quantitative assessment of its viability. The integration of IoT into the ontological approaches and data compression techniques presented in this study not only enhances healthcare services, but also highlights the potential of AI and ML to transform data processing and data storage solutions. This study seeks to demonstrate the benefits of this multi-functional approach and its surprising results in the future of smart healthcare.
DOI:10.1109/ICCSAI59793.2023.10421686