ENTERPRISE AI ANALYSIS

Scalability solutions are categorized into Layer0, Layer1, and Layer2 techniques. Layer0 solutions focus on optimizing network data propagation (e.g., Compact Blocks). Layer1 techniques involve direct modifications to the blockchain, such as increasing block size (often problematic), Merkelized Abstract Syntax Trees (MAST) for efficient transaction verification, Segregated Witness (SegWit) for signature separation, sharding to partition nodes, and child chains (e.g., Ethereum Plasma) for offloading transactions.

Layer2 techniques utilize off-chain approaches like state channels (e.g., Bitcoin Lightning Network) and sidechains, allowing transactions to occur outside the main chain to improve throughput and reduce latency.

Machine learning, particularly Deep Learning and KNN Classification, offers promising avenues to enhance blockchain scalability, especially in data-intensive applications like healthcare. Deep learning models, supported by two-layer blockchain architectures with Merkle trees, can improve efficiency, privacy, and transparency. KNN classification, when integrated with decentralized storage like IPFS, can enable real-time medical data classification, improving decision-making and system performance by efficiently processing and analyzing large datasets and reusing trained models.

The paper demonstrates the application of off-chain and ML techniques in supply chain management and healthcare. For supply chain, integrating IPFS with Ethereum smart contracts significantly reduces transaction latency (0.2-1.2 sec vs 2.9 sec). In healthcare, a decentralized IPFS-based storage combined with KNN classification achieves 88.52% accuracy in cardiac state prediction, and reduces 100MB data upload time to 34 sec vs 39.3 sec in existing work, addressing interoperability and data volume challenges.