This study introduces SALF (Secure Academic Ledger Framework), a technically innovative blockchain-based system engineered to overcome persistent challenges in academic credential management, including latency bottlenecks, governance opacity, and integration inflexibility. SALF pioneers a hybrid on-chain/off-chain architecture optimized for low-latency operations while preserving blockchain immutability, and it employs a role-based smart contract suite tailored to institutional hierarchies. Unlike prior frameworks, SALF integrates a degree-based incentive mechanism that quantifies data quality metrics—legibility, correctness, and non-redundancy—to ensure equitable institutional participation and discourage centralization. Built upon a Proof of Authority (PoA) consensus model, SALF achieves high performance under load, maintaining a throughput of over 30 transactions per second (TPS) and P95 latency below 300 milliseconds. RESTful APIs ensure real-time interoperability with existing systems such as ERPs and academic dashboards. Compared to benchmark systems like EduCert-Chain and EduCopyRight-Chain, the proposed framework achieves a 41.3% reduction in latency and maintains stable throughput under high-load conditions, even as other systems exhibit significant degradation or integration constraints. These distinctive technical contributions position SALF as a scalable, governance-aware, and future-ready infrastructure for decentralized academic credentialing across heterogeneous institutions.

Researchers and financiers utilize statistics on stock market operation, including past returns, share values, accessible, large, poor, near, and transaction size, to identify trends in stock activity. Technological evaluation can provide financiers with contradictory indications. They focused on predicting the return and risk levels of stock market prices. As a result, the stock market requires an automatic technology that can anticipate the return rate of bitcoin. Deep learning (DL) technology can estimate share market trends employing technological evaluation. This research creates a deep learning-based smart return rate estimation method for blockchain-based stock market prices (RRE-DLBSP). The proposed RRE-DLBSP framework develops an LSTM (long short-term memory) paradigm for RRE assessment. Additionally, the LSTM algorithm's hyperparameters are effectively adjusted using the Adam optimization algorithm, improving prediction efficiency. The Ethereum return rate is selected as the goal and the model outcomes are examined in various ways to ensure the highest functioning of the RRE-DLBSP paradigm. The research results demonstrated the RRE-DLBSP method's superiority over the cutting-edge approaches based on a variety of performance metrics. The suggested RRE-DLBSP has an MSE of 0.0436 and 0.0656, while the mean for the evaluated techniques in training and evaluation is 0.725 and 0.6124, respectively.

Early detection, therapeutic stratification, and precision medicine all rely on the precise classification of brain cancer subtypes. To categorize brain tumor subtypes, we examine the application of ensemble machine learning models—Random Forest, XGBoost, and LightGBM—using high-dimensional gene expression data from the GSE50161 dataset (CuMiDa). The top 1000 genes were selected using variance thresholding, and models were then trained and evaluated on a stratified split of the dataset. Despite the availability of models achieving similar accuracies (~95–96%) in existing works, our framework integrates SHAP-based interpretability to identify biologically significant genes, such as CDK4, EGFR, and TP53, offering dual benefits of high predictive power and explainability. The use of SHAP (SHapley Additive exPlanations) values to assess model predictions and identify physiologically important gene features revealed that key gene probes, including as CDK4, EGFR, and TP53, were significant across different tumor subtypes. This study demonstrates how SHAP and interpretable ensemble learning may be used to diagnose brain tumors with excellent classification accuracy and physiologically meaningful gene identification.