Smart homes, equipped with interconnected IoT devices such as locks, cameras, and sensors, face critical security challenges due to the limitations of static access control mechanisms like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which lack adaptability to dynamic, multi-user environments and evolving threats. To address this problem, this research introduces a hybrid Blockchain–Machine Learning (ML) framework that ensures secure, adaptive, and context-aware access control for smart home ecosystems. The proposed system integrates IoT devices with ML algorithms, including Support Vector Machines (SVM) and Neural Networks, to predict user behaviours and dynamically adjust access permissions in real time, while Blockchain ensures immutable, decentralized, and tamper-proof logging of access events. The methodology employed a mixed approach, beginning with an extensive literature review to identify shortcomings in existing static models, followed by system design using smart contracts, caching strategies to reduce latency, and a user perception survey involving 25 participants to validate acceptance and usability. Results demonstrated high user trust and readiness to adopt the proposed system, with 96% of respondents favouring Blockchain-ML-enabled dynamic access control over conventional methods despite concerns about privacy risks, costs, and implementation complexity. This work contributes to society by offering a scalable and intelligent smart home security solution that enhances trust, improves user experience, and strengthens resilience against cyber threats, ultimately supporting safer and smarter living environments.
Atikah Balqis Binti Basri, M. I. Mohd Tamrin, Mohd Khairul Azmi Hassan et al.· International Journal of Inn...· 0 citations
Wind power ramp events which are sudden, large swings in turbine output over short windows are difficult to estimate, and standard models often miss them. Hybrid forecasting approach is built which augments semantic context to ramp-event forecast. Rather than applying an extensive language model directly to predict turbine operating data, we have implemented a pipeline where turbine operating data is converted to simplified text, which is then converted to dense embeddings to be used as inputs for ensemble models incorporated with other features. Testing runs are performed at multiple intervals within the SDWPF dataset, including 10-minute, 30-minute, and 60- minute horizons, with ramp events constituting the highest change in future power output. We check robustness against autoregressive, LSTM, and GRU baselines plus several ensemble configurations, using Diebold-Mariano tests and bootstrap confidence intervals, and we vary the ramp threshold, compress the embeddings with PCA, and validate externally on Kaggle SCADA and NREL data with uncertainty-aware scoring. The semantic-context features produce negligible yet statistically significant gains over the baselines in multiple paired ensemble runs, most clearly at the 30- and 60-minute horizons where these gains hold across different ramp-threshold definitions, and PCA compression helps in some longer-horizon cases. The best context- augmented ensembles rank near the top overall, though the GRU model still posts the lowest ramp-event RMSE at 30 and 60 minutes. External tests confirm the error reduction generalizes across datasets, but the size of the gain depends on both model and dataset. Prediction intervals cover most test cases well but weaken during ramp events, pointing to a localized shift in the data distribution.
Momina Liaqat Ali, Muhammad Abid, Muhammad Abdullah et al.· 0 citations
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