Purpose The study addresses operational demands for scalable artificial intelligence frameworks in high-dimensional affinity prediction. Current computational solutions prioritize architectural complexity over interpretability. Architectural complexity creates operational barriers for deployment in decision-support sys...
Chairote Yaiprasert, Natthanan Na Prasit, Achmad Nizar Hidayanto· Applied Computing and Inform...· 0 citations
Current predictive maintenance architectures force a critical compromise between diagnostic precision and physical hardware constraints, routinely failing to deploy complex learning models onto resource-limited microcontrollers. The proposed framework dismantles the limitation through a decentralized, post-training qua...
Continuous ingestion of high-dimensional telemetry forces predictive networks into a severe space-time trade-off, where conventional outlier rejection algorithms dilute mathematical gradients with benign idle data and induce thermal throttling on local hardware. This study introduces an $\mathcal{O}(d)$ ternary quantiz...
Continuous ingestion of high-dimensional telemetry forces predictive networks into a severe space-time trade-off, where conventional outlier rejection algorithms dilute mathematical gradients with benign idle data and induce thermal throttling on local hardware. This study introduces an $\mathcal{O}(d)$ ternary quantiz...
