Home Knowledge Base MAPE

MAPE

No mentions found

This entity hasn't been tracked yet, or Iris is still building its knowledge base.

Related Articles from SNS

Sequential Minimal Optimization for $\varepsilon$-SVR with MAPE Loss and Sample-Dependent Box Constraints

arXiv:2605.01446v3 Announce Type: replace Abstract: Support vector regression with Mean Absolute Percentage Error (MAPE) loss is theoretically well-motivated for forecasting applications where accuracy is evaluated in relative terms, but the sample-dependent dual box constraints it induces have not been addressed in the published SMO literature. We derive a Sequential Minimal Optimization algorithm for this setting and prove a structural-invariance result: the MAPE modification affects...

arXiv CS 1d ago

PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation

Announce Type: new Abstract: Can a visually plausible food mesh be trusted to estimate the volume of consumed food? \method investigates this question using selected paired before- and after-consumption states from the MetaFood CVPR 2026 Continuous 3D Reconstruction While Eating Challenge. The submitted workflow follows a curated reconstruction protocol: SAM~3 segments the food and plate regions; Hunyuan3D/SAM~3D generates a dimensionless food mesh; the plate diameter provides the metric...

arXiv CS 8d ago

Drishti AI-Event Guardian: An Intelligent Real-Time Crowd Monitoring and Emergency Response System for Mass Gathering Events

Announce Type: new Abstract: Mass gathering events are associated with critical safety incidents caused by insufficient crowd monitoring and inadequate emergency response coordination. Traditional surveillance systems lack intelligent analytics, resulting in delayed threat identification, poor resource deployment, and weak support for vulnerable individuals during dense public assemblies. This paper presents Drishti AI-Event Guardian, an intelligent crowd management framework using deep...

arXiv CS 5d ago

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

arXiv:2507.09766v2 Announce Type: replace Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation. However, hybrid models that combine data-driven learning with physics-based regularization often rely on fixed loss weights and therefore lose accuracy when transferred across assets with different degradation behaviors. This study...

arXiv CS 8d ago

Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

arXiv:2605.30720v1 Announce Type: new Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural influences on demand. This study introduces the Kalimati Vegetable Price Index (KVPI), a new inverse-volatility weighted composite index that aggregates 135 daily wholesale commodities from Kathmandu over ten years (2013-2023). By creating a stable macro-level signal, the KVPI reduces the noise...

arXiv CS 9d ago

Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum

new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential. However, orchestrators face a severe "cold start" problem: newly discovered nodes lack the historical data required to train localized predictive models, while generalized models fail to capture unique hardware and microservice behaviors. To solve this, we propose a...

arXiv CS 1d ago

Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions

arXiv:2606.07046v1 Announce Type: new Abstract: Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions...

arXiv CS 2d ago

Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting

arXiv:2605.30486v1 Announce Type: new Abstract: Spatio-temporal forecasting on sensor graphs is commonly tackled with a single backbone architecture applied uniformly across all nodes, although graph regions can exhibit different dynamics. Road segments differ in functional class, structure, and traffic behavior, suggesting that node-wise expert specialization can be useful. We propose GC-MoE, a graph-conditioned mixture of experts framework that assigns each node a personalized combination...

arXiv CS 9d ago