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Found 5 papers, 1 papers with code
An Adaptive Data-Resilient Multi-Modal Framework for Hierarchical Multi-Label Book Genre Identification
A key feature of our framework is its resilience to incomplete data, enabling accurate predictions even when certain modalities, such as text, images, or metadata, are missing or incomplete.
Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network
Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences.
ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution
In this paper, we introduce a real-time QoS prediction framework (called ARRQP) with a specific emphasis on improving resilience to anomalies in the data.
TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e. g., collaborative features) to comprehend the relationship among the user-service interactions.
VStegNET: Video Steganography Networkusing Spatio-Temporal features andMicro-Bottleneck
Traditional image steganography techniques hide the secret image intohigh-frequency regions of the cover images.
