Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Streams of irregularly occurring events are commonly modeled as a marked temporal point
process. Many real-world datasets such as e-commerce transactions and electronic health …

Early multi-label classification of time series, the assignment of a label set to a time series
before the series is entirely observed, is critical for time-sensitive domains such as …

Purpose As drones are rapidly transforming tasks such as mapping and surveying, safety
inspection and progress monitoring, human operators continue to play a critical role in …

Most of the available user information can be represented as a sequence of timestamped
events. Each event is assigned a set of categorical labels whose future structure is of great …

Predictive Maintenance is a type of condition-based maintenance that assesses the
equipment's states and estimates its failure probability and when maintenance should be …

Abstract Arc Stud Welding (ASW) is widely used in many industries such as automotive and
shipbuilding and is employed in building and jointing large-scale structures. While defective …

Online prediction for streaming time series data has practical use for many real-world
applications where downstream decisions depend on accurate forecasts for the future …

In this work, a method for fault diagnosis and localization is proposed. This method adopts
the long short-term memory (LSTM) neural network to detect, isolate and determine the …

Defects are everywhere, and quality assessment is inevitable. Continuous monitoring and
maintenance of manufacturing equipment and infrastructure is critical to ensure delivery …