Transfer learning enables to re-use knowledge learned on a source task to help learning a
target task. A simple form of transfer learning is common in current state-of-the-art computer …

Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating
improved waste sorting for effective recycling and progress toward a circular economy …

Deep learning techniques have been explored within the marine litter problem for
approximately 20 years but the majority of the research has developed rapidly in the last five …

Robust object detection is crucial for automating underwater marine debris collection. While
supervised deep learning achieves state-of-the-art performance in discriminative tasks …

A Huge amount of solid waste is generated daily from human activities in residential,
industrial, agricultural, or commercial areas, leading to seashore, floating, and underwater …

The global plastic crisis is a significant concern. Addressing it requires a multi-faceted
approach involving legislative, social, and technological measures to reduce post-consumer …

With wide adoption of deep learning technology in AI, intelligent garbage detection has
become a hot research topic. However, existing datasets currently used for garbage …

We contribute SEAGULL, a novel pervasive sensing approach for monitoring and identifying
underwater plastics. SEAGULL builds on an innovative light (LED) sensing solution that …

The accumulation of anthropogenic waste in underwater environments leads to a decrease
in water quality, resulting in pollution that negatively impacts human health, ecological …

Abstract Pervasive Data Science (PDS) is an emerging paradigm that combines the Internet
of Things, Pervasive Computing, and Data Science to address everyday challenges. PDS …