Peter Han Joo Chong
Biographic Data
| ID | 6577086 |
|---|---|
| NAME | Peter Han Joo Chong |
| GIVEN NAMES | Peter Han Joo |
| FAMILY NAME | Chong |
| SIGNATURE | CHONG P H J |
| AFFILIATIONS | Auckland University of Technology |
| ORCID | 0000-0002-5375-8961 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A Hierarchical Federated Continual Learning Framework for Dynamic and Heterogeneous IoV
Traditional federated learning (FL) architectures face challenges in handling heterogeneous data and dynamic tasks, often resulting in catastrophic forgetting when new training tasks are continuously introduced. Federated continual learning (FCL) integrates the privacy-preserving capabilities of FL with the knowledge retention and incremental update mechanisms of continual learning, effectively mitigating catastrophic forgetting and protecting us…
Systematic Review of Sleep Monitoring Systems for Babies
Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children’s monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the…
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Systematic Review of Sleep Monitoring Systems for Babies
Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children’s monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the…
A Hierarchical Federated Continual Learning Framework for Dynamic and Heterogeneous IoV
Traditional federated learning (FL) architectures face challenges in handling heterogeneous data and dynamic tasks, often resulting in catastrophic forgetting when new training tasks are continuously introduced. Federated continual learning (FCL) integrates the privacy-preserving capabilities of FL with the knowledge retention and incremental update mechanisms of continual learning, effectively mitigating catastrophic forgetting and protecting us…
Adaptability (1 works) · Cluster analysis (1 works) · Computer Science (1 works) · Context-Aware Activity Recognition Systems (1 works) · Current (fluid (1 works) · Domain Adaptation and Few-Shot Learning (1 works) · Engineering (1 works) · Face recognition and analysis (1 works) · Federated learning (1 works) · Forgetting (1 works)