This study integrates soft sensors with Kafka-ML to predict NO₃⁻ levels in real time, enhancing low-cost IoT-based water monitoring and pollution control.
This paper presents the integration of federated learning and blockchain with Kafka-ML for reliable model training using data streams in decentralized and secure environments.
This study enhances Kafka-ML with PyTorch and GPU acceleration, demonstrating its efficiency for deep-learning workflows in Industry 4.0 scenarios.
This study presents an open-source deep learning web application for automatic pollen counting and classification, reducing manual effort and improving accuracy.