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Found 7 papers, 3 papers with code
Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network
Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources.
Online Self-Supervised Learning in Machine Learning Intrusion Detection for the Internet of Things
This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Machine Learning (ML) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning.
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection
Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices.
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks
Thus this work introduces a collective Botnet attack classification technique that operates on traffic from an n-node IP network with a novel Associated Random Neural Network (ARNN) that identifies the nodes which are compromised.
Engagement Detection with Multi-Task Training in E-Learning Environments
Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak.
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings.
Ranked #1 on
Fire Detection
on NIST Report of Test FR 4016
Curse of Small Sample Size in Forecasting of the Active Cases in COVID-19 Outbreak
The paper shows that simple linear regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models, which have the potential of learning long term predictions with low errors, cannot achieve to obtain good predictions with possessing a high generalization ability.
