Search Results for author:
Found 17 papers, 0 papers with code
Digital Emotion Regulation on Social Media
Emotion regulation is the process of consciously altering one's affective state, that is the underlying emotional state such as happiness, confidence, guilt, anger etc.
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination
Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency.
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years.
A Secure Federated Learning Framework for Residential Short Term Load Forecasting
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few.
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach
Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems.
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles
In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs.
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers
In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development.
False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey
Therefore, this paper presents a comprehensive survey of the recent advances in FDI attacks within active distribution systems and proposes a taxonomy to classify the FDI threats with respect to smart grid targets.
Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch
Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation.
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection.
Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes
Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper.
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction
This paper proposes a novel Bayesian probabilistic technique for forecasting renewable solar generation by addressing data and model uncertainties by integrating bidirectional long short-term memory (BiLSTM) neural networks while compressing the weight parameters using variational autoencoder (VAE).
A Comprehensive Review on the NILM Algorithms for Energy Disaggregation
Incredible research and publications have been conducted on energy disaggregation, non-intrusive load monitoring, home energy management and appliance classification.
Detecting Autism Spectrum Disorder using Machine Learning
Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch.
Machine learning and data analytics for the IoT
The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing.
Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization
Wind based power is one of the most difficult energy to predict accurately, due to the widely varying and unpredictable nature of wind energy.
Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid
In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid.
