With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels
of connectivity and data. Anomaly detection is a security feature that identifies instances in …

Federated Learning (FL) is a promising distributed learning approach that enables multiple
clients to collaboratively train a shared global model. However, recent studies show that FL …

Recently, the emergency of predictive maintenance (PdM) in the building industry has
expanded from facilities to indoor climates, as air quality is highly relevant to residential …

Crimes have clearly had a detrimental impact on a nation's development, prosperity,
reputation, and economy. The issue of crime has become one of the most pressing concerns …

Explainability and alert reasoning are essential but often neglected properties of intrusion
detection systems. The lack of explainability reduces security personnel's trust, limiting the …

The research focuses on smart city technical planning based on IoT data time series
forecasting. The study use machine learning techniques, notably random forest regression …

The identification of anomalous activities is a challenging and crucially important task in
sensor networks. This task is becoming increasingly complex with the increasing volume of …

Missing data presents a significant challenge in statistical analysis and machine learning,
often resulting in biased outcomes and diminished efficiency. This comprehensive review …

In alarm systems, conventional univariate alarm methods often result in frequent false and
missing alarms, calling for an urgent need to introduce multivariate information. For the …

Abstract Coronavirus disease (COVID-19) is a respiratory condition that has quickly
expanded to pandemic levels, affecting people in over 192 countries around the world …