Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Background Detecting food fraud or confirming the authenticity, which falls within the
general concept of food integrity, is a complex problem. Modern analytics platforms are used …

Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of
building energy systems. This paper aims at making a comprehensive literature review of …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

Deep neural networks have achieved state-of-the-art performance in a wide range of
recognition/classification tasks. However, when applying deep learning to real-world …

Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by
perturbing correctly classified inputs to cause DNN models to misbehave (eg …

While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

To date, almost all experimental evaluations of machine learning-based recognition
algorithms in computer vision have taken the form of “closed set” recognition, whereby all …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …