Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges
Predicting the time, location and magnitude of an earthquake is a challenging job as an
earthquake does not show specific patterns resulting in inaccurate predictions. Techniques …
earthquake does not show specific patterns resulting in inaccurate predictions. Techniques …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
DeepAnT: A deep learning approach for unsupervised anomaly detection in time series
Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …
periodic and seasonality related point anomalies which occur commonly in streaming data …
Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity
S Kanarachos, SRG Christopoulos… - … research part C: emerging …, 2018 - Elsevier
Nowadays, more than half of the world's web traffic comes from mobile phones, and by 2020
approximately 70 percent of the world's population will be using smartphones. The …
approximately 70 percent of the world's population will be using smartphones. The …
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …
industrial applications in which sensors monitor expensive machinery. The complexity of this …
A GAN-based anomaly detection approach for imbalanced industrial time series
Imbalanced time series are universally found in industrial applications, where the number of
normal samples is far larger than that of abnormal cases. Traditional machine learning …
normal samples is far larger than that of abnormal cases. Traditional machine learning …
Energy consumption forecasting based on Elman neural networks with evolutive optimization
Buildings are an essential part of our social life. People spend a substantial fraction of their
time and spend a high amount of energy in them. There is a grand variety of systems and …
time and spend a high amount of energy in them. There is a grand variety of systems and …
[HTML][HTML] K-means-based isolation forest
The task of anomaly detection in data is one of the main challenges in data science because
of the wide plethora of applications and despite a spectrum of available methods …
of the wide plethora of applications and despite a spectrum of available methods …
Dynamic prediction for attitude and position in shield tunneling: A deep learning method
Quality management in shield tunneling projects is a challenging problem. To improve the
quality of segment erection, the shield driver must constantly adjust the shield's attitude and …
quality of segment erection, the shield driver must constantly adjust the shield's attitude and …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …