Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges

MH Al Banna, KA Taher, MS Kaiser, M Mahmud… - IEEE …, 2020 - ieeexplore.ieee.org
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 …

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 …

DeepAnT: A deep learning approach for unsupervised anomaly detection in time series

M Munir, SA Siddiqui, A Dengel, S Ahmed - Ieee Access, 2018 - ieeexplore.ieee.org
Traditional distance and density-based anomaly detection techniques are unable to detect
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 …

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

M Canizo, I Triguero, A Conde, E Onieva - Neurocomputing, 2019 - Elsevier
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 …

A GAN-based anomaly detection approach for imbalanced industrial time series

W Jiang, Y Hong, B Zhou, X He, C Cheng - IEEE Access, 2019 - ieeexplore.ieee.org
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 …

Energy consumption forecasting based on Elman neural networks with evolutive optimization

LGB Ruiz, R Rueda, MP Cuéllar… - Expert Systems with …, 2018 - Elsevier
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 …

[HTML][HTML] K-means-based isolation forest

P Karczmarek, A Kiersztyn, W Pedrycz, E Al - Knowledge-based systems, 2020 - Elsevier
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 …

Dynamic prediction for attitude and position in shield tunneling: A deep learning method

C Zhou, H Xu, L Ding, L Wei, Y Zhou - Automation in Construction, 2019 - Elsevier
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 …

Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting

GT Ribeiro, VC Mariani, L dos Santos Coelho - Engineering Applications of …, 2019 - Elsevier
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 …