A review of deep learning with special emphasis on architectures, applications and recent trends
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
Benchmarking studies aimed at clustering and classification tasks using K-means, fuzzy C-means and evolutionary neural networks
A Pickens, S Sengupta - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …
machine learning applications and finds frequent use in diverse fields ranging from …
Data-driven detection of anomalies and cascading failures in traffic networks
Traffic networks are one of the most critical infrastructures for any community. The increasing
integration of smart and connected sensors in traffic networks provides researchers with …
integration of smart and connected sensors in traffic networks provides researchers with …
Benchmarking Clustering and Classification Tasks using K-Means, Fuzzy C-Means and Feedforward Neural Networks optimized by PSO
A Pickens - 2021 - digitalcommons.murraystate.edu
Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …
machine learning applications and finds frequent use in diverse fields ranging from …
Spatiotemporal Anomaly Detection and Prediction of Anomaly Propagation Path Using LSTM Networks
S Basak - 2020 - ir.vanderbilt.edu
Anomaly detection for connected systems is challenging as it is hard to observe and analyze
multiple spatial and temporal scales of sub-processes and operations given the …
multiple spatial and temporal scales of sub-processes and operations given the …