Search Results for author:
Found 5 papers, 2 papers with code
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries
Traditional anomaly detection methods often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness.
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment
Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency.
Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data
Ensembles have shown to be able to successfully address imbalanced data problems.
Finding the most similar textual documents using Case-Based Reasoning
In 2016, the new similarity TS-SS metric is proposed, which showed state-of-the-art results in the field of textual mining for unsupervised learning.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).
