A comparison of LSTM and GRU networks for learning symbolic sequences
We explore the architecture of recurrent neural networks (RNNs) by studying the complexity
of string sequences that it is able to memorize. Symbolic sequences of different complexity …
of string sequences that it is able to memorize. Symbolic sequences of different complexity …
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
In recent years, a variety of research areas have contributed to a set of related problems with
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …
System-level hardware failure prediction using deep learning
Disk and memory faults are the leading causes of server breakdown. A proactive solution is
to predict such hardware failure at the runtime and then isolate the hardware at risk and …
to predict such hardware failure at the runtime and then isolate the hardware at risk and …
Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms
The research in anomaly detection lacks a unified definition of what represents an
anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple …
anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple …
Evaluating feature selection and anomaly detection methods of hard drive failure prediction
As vast amounts of data are saved, hard drive failure prediction is critical to reducing the cost
of data loss and backup. Most existing studies used to detect the anomalous status of a hard …
of data loss and backup. Most existing studies used to detect the anomalous status of a hard …
Multi-instance deep learning based on attention mechanism for failure prediction of unlabeled hard disk drives
G Wang, Y Wang, X Sun - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Failure of hard disk drives (HDDs) is the most critical reliability issue of data center.
Therefore, predicting the failure of the HDD is an important means to ensure the storage …
Therefore, predicting the failure of the HDD is an important means to ensure the storage …
Pred-ID: Future event prediction based on event type schema mining by graph induction and deduction
In the field of information management, effective event intelligence management is crucial
for its development. With the continuous evolution of events, predicting future events has …
for its development. With the continuous evolution of events, predicting future events has …
Supporting telecommunication alarm management system with trouble ticket prediction
MW Asres, MA Mengistu… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Fault alarm data emanated from heterogeneous telecommunication network services and
infrastructures are exploding with network expansions. Managing and tracking the alarms …
infrastructures are exploding with network expansions. Managing and tracking the alarms …
Sce-lstm: Sparse critical event-driven lstm model with selective memorization for agricultural time-series prediction
In the domain of agricultural product sales and consumption forecasting, the presence of
infrequent yet impactful events such as livestock epidemics and mass media influences …
infrequent yet impactful events such as livestock epidemics and mass media influences …
Temporal-Contextual Attention Network for Solid-State Drive Failure Prediction in Data Centers
Proactive strategies for predicting solid state drive (SSD) failures are imperative to ensure
uninterrupted services in data centers. Traditional methods that rely on rule-based …
uninterrupted services in data centers. Traditional methods that rely on rule-based …