Printed text recognition using BLSTM and MDLSTM for Indian languages

Chavan, Vishal; Malage, Abhijit; Mehrotra, Kapil; Gupta, Manish Kumar

doi:10.1109/iciip.2017.8313738

Cited by 11 publications

(1 citation statement)

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“…An LSTM only utilizes past context but a Bidirectional Long-Short Term Memory (BLSTM) [10] can utilize both the past and future contexts by processing the input data in both the forward and backward directions. BLSTMs have have been used for numerous tasks such as text recognition and classification [1], [46]. A Gated Recurrent Unit (GRU) [2] is similar to an LSTM network and the performance is comparable.…”

Section: Introductionmentioning

confidence: 99%

Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU

Farzad¹,

Gulliver²

2019

Preprint

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Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. These models are used to convert unstructured log data to trained features which is suitable for classification algorithms. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB.The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification and is used here to show that the models can be generalized to other text classification tasks. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms.

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Section: Introductionmentioning

confidence: 99%