scholarly journals Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

2020 ◽  
Vol 171 ◽  
pp. 1313-1322
Author(s):  
Vijendra Pratap Singh ◽  
Manish Kumar Pandey ◽  
Pangambam Sendash Singh ◽  
Subbiah Karthikeyan
Keyword(s):  
Time Series ◽  
Web Services ◽  
Time Series Forecasting ◽  
Neural Net ◽  
Time Aware

Time series forecasting for dynamic quality of web services: An empirical study

2017 ◽  
Vol 134 ◽  
pp. 279-303 ◽  
Author(s):  
Yang Syu ◽  
Jong-Yih Kuo ◽  
Yong-Yi Fanjiang
Keyword(s):  
Time Series ◽  
Web Services ◽  
Empirical Study ◽  
Time Series Forecasting ◽  
Dynamic Quality

scholarly journals An Econometric Time Series Forecasting Framework for Web Services Recommendation

2020 ◽  
Vol 167 ◽  
pp. 1615-1625
Author(s):  
Vijendra Pratap Singh ◽  
Manish Kumar Pandey ◽  
Pangambam Sendash Singh ◽  
Subbiah Karthikeyan
Keyword(s):  
Time Series ◽  
Web Services ◽  
Time Series Forecasting

An LSTM Based Time Series Forecasting Framework for Web Services Recommendation

2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Vijendra Pratap Singh ◽  
Manish Kumar Pandey ◽  
Pangambam Sendash Singh ◽  
Subbiah Karthikeyan
Keyword(s):  
Time Series ◽  
Web Services ◽  
Time Series Forecasting

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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