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Index of Industrial Production (IIP) data is one of the important economic indicators that track the manufacturing activity of different sectors of an economy. In this paper, an attempt is made to forecast the IIP data using traditional and deep learning statistical approaches. The data from Apr-2012 to Feb-2020 is used for forecasting. The appropriate best model is evaluated by comparing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results of the study show that RNN is performing better than the other models i.e ARIMA (Traditional method), FFNN, and LSTM (ANN models). Therefore RNN model is used for forecasting. The forecasted values from Mar-2020 to Jun-2021 are compared with the actual IIP values and resulted in a clear decline in industrial production because of lockdown.

The main aim of the present study is to compare the keywords extracted from abstracts and full length text of scientific research papers. In addition to that, here, we compare Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to identify better performer for keyword extraction. This comparative study is divided into three levels, In the first level, scientific research articles on topics such as Indian Economic growth, GDP, Economic Slowdown etc. were collected and abstracts and full length text was extracted from the sources and pre-processed to remove the words and characters which were not useful to obtain the semantic structures or necessary patterns to make the meaningful corpus. In the second level, the pre-processed data were converted into a bag of words and numerical statistic TF-IDF (Term Frequency – Inverse Document Frequency) is used to assess how relevant a word is to a document in a corpus. In the third level, in order to study the feasibility of the Natural Language Processing (NLP) techniques, Latent Semantic analysis (LSA) and Latent Dirichlet Allocations (LDA) methods were applied over the resultant corpus.