Zair Bouzidi – LIMPAF Laboratory, Computer Science Dept, Faculty of Science and Applied Science, Bouira University,
Abdelmalek Boudries – Laboratory LMA, Commercial Science Dept, Faculty of Economics, Business and Management, Bejaia
University, Algeria
Mourad Amad – LIMPAF Laboratory, Computer Science Dept, Faculty of Science and Applied Science, Bouira University,


5th International Scientific Conference – EMAN 2021 – Economics and Management: How to Cope With Disrupted Times, Online/Virtual, March 18, 2021, CONFERENCE PROCEEDINGS published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-43-1, ISSN 2683-4510


This proposed model is based on a deep recurrent neural network trained with Long Short-
Term Memory Network (LSTM), used because of its ability to learn long term dependencies, taking the
concatenated function and Financial data as input, while integrating encapsulations, using Smart Data
and retrieving information by combining multiple search results (all the Web). It combines representation
training with financial data while integrating encapsulations from multiple sources and retrieving
information by combining multiple search results. It provides some good ideas that we have extended
to improve Corporate Marketing and Business Strategies. We show that the proposed model learns to
localize and recognize different aspects of Corporate Marketing and Business Strategies. We evaluate
it on the challenging task of detecting Fraud in Financial Services and Financial Time Series Forecasting
and show that it is more accurate than the state-of-the-art of other neural networks and that it uses
fewer parameters and less computation.


Business, Marketing, Forecasting of financial times series, Fraud detection, LSTM, Smart Data.


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