Dual Information Technology Strategy and Firm Performance: Insights from a Deep Learning Approach

2021 ◽  
Vol 2021 (1) ◽  
pp. 12694
Author(s):  
Yi Yang ◽  
Chewei Liu ◽  
Sunil Mithas
Author(s):  
Rushali Deshmukh, Et. al.

People have a tendency to analyze existing strategies and so planned new strategies for inventory prediction. We have used Sentiment evaluation and Technical evaluation through NLP and Deep mastering approach. In order to exploit benefits of sentiment analysis on enterprise associated inventory, we have proposed a model that will use the sentiment analysis on twits associated with special sectors that are Information Technology sector, Banking sector, Pharmaceutical sector, Automobile sector, Infrastructure sector which are extracted from twitter. These twits are extracted from twitter for calculating polarity. The rating of sentiment analysis is calculated here by using Natural Language Processing’s method. According to sector we've taken five groups. Top four performer businesses of every sector. Using polarity score we got finalized pinnacle ten groups with great sentiment rating. We then downloaded the CSV facts of historical share charge of top ten organizations that we've selected. Then downloaded CSV records are used to build a CNN version to predict in addition stock movement of these pinnacle ten companies.


MIS Quarterly ◽  
2015 ◽  
Vol 39 (4) ◽  
pp. 809-836 ◽  
Author(s):  
Rajiv Sabherwal ◽  
◽  
Anand Jeyaraj ◽  

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.


2007 ◽  
Author(s):  
Ali R. Tafti ◽  
Sunil Mithas ◽  
Mayuram S. Krishnan

Sign in / Sign up

Export Citation Format

Share Document