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Send the proposals for the Special Sessions to socta2016@gmail.com with the following details:
Title of the special session:
Names of proposers:
Affiliations:
Email addresses:
Description of the special session/workshop:
 Author(s) are required to mention the name of the special session on the 1st page of the manuscript.
Selected Special Sessions
1. Artificial Intelligence for Predictive Analytics and better
Decision-Making in next Generation Computational Systems
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Title of the special session:
- Artificial Intelligence for Predictive Analytics and better Decision-Making in next Generation Computational Systems
Names of proposers:
- Dr. Hardeo Kumar Thakur; Dr. Neeraj; Dr. Dinesh Prasad Sahu
Affiliations:
- Bennett University, Gr. Noida(INDIA)
Email addresses:
- hardeokumar@gmail.com; Hardeo.Thakur@bennett.edu.in; neeraj@bennett.edu.in; Dinesh.Sahu@bennett.edu.in
Description of the special session/workshop:
In recent years, the high availability of data, expansion in computational and storage capacities, and rapid advancement of Artificial Intelligence (AI) techniques have had a huge impact in many industries. Ranging from education, insurance, and financial services to healthcare, communication, industries are finding benefits from predictive analytics for better decision making. Predictive analytics helps in increasing revenue via reducing risks and optimising operations. However, many challenges remain due to the domain specific and data challenges of volume, variety, velocity, veracity, and value that are limiting their wide scale adoption.
The special session on Artificial Intelligence for Predictive Analytics and Better DecisionMaking in next generation computational systems invites contributions that address these challenges and/or showcase the latest real-world applications and enable algorithmic advancements in Artificial Intelligence for prediction.
Topics of Interest:
-AI driven time series modelling and forecasting
-AI driven time series modelling and forecasting
-AI driven temporal data analysis, prediction, and forecasting
-Transfer learning for time series forecasting
-Transformer architectures for time series forecasting
-Neural network architectures for time series forecasting
-Deep neural networks for time series modelling
-Deep learning-based outcome prediction
-GANs for time series analysis
-Advanced Predictive analytics algorithms
