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# A Bayesian Network-based Uncertainty Modeling (BNUM) to Analyze and Predict Next Optimal

As machine learning emerged, it is being used in a variety of applications like speech recognition, image
recognition, sequence modeling, etc., Sequence modeling is one type of application where resultant sequences are generated
based on historical data inputs provided. These sequences are fairly work in an uncertain environment like games or sports. In
the case of a game or a sport, there is a sequence of moves selected by multiple players. There is a statistical uncertainty
observed for simple to more complex games. For example, while playing chess, a simple statistical modeled uncertainty would
be enough to choose the next possible. This move selection is dependent on available free spaces of pieces or pawns. The
sports like tennis, cricket, and other games need a more complex design for uncertainty modeling for next move selection. A
Bayesian Network model will work if there is fairly less uncertainty in the selection of the next move. A Bayesian Network-
based model will be best fitted if all possible moves are included before training any machine learning or deep learning model.
This will be achieved with the usage of the Context-Li model. The proposed Bayesian Network-based Uncertainty Modeling
(BNUM) is used to incorporate uncertainty, for next move selection. BNUM is a multi-variable, multi-level association to
incubate uncertainty in learning. It helps to predict the next move in an uncertain gaming environment. Different case studies
are incorporated to verify the hypothesis and the results are a sequence of moves represented in the context graph.

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[27] Zichermann G. and Cunningham C., Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps, O'Reilly Media, 2011. A Bayesian Network-based Uncertainty Modeling (BNUM) to Analyze and Predict Next Optimal ... 205 Vinayak Jagtap is a passionate researcher in the field of computer science. His research areas include data science, machine learning, and data security. Vinayak holds a Bachelor of Engineering from Walchand College of Engineering, a Master in Engineering from Pune Institute of Computer Technology, and currently pursuing his Ph.D. from the College of Engineering Pune. He has a couple of patents filed along with dozen publications. Parag Kulkarni is an entrepreneur, Machine Learning researcher, and author of best- selling Innovation strategy, ML, and Data science books. An avid reader, Parag holds Bachelors from Walchand College of Engineering Sangli (1990), a Ph.D. from IIT Kharagpur (2001), management education from IIM Kolkata, and was conferred a higher doctorate DSc by UGSM monarch, Switzerland (2010). Parag helped underperforming professionals, start-ups, and students to transform into happy and passionate warriors. Fellow of the IET, IETE, and senior member IEEE, Parag is the recipient of the Oriental Foundation Scholarship, distinguished alumnus award WCE - Sangli, and was nominated for the prestigious Bhatnagar award in 2013 and 2014. He was also awarded IETE-KR Phadke award for innovative entrepreneurship and research in 2019. Parag has published over 300+ research papers and articles in peer-reviewed journals and renowned conferences. He invented over a dozen patents and authored 14 books (with the world’s best technical and business publishers like Bloomsbury, IEEE, Wiley, Prentice Hall, Springer, Oxford University Press, etc.).