JYOTISMITA CHAKI, PhD. is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Artificial Intelligence and Machine learning. She has authored and edited many international conferences, journal papers and books. Currently she is the editor of Engineering Applications of Artificial Intelligence Journal, Elsevier, academic editor of PLOS ONE journal and associate editor of Array journal, Elsevier, IET Image Processing, Applied Computational Intelligence and Soft Computing and Machine Learning with Applications journal, Elsevier.

• Introduction to handling uncertainty in artificial intelligence.- Probability and Bayesian Theory to Handle Uncertainty in artificial intelligence.- The Dempster-Shafer Theory to handle uncertainty in artificial intelligence.- Certainty factor and evidential reasoning to handle uncertainty in artificial intelligence.- A fuzzy logic-based approach to handle uncertainty in artificial intelligence.- Decision-making under uncertainty in artificial intelligence.- Applications of different methods to handle uncertainty in artificial intelligence.

JYOTISMITA CHAKI, PhD. is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Artificial Intelligence and Machine learning. She has authored and edited many international conferences, journal papers and books. Currently she is the editor of Engineering Applications of Artificial Intelligence Journal, Elsevier, academic editor of PLOS ONE journal and associate editor of Array journal, Elsevier, IET Image Processing, Applied Computational Intelligence and Soft Computing and Machine Learning with Applications journal, Elsevier.

• Introduction to handling uncertainty in artificial intelligence.- Probability and Bayesian Theory to Handle Uncertainty in artificial intelligence.- The Dempster-Shafer Theory to handle uncertainty in artificial intelligence.- Certainty factor and evidential reasoning to handle uncertainty in artificial intelligence.- A fuzzy logic-based approach to handle uncertainty in artificial intelligence.- Decision-making under uncertainty in artificial intelligence.- Applications of different methods to handle uncertainty in artificial intelligence.

JYOTISMITA CHAKI, PhD. is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Artificial Intelligence and Machine learning. She has authored and edited many international conferences, journal papers and books. Currently she is the editor of Engineering Applications of Artificial Intelligence Journal, Elsevier, academic editor of PLOS ONE journal and associate editor of Array journal, Elsevier, IET Image Processing, Applied Computational Intelligence and Soft Computing and Machine Learning with Applications journal, Elsevier.

• Introduction to handling uncertainty in artificial intelligence.- Probability and Bayesian Theory to Handle Uncertainty in artificial intelligence.- The Dempster-Shafer Theory to handle uncertainty in artificial intelligence.- Certainty factor and evidential reasoning to handle uncertainty in artificial intelligence.- A fuzzy logic-based approach to handle uncertainty in artificial intelligence.- Decision-making under uncertainty in artificial intelligence.- Applications of different methods to handle uncertainty in artificial intelligence.