Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty
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machine learning↗
Engineering topics
Mudit Paliwal
Publications and source records attributed to Mudit Paliwal.
Comparing Deep Neural Network Architectures using Transfer Learning to Predict Cognitive States
Abstract is attached
neural network↗
Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty
The development of a synergetic system between humans and technology is a challenge that the scientific community has been facing for many years. Our aeronautic research aims to enhance this synergy between humans and machines through predictive human performance modeling for systems to mitigate high-risk situations. By being able to predict and anticipate human states, the crew monitoring system should be able to adjust and support the pilot for aviation safety. Our work focusing on attention-related human performance-limiting states (AHPLS) that impact a pilot’s performance and introduce high-risk catastrophic situations [1]. For example, AHPLS has been cited as a causal factor in more than 50% of all loss control in flight and thus contributes significantly toward commercial aviation fatalities [1, 2]. Cognitive state and its physiological fingerprint can be valuable information for this detecting AHPLS, but human cognitive state detection is still a major limitation for these crew monitoring systems.
machine learning↗
Comparing Deep Neural Network Architectures using Transfer-Learning to Predict Cognitive States
Explore the source record for details and available documents.
machine learning↗