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ARTIFICIAL INTELLIGENCE AI PEER REVIEW The quantification of visual affect data (i.e., face images) is essential to
ARTIFICIAL INTELLIGENCE | MACHINE LEARNING MULTIMODAL ASSESSMENT OF NEONATAL PAIN USING COMPUTER VISION
build and monitor automated affect modeling systems efficiently. John Licato, PhD
A robust and comprehensive automatic system that generates a standardized pain assessment comparable to
ARTIFICIAL INTELLIGENCE
those obtained by conventional nurse-derived pain scores. Dmitry Goldgof, PhD
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TEMPORAL EXPRESSIVENESS DYNAMICS The quantification of visual affect data (i.e., face images) is essential to build and monitor automated affect modeling systems efficiently.
ARTIFICIAL INTELLIGENCE | MACHINE LEARNING
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USING AI AND ML TO ENHANCE REMOTE LEARNING
Shaun Canavan, PhD
ARTIFICIAL INTELLIGENCE USING AI AND DRONE MAPPING TO COMBAT MALARIA
Using online remote instruction to extend education beyond school campuses has been growing steadily since the turn of Autar Kaw, PhD
increasingly deployed as a response to the
To combat the spread of malaria in developing regions
COVID-19 global pandemic that has made
such as sub-Saharan Africa, Sriram Chellappan, professor
classrooms risky places to be.
of computer science and engineering, is developing Sriram Chellappan, PhD
drone mapping technology to locate habitats of malaria
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spreading mosquitoes.
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the 21st Century and recently has been
Alci Yalcin, PhD
MACHINE LEARNING EVENT UNDERSTANDING Video data captures a tremendous amount of data that encompasses both visual and semantic knowledge that representations require reasoning
MACHINE LEARNING REAL-TIME ANOMALY DETECTION
about the semantic relationships among these Sudeep Sarkar, PhD
concepts, often beyond what is directly observed in
Detect changes in data patterns with respect to a
the data.
baseline.
Yasin Yilmaz, PhD
MACHINE LEARNING THIRTY YEARS OF MACHINE LEARNING: THE ROAD TO ARTIFICIAL INTELLIGENCE PARETO-OPTIMAL WIRELESS NETWORKS Project Description Kwang-Cheng Chen, PhD
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MACHINE LEARNING
Kwang-Cheng Chen, PhD
MACHINE LEARNING PARADIGMS FOR NEXT-GENERATION WIRELESS NETWORKS Project Description
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