首页|Research Results from Texas A&M University Update Understanding of Machine Learning (Exploring the Impact of the NULL Class on In-The-Wild Human Ac tivity Recognition)
Research Results from Texas A&M University Update Understanding of Machine Learning (Exploring the Impact of the NULL Class on In-The-Wild Human Ac tivity Recognition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting originating from Col lege Station, Texas, by NewsRx correspondents, research stated, “Monitoring acti vities of daily living (ADLs) plays an important role in measuring and respondin g to a person’s ability to manage their basic physical needs.” Financial supporters for this research include National Science Foundation. Our news reporters obtained a quote from the research from Texas A& M University: “Effective recognition systems for monitoring ADLs must successful ly recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges ass ociated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of prep rocessing techniques to increase recall and postprocessing techniques to increas e precision can result in more desirable models for tasks such as ADL monitoring .”
Texas A&M UniversityColle ge StationTexasUnited StatesNorth and Central AmericaCyborgsEmerging T echnologiesMachine Learning
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