首页|Investigators at University of Pittsburgh Report Findings in Machine Learning (P erformance of Unmarked Abundance Models With Data From Machine-learning Classifi cation of Passive Acoustic Recordings)
Investigators at University of Pittsburgh Report Findings in Machine Learning (P erformance of Unmarked Abundance Models With Data From Machine-learning Classifi cation of Passive Acoustic Recordings)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; A new study on Machine Learning is now available. According to news reporting fromPittsburgh, Pennsylvania, by NewsRx journalists, research stated, “The ability to conduct cost-effectivewildlife m onitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recordingunits (ARUs) and automated species recognition, presenting a variety of advantages over human-basedsurveys. However, estimating abundance with such data collection techniques remains challenging becausemost abundance models require data that are difficult for low-cost monoaural ARUs to gather (e. g.,counts of individuals, distance to individuals), especially when using the o utput of automated speciesrecognition.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUn iversity of Pittsburgh
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