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Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be used for asteroseismic analysis. These data sets are expected to significantly grow with future missions such as \textit{PLATO}, and efficient methods are therefore required to analyze these data rapidly. Here, we describe a machine learning algorithm that identifies red giants from the raw oscillation spectra and captures \textit{p} and \textit{mixed} mode parameters from the red-giant power spectra. We report algorithmic inferences for large frequency separation ($Δν$), frequency at maximum amplitude ($ν_{max}$), and period separation ($ΔΠ$) for an ensemble of stars. In addition, we have discovered $\sim$25 new probable red giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra analyzed by the method, among which four are binary candidates which appear to possess red-giant counterparts. To validate the results of this method, we selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages ranging from subgiants to red clumps, and compare inferences of $Δν$, $ΔΠ$, and $ν_{max}$ with estimates obtained using other techniques. The power of the machine-learning algorithm lies in its speed: it is able to accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on a modern computer (single core of the Intel Xeon Platinum 8280 CPU).