首页|Reports Summarize Machine Learning Findings from Pennsylvania State University ( Penn State) (The Development of a Featuredriven Analytical Approach for Gamma-r ay Spectral Analysis)
Reports Summarize Machine Learning Findings from Pennsylvania State University ( Penn State) (The Development of a Featuredriven Analytical Approach for Gamma-r ay Spectral Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting out of University Park, Pennsylvania , by NewsRx editors, research stated, “Gamma-ray spectroscopy is an essential to ol in nuclear science, nuclear security, and environmental monitoring. However, challenges arise in interpreting spectral data due to the presence of low counts , multiple sources, and dynamic backgrounds.” Financial supporters for this research include Defense Threat Reduction Agency ( DTRA) as part of the Interaction of Ionizing Radiation, Matter University Resear ch Alliance (IIRM-URA). Our news journalists obtained a quote from the research from Pennsylvania State University (Penn State), “To address these issues, a novel feature-driven analyt ical approach for gamma-ray spectral analysis using machine-learning techniques is developed. The method utilizes a series of random forest models for in-distri bution (ID) multi-label classification, and the model-derived feature importance values to guide the out-of-distribution (OOD) detection task. The performance o f this approach is quantitatively evaluated across various spectral parameters, including acquisition time, number of sources, energy of an OOD source, and back ground composition. Increasing the acquisition time from 1 s to 100 s leads to i mproved performance for multi-label classification, with 22 sources achieving F1 -scores >= 0.9 after 50 s acquisitions for a CLLBC handh eld detector and a standoff distance of 30 cm. The feature-driven analytical app roach also demonstrates robustness when handling complex source mixtures. Furthe rmore, it provides contextual energetic information for OOD detection. The resul ts presented here highlight the interpretability of the approach, establishing c lear links between the spectral features and underlying physics. Moreover, the a pproach effectively distinguishes overlapping spectral signatures of different I D gamma-ray sources, enhancing human reliability in machine learning-based gamma -ray spectral analysis.”
University ParkPennsylvaniaUnited St atesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learnin gPennsylvania State University (Penn State)
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