首页|Zhejiang Normal University Reports Findings in Machine Learning (POPs identifica tion using simple low-code machine learning)
Zhejiang Normal University Reports Findings in Machine Learning (POPs identifica tion using simple low-code machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Jinhua, People 's Republic of China, by NewsRx journalists, research stated, "Effectively ident ifying persistent organic pollutants (POPs) with extensive organic chemical data sets poses a formidable challenge but is of utmost importance. Leveraging machin e learning techniques can enhance this process, but previous models often demand ed advanced programming skills and high-end computing resources." The news reporters obtained a quote from the research from Zhejiang Normal Unive rsity, "In this study, we harnessed the simplicity of PyCaret, a Python-based pa ckage, to construct machine-learning models for POP screening based on 2D molecu lar descriptors. We compared the performance of these models against a deep conv olutional neural network (DCNN) model. Utilising minimal Python code, we generat ed several models that exhibited superior or comparable performance to the DCNN. The most outstanding performer, the Light Gradient Boosting Machine (LGBM), ach ieved an accuracy of 96.20 %, an AUC of 97.70 %, and a n F1 score of 82.58 %. This model outshone the DCNN model. Furtherm ore, it excelled in identifying POPs within the REACH PBT and compiled industria l chemical lists. Our findings highlight the accessibility and simplicity of PyC aret, requiring only a few lines of code, rendering it suitable for non-computin g professionals in environmental sciences. The ability of low code machine learn ing tools (e.g."
JinhuaPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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