首页|Shaanxi Normal University Reports Findings in Machine Learning (Automated extrac tion of Tridacna shell growth patterns via machine learning for enhanced paleocl imate/paleoweather research)
Shaanxi Normal University Reports Findings in Machine Learning (Automated extrac tion of Tridacna shell growth patterns via machine learning for enhanced paleocl imate/paleoweather research)
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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 out of Xi’an, People’s Republ ic of China, by NewsRx editors, research stated, “Tridacna spp. are valuable arc hives for paleoclimate and paleoweather research due to their distinct daily gro wth patterns and the sensitivity of the daily growth patterns to environment cha nges. However, manually identifying daily growth lines and measuring the daily g rowth increment width (DGIW) of Tridacna shells from Laser Scanning Confocal Mic roscopy (LSCM) images is a tedious task that has become a significant barrier to Tridacna studies.” Our news journalists obtained a quote from the research from Shaanxi Normal Univ ersity, “This paper addresses this challenge by integrating machine learning int o Tridacna research for the first time to automate the calculation of the number of daily growth lines and DGIW of Tridacna shells. Specifically, we propose an unsupervised generative adversarial attention network called TriGAN to automatic ally recognize distinct daily growth lines of Tridacna shells from LSCM images. Utilizing modern Tridacna specimens collected from the South China Sea, our expe rimental results demonstrate that TriGAN can effectively reconstruct the ambiguo us and blurred regions in LSCM images and produce higher quality images of daily growth patterns compared to existing image generation networks. Furthermore, th e daily growth line number and DGIW of Tridacna shells can be counted automatica lly from the images recognized by TriGAN, which are in good agreement with the s tatistical results obtained manually from the original LSCM images (R = 0.7, p<0.01 for the DGIW profile of T. gigas specimen MD1 and R = 0.6, p<0.01 for T. derasa specimen XB10).”
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