摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据Newsrx记者在巴西Uberlandia的新闻报道，研究表明，“人类遗骸的生物学性别调查是体质人类学的一个关键方面。然而，由于骨骼保存的状态不同，需要探索多种方法和有趣的结构。”新闻记者从Uberlandia联邦大学的研究中获得了一句话，“本研究旨在调查双额宽(FMB)，眶下孔距离(IOD)，鼻宽(NLB)，犬间宽度(ICD)，通过传统统计方法和开放式机器学习工具进行性别预测，获得伦理委员会的伦理批准，在100次锥形束CT(CBCT)扫描中，选择54人，所有点都可见，另外选择10次测试来测试从学习样本中获得的预测因子，描述性分析测量值、标准差、标准差、应用T-Student检验和Mann-Whitney检验评价变量的性别差异,建立了Logistic回归方程,并对其进行了检验,结果表明测量值与个体性别之间存在一定的相关性。通过回归公式或基于Mac Hine学习的模型，这些模型可以导出并添加到软件或WebPAG ES中，考虑到这些方法，男性和女性的估计准确率分别高于80%和82%。
Abstract
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 from Uberlandia, Brazil, by N ewsRx journalists, research stated, "Investigation of the biological sex of huma n remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored." The news correspondents obtained a quote from the research from the Federal Univ ersity of Uberlandia, "This research aims to investigate the potential use of di stances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), n asal breadth (NLB), inter-canine width (ICD), and distance between mental forami na (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive an alysis of measurements, standard deviation, and standard error were obtained. T- student and Mann- Whitney tests were utilized to assess the sex differences withi n the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, a nd artificial neural networks machine-learning models. The results indicate a st rong correlation between the measurements and the sex of individuals. When combi ned, the measurements were able to predict sex using a regression formula or mac hine learning based models which can be exported and added to software or webpag es. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in th e test sample were accurately predicted by both statistical and machine-learning models."
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