【24h】

Dyscalculia Detection Using Machine Learning

机译:使用机器学习检测肌萎缩症

获取原文

摘要

A great amount of research is going on in the detection of learning disabilities, but the detection of Dyscalculia remains a tedious and time-consuming task even today. Various tests are conducted to detect if the patient has Dyscalculia and each test has to be evaluated manually as the scores alone are not sufficient to determine it. In some cases, Curriculum-Based Tests [CB'Ps] or Wide Range Achievement Tests [WRAT] or both need to be conducted after analysis of the results of the Woodcock-Johnson Tests. As a collaborative project between the Department of Psychiatry B.Y.L. Nair Ch. Hospital and Department of Computer Engineering, Vivekanand Education Society's Institute of Technology a system is developed to help improve the detection of Dyscalculia. The Woodcock-Johnson Tests of Achievements are conducted by the doctors and the results of these tests determine the learning disability.
机译:关于学习障碍的检测正在进行大量研究,但是,即使到了今天,对肌萎缩症的检测仍然是一项繁琐而耗时的任务。进行了各种测试以检测患者是否患有肌萎缩症,并且每种测试都必须手动进行评估,因为仅凭分数不足以确定它。在某些情况下,需要在对Woodcock-Johnson测验的结果进行分析之后,再进行基于课程的测验[CB'Ps]或广泛成就测验[WRAT]。作为精神病学系之间的合作项目Nair Ch。 Vivekanand教育协会技术学院的医院和计算机工程系开发了一个系统,以帮助改善对Dyscalculia的检测。伍德考克-约翰逊成就测验由医生进行,这些测验的结果决定了学习障碍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号