A STUDY OF FIELD GEOTHERMAL POWER POTENTIAL & THERMODYNAMIC CONCEPTS OF PROSPECTIVE BINARY POWER CYCLE BASED GEOTHERMAL POWER PLANT FOR TATAPANI GEOTHERMAL FIELD.

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Abstract

Dyslexia is a neuro developmental reading disorder that degrades the speed and accuracy of word recogni- tion, and as a consequence, impedes reading fluency and text comprehension. Between 5 and 10 percent of the population are normally affected by it. It has long been known that the eye movements of dyslexic readers differ from those of typical readers. The dataset for this study has been taken from the dataset used by a similar study (Benfatto et al., 2016). The experiments reported by the authors are based on eye tracking data from 185 subjects participating in the Kronoberg reading development project, a longitudinal research project on reading development and reading disability in Swedish school children running between 1989 and 2010. For our present study, we use eye movement recordings made while the subjects were reading a short natural passage of text adapted to their age. Recordings were available for 185 subjects, 97 High Risk (HR) subjects (76 males and 21 females) and 88 Low Risk(LR) subjects (69 males and 19 females Machine learning based predictive model developed in this study employ feature set based on eye fixations and saccades parameters and can be used to give individual level diagnosis with high sensitivity and specificity. Using statistical cross-validation techniques on a sample of 97 dyslexic and 88 control subjects, we achieve a classification accuracy of over 96% with balanced levels of sensitivity and specificity. Diagnostic follow-up of a screening result is always necessary so that intervention strategies can be personalized. Nevertheless, early identification of individuals in need of support is the first important step in this process and using eye tracking along with this system during reading may prove very useful. The system’s accuracy can be further enhanced by collecting a larger sample and then training these and other classification models.
塔塔帕尼地热田地热发电潜力及前景二元动力循环地热电厂热力概念研究。
阅读障碍是一种神经发育性阅读障碍,它会降低单词识别的速度和准确性,从而阻碍阅读的流畅性和对文本的理解。5%到10%的人口通常会受到影响。人们早就知道,诵读困难的读者的眼球运动不同于那些典型的读者。本研究的数据集取自类似研究使用的数据集(Benfatto et al., 2016)。作者报告的实验基于参与Kronoberg阅读发展项目的185名受试者的眼动追踪数据。Kronoberg阅读发展项目是1989年至2010年间进行的一项关于瑞典学童阅读发展和阅读障碍的纵向研究项目。在我们目前的研究中,我们使用了受试者在阅读一段适合他们年龄的自然短文时的眼动记录。185名受试者,97名高风险(HR)受试者(76名男性和21名女性)和88名低风险(LR)受试者(69名男性和19名女性)可获得记录。本研究开发的基于机器学习的预测模型采用基于眼睛注视和扫视参数的特征集,可用于具有高灵敏度和特异性的个体水平诊断。使用统计交叉验证技术,对97名阅读困难患者和88名对照受试者进行分类,我们实现了超过96%的分类准确率,并平衡了敏感性和特异性水平。筛查结果的诊断性随访总是必要的,因此干预策略可以个性化。然而,早期识别需要支持的个体是这个过程中重要的第一步,在阅读过程中使用眼动追踪和这个系统可能会非常有用。通过收集更大的样本,然后训练这些和其他分类模型,可以进一步提高系统的准确性。
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