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PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257884
Author(s):  
Lei Rigi Baltazar ◽  
Mojhune Gabriel Manzanillo ◽  
Joverlyn Gaudillo ◽  
Ethel Dominique Viray ◽  
Mario Domingo ◽  
...  

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.


2021 ◽  
Author(s):  
Andrew Agnew

<p>The actor director task (DT) has been used extensively to assess differences in perspective taking ability. Previous studies have found that individuals from collectivist cultures outperform those from individualist cultures in the DT. The current study uses an online form of the DT to assess individuals from European, New Zealand Pasifika and Māori cultural groups. Pasifika and Māori cultures tend to be categorised as collectivist, but have theory of mind norms that differ from previously assessed collectivist cultures. It is hypothesised that these norms will advantage Pasifika in the DT but not Māori. No significant differences are found in performance on the DT across all three cultural groups. All three groups replicated general performance on the DT in previous studies.</p>


2021 ◽  
Author(s):  
Andrew Agnew

<p>The actor director task (DT) has been used extensively to assess differences in perspective taking ability. Previous studies have found that individuals from collectivist cultures outperform those from individualist cultures in the DT. The current study uses an online form of the DT to assess individuals from European, New Zealand Pasifika and Māori cultural groups. Pasifika and Māori cultures tend to be categorised as collectivist, but have theory of mind norms that differ from previously assessed collectivist cultures. It is hypothesised that these norms will advantage Pasifika in the DT but not Māori. No significant differences are found in performance on the DT across all three cultural groups. All three groups replicated general performance on the DT in previous studies.</p>


2021 ◽  
Vol 3 (1) ◽  
pp. 101-110
Author(s):  
Taufiq Ridwan Murtadho ◽  
Rizky Ridwansyah

Along with the era, sharia banking is now soaring. With a system that implements revenue sharing and free from maisyir elements. So the community began to switch to keep the money and conduct other transactions at sharia banks. Because of the increasing number of Sharia banks, every bank is required to be able to maintain the level of health of the bank. Because this becomes the foundation of whether the bank is able or not to manage funds from customers and this becomes the main foundation of stakeholder trust. In the process of assessing the health of the bank there are several provisions that have been set in writing by Bank Indonesia, so that anyone can easily analyze, one of them by using camel method. Because the assessment of the bank's health level becomes a benchmark to evaluate the management of the bank. The purpose of this study is to analyze the level of health of banks using camel method in PT. Bank Panin Dubai Syariah period 2016-2019, the overall results of the evaluation in general performance and health in fairly healthy condition.Keywords: Health, Bank, CAMEL


2021 ◽  
Author(s):  
Zhongzheng Fu ◽  
Danielle Beam ◽  
Jeffrey M. Chung ◽  
Chrystal M. Reed ◽  
Adam N. Mamelak ◽  
...  

Languages ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 64
Author(s):  
Yong-cheol Lee ◽  
Sunghye Cho

Production and perception experiments were conducted to examine whether focus prosody varies by phrase-initial tones in Seoul Korean. We also trained an automatic classifier to locate prosodic focus within a sentence. Overall, focus prosody in Seoul Korean was weak and confusing in production, and poorly identified in perception. However, Seoul Korean’s focus prosody differed between phrase-initial low and high tones. The low tone group induced a smaller pitch increase by focus than the high tone group. The low tone group was also subject to a greater degree of confusion, although both tone groups showed some degree of confusion spanning the entire phrase as a focus effect. The identification rate was, therefore, approximately half in the low tone group (23.5%) compared to the high tone group (40%). In machine classification, the high tone group was also more accurately identified (high: 86% vs. low: 68%) when trained separately, and the machine’s general performance when the two tone groups were trained together was much superior to the human’s (machine: 65% vs. human: 32%). Although the focus prosody in Seoul Korean was weak and confusing, the identification rate of focus was higher under certain circumstances, which avers that focus prosody can vary within a single language.


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