Early autism identification: Examining the diagnostic time line in two community samples

2012 ◽  
Author(s):  
Mallory A. Brown ◽  
Kenya Talton ◽  
Laura Lee Mcintyre
Keyword(s):  
Time Line ◽  
Diagnostic Time

Is the Future the Right Time?

2010 ◽  
Vol 57 (4) ◽  
pp. 308-314 ◽  
Author(s):  
Marc Ouellet ◽  
Julio Santiago ◽  
Ziv Israeli ◽  
Shai Gabay
Keyword(s):  
Auditory Task ◽  
English Speakers ◽  
Visual Modality ◽  
Opposite Pattern ◽  
Time Line ◽  
Temporal Reference ◽  
Left Hand ◽  
Reading Direction ◽  
Mental Time Line ◽  
The Right

Spanish and English speakers tend to conceptualize time as running from left to right along a mental line. Previous research suggests that this representational strategy arises from the participants’ exposure to a left-to-right writing system. However, direct evidence supporting this assertion suffers from several limitations and relies only on the visual modality. This study subjected to a direct test the reading hypothesis using an auditory task. Participants from two groups (Spanish and Hebrew) differing in the directionality of their orthographic system had to discriminate temporal reference (past or future) of verbs and adverbs (referring to either past or future) auditorily presented to either the left or right ear by pressing a left or a right key. Spanish participants were faster responding to past words with the left hand and to future words with the right hand, whereas Hebrew participants showed the opposite pattern. Our results demonstrate that the left-right mapping of time is not restricted to the visual modality and that the direction of reading accounts for the preferred directionality of the mental time line. These results are discussed in the context of a possible mechanism underlying the effects of reading direction on highly abstract conceptual representations.

A Problem of Long - Term Digital Preservation: Difference in Humanities and Natural Sciences

2014 ◽  
Vol 3 ◽  
pp. 183-195
Author(s):  
Elena Macevičiūtė
Keyword(s):  
Social Sciences ◽  
Digital Preservation ◽  
Dynamic Activity ◽  
Private Companies ◽  
Time Line ◽  
Digital Documents ◽  
Whole Process ◽  
The Difference ◽  
Technological Science

The article deals with the requirements and needs for long-term digital preservation in different areas of scholarly work. The concept of long-term digital preservation is introduced by comparing it to digitization and archiving concepts and defined with the emphasis on dynamic activity within a certain time line. The structure of digital preservation is presented with regard to the elements of the activity as understood in Activity Theory. The life-cycle of digitization processes forms the basis of the main processing of preserved data in preservation archival system.The author draws on the differences between humanities and social sciences on one hand and natural and technological science on the other. The empirical data characterizing the needs for digital preservation within different areas of scholarship are presented and show the difference in approaches to long-term digital preservation, as well as differences in selecting the items and implementing the projects of digital preservation. Institutions and organizations can also develop different understanding of preservation requirements for digital documents and other objects.The final part of the paper is devoted to some general problems pertaining to the longterm digital preservation with the emphasis of the responsibility for the whole process of safe-guarding the cultural and scholarly heritage for the re-use of the posterior generations. It is suggested that the longevity of the libraries in comparison with much shorter life-span of private companies strengthens the claim of memory institutions to playing the central role in the long-term digital preservation.

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot

Real-time line segments detection based on graphic processor

2009 ◽  
Vol 29 (5) ◽  
pp. 1359-1361
Author(s):  
Tong ZHANG ◽  
Zhao LIU ◽  
Ning OUYANG
Keyword(s):  
Real Time ◽  
Time Line ◽  
Line Segments ◽  
Graphic Processor

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoguo Zhang ◽  
Dawei Wang ◽  
Jiang Shao ◽  
Song Tian ◽  
Weixiong Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Medical Centers ◽  
Global Pandemic ◽  
Diagnostic Time ◽  
Logistic Regression Classifier

AbstractSince its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

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