Extending emotional lexicon for improving the classification accuracy of Chinese film reviews

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

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Philip French, Notes from the Dream House: Selected Film Reviews, 1963–2013, eds Kersti French, Karl French, Patrick French and Sean French

Journal of British Cinema and Television ◽

10.3366/jbctv.2020.0513 ◽

2020 ◽

Vol 17 (1) ◽

pp. 141-143

Author(s):

Sergio Angelini

Keyword(s):

Film Reviews

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The Early Chinese Film Narrative

Contemporary Film Studies ◽

10.15751/cofis.2018.14.3.251 ◽

2018 ◽

Vol 14 (3) ◽

pp. 251-284

Author(s):

김서운 ◽

HyoIn Yi

Keyword(s):

Chinese Film

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Characterization of Ischemic Stroke in CT Images using Image Processing

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.405 ◽

2017 ◽

Vol 7 (9) ◽

pp. 18

Author(s):

Amal Alzain ◽

Suhaib Alameen ◽

Rani Elmaki ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Classification Accuracy ◽

Ct Images ◽

Gray Level ◽

Level Variation ◽

Interactive Data ◽

The Brain ◽

Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.88 ◽

2017 ◽

Vol 7 (7) ◽

pp. 8

Author(s):

Mona E. Elbashier ◽

Suhaib Alameen ◽

Caroline Edward Ayad ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Size Distribution ◽

Texture Analysis ◽

Classification Accuracy ◽

Ct Images ◽

Diabetic Patients ◽

Gray Level ◽

Grey Level ◽

Run Length ◽

Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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