scholarly journals Automated detection and sorting of microencapsulation via machine learning

Lab on a Chip ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 1808-1817 ◽  
Author(s):  
Albert Chu ◽  
Du Nguyen ◽  
Sachin S. Talathi ◽  
Aaron C. Wilson ◽  
Congwang Ye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Production System ◽  
Automated Detection ◽  
Capsule Production ◽  
System P

We automated a traditionally labor-intensive, yet widely-used capsule production system.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Automated Detection of Racial Microaggressions using Machine Learning

2020 ◽  
Author(s):  
Omar Ali ◽  
Nancy Scheidt ◽  
Alexander Gegov ◽  
Ella Haig ◽  
Mo Adda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Racial Microaggressions ◽  
Automated Detection

scholarly journals Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
ShirYing Lee ◽  
CrystalM E Chen ◽  
ElaineY P Lim ◽  
Liang Shen ◽  
Aneesh Sathe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Automated Detection ◽  
Blood Smears ◽  
Rare Cells

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning

2018 ◽  
Vol 307 ◽  
pp. 53-59 ◽  
Author(s):  
Joshua Levitt ◽  
Adam Nitenson ◽  
Suguru Koyama ◽  
Lonne Heijmans ◽  
James Curry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automated Detection

Automated Detection of Valve Degradation using Machine Learning

2019 ◽  
Vol 3 (sup1) ◽  
pp. 101-101
Author(s):  
Bernhard Vennemann ◽  
Dominik Obrist ◽  
Thomas Rösgen
Keyword(s):  
Machine Learning ◽  
Automated Detection

scholarly journals Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

2018 ◽  
Vol 7 (3.34) ◽  
pp. 61
Author(s):  
R Srividhya ◽  
K Shanmugapriya ◽  
K Sindhu Priya
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Surface Defects ◽  
Processing Method ◽  
Automated Detection ◽  
Image Construction ◽  
Good Classification ◽  
Industrial Materials ◽  
Manual Calculation ◽  
Visible Defect

In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.  

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