scholarly journals Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices

ACS Omega ◽  
2021 ◽  
Author(s):  
Bidur Khanal ◽  
Pravin Pokhrel ◽  
Bishesh Khanal ◽  
Basant Giri
Keyword(s):  
Machine Learning ◽  
Colorimetric Assays ◽  
Assisted Analysis

scholarly journals Machine Learning-Assisted Analysis of Small Angle X-ray Scattering

2021 ◽  
Author(s):  
Piotr Tomaszewski ◽  
Shun Yu ◽  
Markus Borg ◽  
Jerk Ronnols
Keyword(s):  
Machine Learning ◽  
Small Angle ◽  
X Ray ◽  
X Ray Scattering ◽  
Assisted Analysis ◽  
Ray Scattering

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Computer-Assisted Analysis of Multi-Color Confocal Microscopic Datasets: Automated Light Microscopic Discrimination of Synaptic Relationships

1996 ◽  
Vol 54 ◽  
pp. 284-285
Author(s):  
M Wessendorf ◽  
A Beuning ◽  
D Cameron ◽  
J Williams ◽  
C Knox
Keyword(s):  
Nerve Fibers ◽  
Confocal Scanning Laser Microscopy ◽  
Computer Assisted ◽  
Nerve Terminals ◽  
Neuronal Somata ◽  
Scanning Laser Microscopy ◽  
Confocal Scanning ◽  
Entire Cell ◽  
Confocal Scanning Laser ◽  
Assisted Analysis

Multi-color confocal scanning-laser microscopy (CSLM) allows examination of the relationships between neuronal somata and the nerve fibers surrounding them at sub-micron resolution in x,y, and z. Given these properties, it should be possible to use multi-color CSLM to identify relationships that might be synapses and eliminate those that are clearly too distant to be synapses. In previous studies of this type, pairs of images (e.g., red and green images for tissue stained with rhodamine and fluorescein) have been merged and examined for nerve terminals that appose a stained cell (see, for instance, Mason et al.). The above method suffers from two disadvantages, though. First, although it is possible to recognize appositions in which the varicosity abuts the cell in the x or y axes, it is more difficult to recognize them if the apposition is oriented at all in the z-axis—e.g., if the varicosity lies above or below the neuron rather than next to it. Second, using this method to identify potential appositions over an entire cell is time-consuming and tedious.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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