scholarly journals A workflow for characterizing nanoparticle monolayers for biosensors: Machine learning on real and artificial SEM images

2014 ◽  
Author(s):  
Adam Hughes ◽  
Zhaowen Liu ◽  
Maryam Raftari ◽  
Mark E Reeves
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Open Source Software ◽  
Materials Science ◽  
Sampling Bias ◽  
Supervised Machine Learning ◽  
Sem Images ◽  
Nanoscale Features ◽  
Nanoparticle Film

A persistent challenge in materials science is the characterization of a large ensemble of heterogeneous nanostructures in a set of images. This often leads to practices such as manual particle counting, and sampling bias of a favorable region of the “best” image. Herein, we present the open-source software, imaging criteria and workflow necessary to fully characterize an ensemble of SEM nanoparticle images. Such characterization is critical to nanoparticle biosensors, whose performance and characteristics are determined by the distribution of the underlying nanoparticle film. We utilize novel artificial SEM images to objectively compare commonly-found image processing methods through each stage of the workflow: acquistion, preprocessing, segmentation, labeling and object classification. Using the semi- supervised machine learning application, Ilastik, we demonstrate the decomposition of a nanoparticle image into particle subtypes relevant to our application: singles, dimers, flat aggregates and piles. We outline a workflow for characterizing and classifying nanoscale features on low-magnification images with thousands of nanoparticles. This work is accompanied by a repository of supplementary materials, including videos, a bank of real and artificial SEM images, and ten IPython Notebook tutorials to reproduce and extend the presented results.

scholarly journals A workflow for characterizing nanoparticle monolayers for biosensors: Machine learning on real and artificial SEM images

2014 ◽  
Author(s):  
Adam S Hughes ◽  
Zhaowen Liu ◽  
Maryam Raftari ◽  
Marke M. E. Reeves
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Open Source Software ◽  
Materials Science ◽  
Sampling Bias ◽  
Supervised Machine Learning ◽  
Sem Images ◽  
Nanoscale Features ◽  
Nanoparticle Film

A persistent challenge in materials science is the characterization of a large ensemble of heterogeneous nanostructures in a set of images. This often leads to practices such as manual particle counting, and sampling bias of a favorable region of the “best” image. Herein, we present the open-source software, imaging criteria and workflow necessary to fully characterize an ensemble of SEM nanoparticle images. Such characterization is critical to nanoparticle biosensors, whose performance and characteristics are determined by the distribution of the underlying nanoparticle film. We utilize novel artificial SEM images to objectively compare commonly-found image processing methods through each stage of the workflow: acquistion, preprocessing, segmentation, labeling and object classification. Using the semi- supervised machine learning application, Ilastik, we demonstrate the decomposition of a nanoparticle image into particle subtypes relevant to our application: singles, dimers, flat aggregates and piles. We outline a workflow for characterizing and classifying nanoscale features on low-magnification images with thousands of nanoparticles. This work is accompanied by a repository of supplementary materials, including videos, a bank of real and artificial SEM images, and ten IPython Notebook tutorials to reproduce and extend the presented results.

scholarly journals A workflow for characterizing nanoparticle monolayers for biosensors: Machine learning on real and artificial SEM images

2014 ◽  
Author(s):  
Adam Hughes ◽  
Zhaowen Liu ◽  
Maryam Raftari ◽  
Mark E Reeves
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Open Source Software ◽  
Materials Science ◽  
Sampling Bias ◽  
Supervised Machine Learning ◽  
Sem Images ◽  
Nanoscale Features ◽  
Nanoparticle Film

A persistent challenge in materials science is the characterization of a large ensemble of heterogeneous nanostructures in a set of images. This often leads to practices such as manual particle counting, and sampling bias of a favorable region of the “best” image. Herein, we present the open-source software, imaging criteria and workflow necessary to fully characterize an ensemble of SEM nanoparticle images. Such characterization is critical to nanoparticle biosensors, whose performance and characteristics are determined by the distribution of the underlying nanoparticle film. We utilize novel artificial SEM images to objectively compare commonly-found image processing methods through each stage of the workflow: acquistion, preprocessing, segmentation, labeling and object classification. Using the semi- supervised machine learning application, Ilastik, we demonstrate the decomposition of a nanoparticle image into particle subtypes relevant to our application: singles, dimers, flat aggregates and piles. We outline a workflow for characterizing and classifying nanoscale features on low-magnification images with thousands of nanoparticles. This work is accompanied by a repository of supplementary materials, including videos, a bank of real and artificial SEM images, and ten IPython Notebook tutorials to reproduce and extend the presented results.

scholarly journals Delving into Android Malware Families with a Novel Neural Projection Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rafael Vega Vega ◽  
Héctor Quintián ◽  
Carlos Cambra ◽  
Nuño Basurto ◽  
Álvaro Herrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Projection Method ◽  
Hebbian Learning ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Learning Techniques ◽  
First Time

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

scholarly journals vRhyme enables binning of viral genomes from metagenomes

2021 ◽  
Author(s):  
Kristopher Kieft ◽  
Alyssa Adams ◽  
Rauf Salamzade ◽  
Lindsay Kalan ◽  
Karthik Anantharaman
Keyword(s):  
Machine Learning ◽  
Nitrate Reductase ◽  
Human Skin ◽  
Effect Size ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Viral Ecology ◽  
Viral Genomes ◽  
Complete Genomes

Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, no approach exists for viruses. We developed vRhyme, a fast and precise software for construction of viral metagenome-assembled genomes (vMAGs). vRhyme utilizes single- or multi-sample coverage effect size comparisons between scaffolds and employs supervised machine learning to identity nucleotide feature similarities, which are compiled into iterations of weighted networks and refined bins. Using simulated viromes, we displayed superior performance of vRhyme compared to available binning tools in constructing more complete and uncontaminated vMAGs. When applied to 10,601 viral scaffolds from human skin, vRhyme advanced our understanding of resident viruses, highlighted by identification of a Herelleviridae vMAG comprised of 22 scaffolds, and another vMAG encoding a nitrate reductase metabolic gene, representing near-complete genomes post-binning. vRhyme will enable a convention of binning uncultivated viral genomes and has the potential to transform metagenome-based viral ecology.

scholarly journals Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals

2021 ◽  
Author(s):  
Kazuki Morita ◽  
Daniel Davies ◽  
Keith Butler ◽  
Aron Walsh
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Supervised Machine Learning ◽  
High Symmetry ◽  
Thermally Activated ◽  
Structural Distortions ◽  
Abo3 Perovskite ◽  
Efficient Machine ◽  
Perovskite Type ◽  
Special Case

While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach.

scholarly journals Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

2022 ◽  
Vol 12 ◽  
Author(s):  
Jana Ebersbach ◽  
Nazifa Azam Khan ◽  
Ian McQuillan ◽  
Erin E. Higgins ◽  
Kyla Horner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Breeding Population ◽  
Supervised Machine Learning ◽  
Oilseed Crop

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

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