scholarly journals Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

2020 ◽  
Vol 124 (33) ◽  
pp. 18302-18315 ◽  
Author(s):  
Nathan D. Bamberger ◽  
Jeffrey A. Ivie ◽  
Keshaba N. Parida ◽  
Dominic V. McGrath ◽  
Oliver L. A. Monti
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Analysis Tool ◽  
Break Junction ◽  
Unsupervised Segmentation ◽  
Advanced Analysis

scholarly journals Correction to “Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data”

2020 ◽  
Vol 124 (43) ◽  
pp. 24029-24031
Author(s):  
Nathan D. Bamberger ◽  
Jeffrey A. Ivie ◽  
Keshaba N. Parida ◽  
Dominic V. McGrath ◽  
Oliver L. A. Monti
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Analysis Tool ◽  
Break Junction ◽  
Unsupervised Segmentation ◽  
Advanced Analysis

scholarly journals Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4818
Author(s):  
Nils Mandischer ◽  
Tobias Huhn ◽  
Mathias Hüsing ◽  
Burkhard Corves
Keyword(s):  
Machine Learning ◽  
Shop Floor ◽  
Unsupervised Segmentation ◽  
User Friendliness ◽  
Industrial Environment ◽  
Machine Learning Methods ◽  
Eu Project ◽  
Detection And Localization ◽  
Industrial Context ◽  
The Eu

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at 0.85 Hz.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

Measurement and control of detailed electronic properties in a single molecule break junction

2014 ◽  
Vol 174 ◽  
pp. 91-104 ◽  
Author(s):  
Kun Wang ◽  
Joseph Hamill ◽  
Jianfeng Zhou ◽  
Cunlan Guo ◽  
Bingqian Xu
Keyword(s):  
Data Analysis ◽  
Molecular Electronics ◽  
Single Molecule ◽  
Molecular Orbitals ◽  
Break Junction ◽  
Break Junctions ◽  
Single Molecule Level ◽  
Analysis Techniques ◽  
Future Design ◽  
Molecule Junction

The lack of detailed experimental controls has been one of the major obstacles hindering progress in molecular electronics. While large fluctuations have been occurring in the experimental data, specific details, related mechanisms, and data analysis techniques are in high demand to promote our physical understanding at the single-molecule level. A series of modulations we recently developed, based on traditional scanning probe microscopy break junctions (SPMBJs), have helped to discover significant properties in detail which are hidden in the contact interfaces of a single-molecule break junction (SMBJ). For example, in the past we have shown that the correlated force and conductance changes under the saw tooth modulation and stretch–hold mode of PZT movement revealed inherent differences in the contact geometries of a molecular junction. In this paper, using a bias-modulated SPMBJ and utilizing emerging data analysis techniques, we report on the measurement of the altered alignment of the HOMO of benzene molecules with changing the anchoring group which coupled the molecule to metal electrodes. Further calculations based on Landauer fitting and transition voltage spectroscopy (TVS) demonstrated the effects of modulated bias on the location of the frontier molecular orbitals. Understanding the alignment of the molecular orbitals with the Fermi level of the electrodes is essential for understanding the behaviour of SMBJs and for the future design of more complex devices. With these modulations and analysis techniques, fruitful information has been found about the nature of the metal–molecule junction, providing us insightful clues towards the next step for in-depth study.

AStrap: identification of alternative splicing from transcript sequences without a reference genome

2018 ◽  
Vol 35 (15) ◽  
pp. 2654-2656 ◽  
Author(s):  
Guoli Ji ◽  
Wenbin Ye ◽  
Yaru Su ◽  
Moliang Chen ◽  
Guangzao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Reference Genome ◽  
De Novo ◽  
Supplementary Information ◽  
Model Organisms ◽  
Sequencing Data ◽  
Extensive Evaluation ◽  
Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Unsupervised feature recognition in single-molecule break junction data

Nanoscale ◽  
2020 ◽  
Vol 12 (15) ◽  
pp. 8355-8363 ◽  
Author(s):  
András Magyarkuti ◽  
Nóra Balogh ◽  
Zoltán Balogh ◽  
Latha Venkataraman ◽  
András Halbritter
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Single Molecule ◽  
Feature Recognition ◽  
Principal Component ◽  
Break Junction ◽  
Neural Network Analysis ◽  
Efficient Tool

A combined principal component and neural network analysis serves as an efficient tool for the unsupervised recognition of unobvious but highly relevant trace classes in single-molecule break junction data.

A Bayesian Based Machine Learning Application to Task Analysis

2012 ◽  
pp. 234-242
Author(s):  
Shu-Chiang Lin
Keyword(s):  
Machine Learning ◽  
Task Analysis ◽  
Analysis Tool ◽  
Analysis Techniques ◽  
Bayesian Methodology ◽  
The Past ◽  
Expensive Process

Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.

scholarly journals Photo-induced carbocation-enhanced charge transport in single-molecule junctions

2020 ◽  
Vol 11 (23) ◽  
pp. 6026-6030
Author(s):  
Zhongwu Bei ◽  
Yuan Huang ◽  
Yangwei Chen ◽  
Yiping Cao ◽  
Jin Li
Keyword(s):  
Scanning Tunneling Microscopy ◽  
Charge Transport ◽  
Single Molecule ◽  
Scanning Tunneling ◽  
Break Junction ◽  
Tunneling Microscopy ◽  
Single Molecule Junctions

We report the first example of photo-induced carbocation-enhanced charge transport in triphenylmethane junctions using the scanning tunneling microscopy break junction (STM-BJ) technique.

scholarly journals Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229041 ◽  
Author(s):  
Lucas Encarnacion-Rivera ◽  
Steven Foltz ◽  
H. Criss Hartzell ◽  
Hyojung Choo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Analysis Tool ◽  
Machine Learning Algorithm ◽  
Muscle Histology ◽  
Imagej Software
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