scholarly journals Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

2022 ◽  
Vol 23 (2) ◽  
pp. 645
Author(s):  
Dmitry Tolmachev ◽  
Natalia Lukasheva ◽  
Ruslan Ramazanov ◽  
Victor Nazarychev ◽  
Natalia Borzdun ◽  
...  
Keyword(s):  
Deep Eutectic Solvents ◽  
Machine Learning Techniques ◽  
Structure And Properties ◽  
Physical Mechanisms ◽  
Learning Techniques ◽  
Fundamental Relationship ◽  
Computational Research ◽  
Technical Features ◽  
Biomass Transformation ◽  
Do So

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

scholarly journals Computer Simulations of Deep Eutectic Solvents. Challenges, Solutions, and Perspectives

2021 ◽  
Author(s):  
Dmitry Tolmachev ◽  
Natalia Lukasheva ◽  
Ruslan Ramazanov ◽  
Victor Nazarychev ◽  
Natalia Borzdun ◽  
...  
Keyword(s):  
Deep Eutectic Solvents ◽  
Machine Learning Techniques ◽  
Structure And Properties ◽  
Physical Mechanisms ◽  
Learning Techniques ◽  
Fundamental Relationship ◽  
Computational Research ◽  
Technical Features ◽  
Biomass Transformation ◽  
Do So

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can provide predictions, reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

scholarly journals Automated distant galaxy merger classifications from Space Telescope images using the Illustris simulation

2019 ◽  
Vol 486 (3) ◽  
pp. 3702-3720 ◽  
Author(s):  
Gregory F Snyder ◽  
Vicente Rodriguez-Gomez ◽  
Jennifer M Lotz ◽  
Paul Torrey ◽  
Amanda C N Quirk ◽  
...  
Keyword(s):  
Galaxy Evolution ◽  
Hubble Space Telescope ◽  
Machine Learning Techniques ◽  
Galaxy Mergers ◽  
Distant Galaxy ◽  
Space Telescope ◽  
James Webb Space Telescope ◽  
Learning Techniques ◽  
Merger Rate ◽  
Do So

ABSTRACT We present image-based evolution of galaxy mergers from the Illustris cosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we created approximately one million synthetic deep Hubble Space Telescope and James Webb Space Telescope images and measured common morphological indicators. Using the merger tree, we assess methods to observationally select mergers with stellar mass ratios as low as 10:1 completing within ±250 Myr of the mock observation. We confirm that common one- or two-dimensional statistics select mergers so defined with low purity and completeness, leading to high statistical errors. As an alternative, we train redshift-dependent random forests (RFs) based on 5–10 inputs. Cross-validation shows the RFs yield superior, yet still imperfect, measurements of the late-stage merger fraction, and they select more mergers in bulge-dominated galaxies. When applied to CANDELS morphology catalogues, the RFs estimate a merger rate increasing to at least z = 3, albeit two times higher than expected by theory. This suggests possible mismatches in the feedback-determined morphologies, but affirms the basic understanding of galaxy merger evolution. The RFs achieve completeness of roughly $70{{\ \rm per\ cent}}$ at 0.5 < z < 3, and purity increasing from $10{{\ \rm per\ cent}}$ at z = 0.5–60 per cent at z = 3. At earlier times, the training sets are insufficient, motivating larger simulations and smaller time sampling. By blending large surveys and large simulations, such machine learning techniques offer a promising opportunity to teach us the strengths and weaknesses of inferences about galaxy evolution.

scholarly journals Building High-Quality Auction Fraud Dataset

2019 ◽  
Vol 12 (4) ◽  
pp. 1 ◽  
Author(s):  
Sulaf Elshaar ◽  
Samira Sadaoui
Keyword(s):  
Machine Learning ◽  
Deep Understanding ◽  
Machine Learning Techniques ◽  
Online Auction ◽  
High Quality ◽  
Shill Bidding ◽  
Learning Techniques ◽  
Do So

Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. To date, the application of Machine Learning Techniques (MLTs) to auction fraud has been limited, unlike their applications for combatting other types of fraud. Shill Bidding (SB) is a severe auction fraud, which is driven by modern-day technologies and clever scammers. The difficulty of identifying the behavior of sophisticated fraudsters and the unavailability of training datasets hinder the research on SB detection. In this study, we developed a high-quality SB dataset. To do so, first, we crawled and preprocessed a large number of commercial auctions and bidders’ history as well. We thoroughly preprocessed both datasets to make them usable for the computation of the SB metrics. Nevertheless, this operation requires a deep understanding of the behavior of auctions and bidders. Second, we introduced two new SB patterns and implemented other existing SB patterns. Finally, we removed outliers to improve the quality of training SB data.

EXPRESS: Machine Learning for Creativity: Using Similarity Networks to Design Better Crowdfunding Projects

2021 ◽  
pp. 002224292110054
Author(s):  
Yanhao “Max” Wei ◽  
Jihoon Hong ◽  
Gerard J. Tellis
Keyword(s):  
Machine Learning ◽  
Optimal Level ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Similarity Networks ◽  
Fundamental Tension ◽  
Similarity Pattern ◽  
Do So

A fundamental tension exists in creativity between novelty and similarity. This paper exploits this tension to help creators craft successful projects in crowdfunding. To do so, we apply the concept of combinatorial creativity, analyzing each new project in connection to prior similar projects. By using machine learning techniques (Word2vec and Word Mover’s Distance), we measure the degrees of similarity between crowdfunding projects on Kickstarter. We analyze how this similarity pattern relates to a project’s funding performance. We find: (i) the prior level of success of similar projects strongly predicts a new project’s funding performance, (ii) the funding performance increases with a balance between being novel and imitative, (iii) the optimal level for funding goal is close to the funds raised by prior similar projects, and (iv) the funding performance increases with a balance between atypical and conventional imitation. We use these findings to generate actionable recommendations for project creators and crowdfunding platforms.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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