Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques

2020 ◽  
Vol 246 (6) ◽  
pp. 1193-1205 ◽  
Author(s):  
Nattane Luíza da Costa ◽  
Joao Paulo Bianchi Ximenez ◽  
Jairo Lisboa Rodrigues ◽  
Fernando Barbosa ◽  
Rommel Barbosa
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Cabernet Sauvignon ◽  
Ms Analysis ◽  
Learning Techniques ◽  
Icp Ms

Characterization of Different Types of Biomass Wastes Using Thermogravimetric and ICP-MS Analyses

2020 ◽  
Vol 70 (12) ◽  
pp. 4594-4600
Keyword(s):  
Thermogravimetric Analysis ◽  
Food Industry ◽  
Wood Waste ◽  
Biomass Ash ◽  
Minor Elements ◽  
Ms Analysis ◽  
Icp Ms ◽  
Different Types

The purpose of this study was to characterize some types of biomass wastes resulted from different activities such as: agriculture, forestry and food industry using thermogravimetric and ICP-MS analyses. Also, it was optimized an ICP-MS method for the determination of As, Cd and Pb from biomass ash samples. The ICP-MS analysis revealed that the highest concentration of metals (As, Cd, Pb) was recorded in the wood waste ash sample, also the thermogravimetric analysis indicated that the highest amount of ash was obtained for the same sample (26.82%). The biomass wastes mentioned in this study are alternative recyclable materials, reusable as pellets and briquettes. Keywords: ash, biomass, ICP-MS, minor elements, TG

scholarly journals On the use of machine learning techniques for the mechanical characterization of soft biological tissues

2018 ◽  
Vol 34 (10) ◽  
pp. e3121 ◽  
Author(s):  
Myriam Cilla ◽  
Ignacio Pérez-Rey ◽  
Miguel Angel Martínez ◽  
Estefania Peña ◽  
Javier Martínez
Keyword(s):  
Machine Learning ◽  
Mechanical Characterization ◽  
Biological Tissues ◽  
Machine Learning Techniques ◽  
Soft Biological Tissues ◽  
Learning Techniques

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 Characterization of clot composition in acute cerebral infarct using machine learning techniques

2019 ◽  
Vol 6 (4) ◽  
pp. 739-747 ◽  
Author(s):  
Jong‐Won Chung ◽  
Yoon‐Chul Kim ◽  
Jihoon Cha ◽  
Eun‐Hyeok Choi ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarct ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Acute Cerebral Infarct

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques

2020 ◽  
Vol 16 (4) ◽  
pp. 407-419
Author(s):  
Aytun Onay ◽  
Melih Onay
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Support ◽  
Support System ◽  
Molecular Descriptors ◽  
Machine Learning Techniques ◽  
Drug Candidate ◽  
Drug Molecules ◽  
Learning Techniques

Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. Introduction: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. Methods: Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. Results: We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. Conclusion: The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

Machine learning distributions of quantum ansatz with hierarchical structure

2020 ◽  
Vol 34 (20) ◽  
pp. 2050196
Author(s):  
Haozhen Situ ◽  
Zhimin He
Keyword(s):  
Machine Learning ◽  
Quantum Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Variational Autoencoder ◽  
Systems Learning ◽  
Near Term ◽  
Learning Measurement

Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.

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