scholarly journals Investigating the formation mechanism of polycyclic aromatic hydrocarbons and adapting particle swarm optimization techniques to search large data sets

2010 ◽  
Author(s):  
Daniel P. Caputo
Keyword(s):  
Particle Swarm Optimization ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Formation Mechanism ◽  
Aromatic Hydrocarbons ◽  
Large Data ◽  
Optimization Techniques ◽  
Large Data Sets ◽  
Data Sets ◽  
Swarm Optimization ◽  
Polycyclic Aromatic

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

Effect of fatty acid on the formation of polycyclic aromatic hydrocarbons (PAHs) and the proposed formation mechanism during electric roasting

2019 ◽  
Vol 121 (12) ◽  
pp. 3193-3207
Author(s):  
Congcong Liu ◽  
Chong Wang ◽  
Keping Ye ◽  
Yun Bai ◽  
Xiaobo Yu ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Fatty Acid ◽  
Carbon Atom ◽  
Formation Mechanism ◽  
Aromatic Hydrocarbons ◽  
Meat Products ◽  
Content Type ◽  
Animal Fats ◽  
Animal Fat ◽  
Polycyclic Aromatic

Purpose The purpose of this paper is to elucidate the influences of the animal fat and fatty acid type on the formation of polycyclic aromatic hydrocarbons (PAHs) and to propose a formation mechanism of PAHs in fat during electric roasting, which is a method of non-direct-contact-flame heating. Design/methodology/approach The effects of animal fats and model fat on the formation of PAHs were valued on the basis of the ultra high-performance liquid chromatography data. The corresponding products of the FAME pyrolysis were detected by TG-FTIR. The proposal formation mechanism of PAHs was based on the summary of the literature. Findings Contrary to the International Agency for Research on Cancer, DF had higher risk with 280.53 ng/g of concentration after being roasted than the others animal fats of red meat in terms of PAHs formation. This research also ensured the importance of fat on PAHs formation, the concentration of PAHs in pure fats was higher after being electric roasted than that in meat patties and juice which made from corresponding animal fat. What is more, during pure animal fats and meat products being processed, less PAHs formed in the fat with lower extent of unsaturation and lower content of linolenate. In the same way, methyl linolenate demonstrated the significant increasement to PAHs formation compared to the other fatty acids. And, the number of carbon atom and the extent of unsaturation in fatty acid affects the formation of PAHs during roasting. The detection of alkene and alkane allows to propose a formation mechanism of PAHs during model fat being heated. Further study is required to elucidate the confirm moleculars during the formation of PAHs. Originality/value This work studied the effect of the carbon atom number and the unsaturation extent of fats and model fats on the formation of PAHs. This work also assure the important of alkene and alkane on the pyrolysis of model fats. This study also researched the formation and distribution of PAHs in pure fats and meat products after being heated.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2018 ◽  
Vol 10 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Optimization Techniques ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals Learning mid-IR emission spectra of polycyclic aromatic hydrocarbon populations from observations

2019 ◽  
Vol 632 ◽  
pp. A84 ◽  
Author(s):  
S. Foschino ◽  
O. Berné ◽  
C. Joblin
Keyword(s):  
Polycyclic Aromatic Hydrocarbon ◽  
Aromatic Hydrocarbon ◽  
Spectral Data ◽  
Spectral Resolution ◽  
Large Data ◽  
Chemical Diversity ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Polycyclic Aromatic

Context. The James Webb Space Telescope (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6−28 μm range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets. Aims. Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs). Methods. We present a method based on linear fitting and blind signal separation (BSS) for extracting representative spectra for a spectral data set. The method is fast and robust, which ensures its applicability to JWST spectral cubes. We tested this method on a sample of ISO-SWS data, which resemble most closely the JWST spectra in terms of spectral resolution and coverage. Results. Four representative spectra were extracted. Their main characteristics appear consistent with previous studies with populations dominated by cationic PAHs, neutral PAHs, evaporating VSGs, and large ionized PAHs, known as the PAHx population. In addition, the 3 μm range, which is considered here for the first time in a BSS method, reveals the presence of aliphatics connected to neutral PAHs. Each representative spectrum is found to carry second-order spectral signatures (e.g., small bands), which are connected with the underlying chemical diversity of populations. However, the precise attribution of theses signatures remains limited by the combined small size and heterogeneity of the sample of astronomical spectra available in this study. Conclusions. The upcoming JWST data will allow us to overcome this limitation. The large data sets of hyperspectral images provided by JWST analysed with the proposed method, which is fast and robust, will open promising perspectives for our understanding of the chemical evolution of the AIB carriers.

scholarly journals Evaluation of Optimization Strategies for Incremental Graph Queries

2017 ◽  
Vol 61 (2) ◽  
pp. 175 ◽  
Author(s):  
Gábor Szárnyas ◽  
János Maginecz ◽  
Dániel Varró
Keyword(s):  
Response Times ◽  
Distributed Storage ◽  
Large Data ◽  
Database Systems ◽  
Optimization Techniques ◽  
Large Data Sets ◽  
Data Sets ◽  
Multiple Datasets ◽  
Graph Queries ◽  
Relational Database Systems

The last decade brought considerable improvements in distributed storage and query technologies, known as NoSQL systems. These systems provide quick evaluation of simple retrieval operations and are able to answer certain complex queries in a scalable way, albeit not instantly. Providing scalability and quick response times at the same time for querying large data sets is still a challenging task. Evaluating complex graph queries is particularly difficult, as it requires lots of join, antijoin and filtering operations. This paper presents optimization techniques used in relational database systems and applies them on graph queries. We evaluate various query plans on multiple datasets and discuss the effect of different optimization techniques.

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