Modeling Multi-Level Systems

Iordache, Octavian

doi:10.1007/978-3-642-17946-4

Cited by 24 publications

(11 citation statements)

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“…The algorithm result is a set of weights allowing classification. The procedure was applied to the synthesis of complex dendrograms using information entropy [62,63,64,65]. Our program MolClas is simple, reliable, efficient and fast procedure for molecular classification, based on equipartition conjecture of entropy production according to Equations (11)–(17); it reads number of properties and molecular properties; it allows optimization of coefficients; it optionally reads starting coefficients and number of iteration cycles.…”

Section: Methodsmentioning

confidence: 99%

Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides

Torrens

Castellano

2014

Molecules

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Pesticide residues in wine were analyzed by liquid chromatography-tandem mass spectrometry. Retentions are modelled by structure-property relationships. Bioplastic evolution is an evolutionary perspective conjugating effect of acquired characters and evolutionary indeterminacy-morphological determination-natural selection principles; its application to design co-ordination index barely improves correlations. Fractal dimensions and partition coefficient differentiate pesticides. Classification algorithms are based on information entropy and its production. Pesticides allow a structural classification by nonplanarity, and number of O, S, N and Cl atoms and cycles; different behaviours depend on number of cycles. The novelty of the approach is that the structural parameters are related to retentions. Classification algorithms are based on information entropy. When applying procedures to moderate-sized sets, excessive results appear compatible with data suffering a combinatorial explosion. However, equipartition conjecture selects criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds agreeing with principal component analyses. Periodic classification shows that pesticides in the same group present similar properties; those also in equal period, maximum resemblance. The advantage of the classification is to predict the retentions for molecules not included in the categorization. Classification extends to phenyl/sulphonylureas and the application will be to predict their retentions.

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Section: Methodsmentioning

confidence: 99%

Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides

Torrens

Castellano

2014

Molecules

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“…The problem in classification studies is to define similarity indices when several criteria of comparison are involved (Iordache, 2011(Iordache, , 2012(Iordache, , 2014. The first step in quantifying similarity for phenylureas is to list main chemical characteristics of molecules.…”

Section: Methodsmentioning

confidence: 99%

Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants

Torrens

Castellano

2014

ETET

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ABSTRACT. Chromatographic retention times of phenylurea herbicides are modelled by structure-property relationships. Properties are hydration free energy and dipole. Bioplastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among evolutionary indeterminacy, morphological determination and natural selection principles. Classification algorithms are proposed based on information entropy and production. Phenylureas are classified by Cl2, O2 and N2 presence; their different behaviour depends on the number of Cl atoms. When applying procedures to moderate-sized sets, excessive results appear compatible with data and suffer a combinatorial explosion; however, the equipartition conjecture allows a selection criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds and agrees with principal component analyses. Phenylureas periodic table shows that those in the same group present similar properties; phenylureas also in the same period, maximum resemblance. Classification extends to phenylureas, sulphonylureas, pesticides and persistent organic pollutants.

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“…Tables 21-24 provide this summary. From these tables we can conclude that FCA has attracted applications in several domains due to its potential of knowledge discovery (Aswani Kumar, 2011a;Aswani Kumar and Singh, 2014), representation (Iordache, 2011;Poelmans et al, 2013a;2014), reasoning (Rainer and Ganapati, 2011;Ruairi, 2013;Sebastien et al, 2013) and the decision context (Li et al, 2011a;Shao et al, 2014) which contains another tuple called a set of decision attributes (Yang et al, 2011a).…”

Section: Applications Of Fcamentioning

confidence: 99%

A comprehensive survey on formal concept analysis, its research trends and applications

Singh

Cherukuri

Gani

2016

International Journal of Applied Mathematics and Computer Science

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In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.

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Modeling Multi-Level Systems

Cited by 24 publications

References 0 publications

Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides

Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides

Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants

A comprehensive survey on formal concept analysis, its research trends and applications

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