Jdwarner/Scikit-Fuzzy: Scikit-Fuzzy 0.3.1

Warner, Josh; Sexauer, Jason; scikit-fuzzy,; twmeggs,; Alexandre, M S; Unnikrishnan, Aishwarya; Castelão, Guilherme; Batista, Fernando; Mishra, Himanshu

doi:10.5281/zenodo.1002946

Cited by 3 publications

(1 citation statement)

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“…This model is widely recognized for generating accurate categorizations in comparative studies due to its fuzzy characteristics in categorization, which extends the concept of unsupervised artificial intelligence [39][40][41]. The c-means classification method was implemented in Python, specifically making use of the fuzzy-c-means library [42].…”

Section: Non-supervized Fuzzy Categorizationmentioning

confidence: 99%

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

Quintero,

Gomez,

Gaitan

2023

Preprint

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The pollution and the progressive depletion of resources are global problems that affect all humanity, but their approach is affected by the different sociopolitical and economic conditions present in the different regions. Thus, the objective of this work is to implement and analyze a methodological structure based on the Solow/Green-Solow models that facilitates the identification and organization of countries according to their economic capacity and the polluting emissions associated with their development, with the purpose of promoting the application of more appropriate and effective solutions, considering the diversity of challenges faced by different economies. To achieve this objective, a preliminary guide is proposed to compare and group viable strategies in different territories, evaluating them according to the parameters established by the Solow-Swan and Green Solow models. For the recognition of the parameters, the Levenberg-Marquardt algorithm has been used, the result of which is classified using the fuzzy artificial intelligence method known as c-means, which provides a category for the different countries. The key findings of this article highlight how the particularities of countries are influenced not only by their geographic location, but also by their level of income and the calculated elasticity related to product exports, together with their ecological potential. It also challenges the widely held belief that enrichment is linked to increased carbon emissions.

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Section: Non-supervized Fuzzy Categorizationmentioning

confidence: 99%

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

Quintero,

Gomez,

Gaitan

2023

Preprint

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Inference System for Irrigation Scheduling with an Intelligent Agent

Jiménez

Herrera

Ortiz

et al. 2018

Advances in Intelligent Systems and Computing

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The Arctic Ocean Manganese Cycle, an Overlooked Mechanism in the Anomalous Palaeomagnetic Sedimentary Record

et al. 2020

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Palaeomagnetic records obtained from Arctic Ocean sediments are controversial because they include numerous and anomalous geomagnetic excursions. Age models that do not rely on palaeomagnetic interpretations reveal that the majority of the changes in inclination do not concur with the established global magnetostratigraphy. Seafloor oxidation of (titano)magnetite to (titano)maghemite with self-reversal of the (titano)maghemite coatings has been proposed as an explanation. However, no existing model can explain when the self-reversed components formed and how they are linked to litho-stratigraphic changes in Arctic Ocean sediments. In this study, we present new palaeo-and rock magnetic measurements of a sediment core recovered from the Arlis Plateau, close to the East Siberian Shelf. The magnetic data set is evaluated in the context of the regional stratigraphy and downcore changes in physical and chemical properties. By cross-core correlation, we show that magnetic inclination changes in the region do not stratigraphically align, similar to results of studies of sediments from the Lomonosov Ridge and Yermak Plateau. Rock magnetic and chemical parameters indicate post-depositional diagenetic changes in the magnetic mineral assemblage that can be linked to manganese cycling in the Arctic Ocean. The potential presence of a magnetic remanence bearing manganese-iron oxide phase, which can undergo self-reversal, leads to an alternative hypothesis to primary seafloor oxidation of (titano)magnetite. This phase may form by precipitation from seawater or by changing redox conditions in the sediment column by mineral precipitation from ions dissolved in pore water. These findings highlight the need for further investigation into the magnetic mineral assemblage, its link to manganese cycling and pore water geochemistry in Arctic Ocean sediments.

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Jdwarner/Scikit-Fuzzy: Scikit-Fuzzy 0.3.1

Cited by 3 publications

References 0 publications

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

Inference System for Irrigation Scheduling with an Intelligent Agent

The Arctic Ocean Manganese Cycle, an Overlooked Mechanism in the Anomalous Palaeomagnetic Sedimentary Record

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