Generalizations of rough sets induced by binary relations approach in semigroups

Prasertpong, Rukchart; Siripitukdet, Manoj

doi:10.3233/jifs-181435

Cited by 2 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the figure, f is the encoding function; X is the input layer data; y is the output layer data; G is the decoding function and l is the loss function. The mathematical relationship between them can be expressed as follows: (1) x + b (1) (1) z = g(h) = s g w (2) h + b (2)…”

Section: Deep Learning Modelmentioning

confidence: 99%

“…In formula (1) and 2, h is the hidden feature, SF is the nonlinear activation function; SG is the commonly used activation function; Z is the actual output after the transformation of the hidden feature; w (1) , w (2) is the weight matrix; B (1) , B (2) is the error vector.…”

Section: Deep Learning Modelmentioning

confidence: 99%

“…Statistics on sports industry jointly released by the State Administration of sports and the National Bureau of statistics show that by the end of 2018, the total scale and added value of China's sports industry have increased significantly [1]. The industrial added value accounted for 1.1% of GDP in that year, of which the total scale reached 2657.9 billion yuan, an increase of 20.9% over 2017; the industrial added value reached 1007.8 billion yuan, an increase of 29.0% over 2017 [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of sports industry under the influence of COVID-19 epidemic situation based on big data

Wang

Zeng

2020

IFS

View full text Add to dashboard Cite

Based on big data, this paper studies the influence of new type of filling pneumonia on the development of sports industry. When selecting the typical economic indicators that reflect the development trend of sports industry, it is found that the data is huge according to the big industrial data, but the information that can be reflected is poor and complex. Therefore, it is necessary to process these big economic data in order to obtain the impact of new coronary pneumonia on the development of sports industry. This paper studies the feature selection algorithm of big data samples, so as to select typical economic indicators from many economic indicators of sports industry to reflect the development trend of sports industry. A deep learning algorithm based on feature selection of big data is proposed. Firstly, a feature selection framework for big data is constructed, and then data fusion and deep learning are carried out. Experiments show that the algorithm can solve the contradiction between large data and poor information. This method has a certain forward-looking, and has a certain reference value for the information discrimination of the development trend of sports industry.

show abstract

Section: Deep Learning Modelmentioning

confidence: 99%

Section: Deep Learning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of sports industry under the influence of COVID-19 epidemic situation based on big data

Wang

Zeng

2020

IFS

View full text Add to dashboard Cite

show abstract

Roughness of soft sets and fuzzy sets in semigroups based on set-valued picture hesitant fuzzy relations

Prasertpong

2022

MATH

View full text Add to dashboard Cite

<abstract><p>In the philosophy of rough set theory, the methodologies of rough soft sets and rough fuzzy sets have been being examined to be efficient mathematical tools to deal with unpredictability. The basic of approximations in rough set theory is based on equivalence relations. In the aftermath, such theory is extended by arbitrary binary relations and fuzzy relations for more wide approximation spaces. In recent years, the notion of picture hesitant fuzzy relations by Mathew et al. can be considered as a novel extension of fuzzy relations. Then this paper proposes extended approximations into rough soft sets and rough fuzzy sets from the viewpoint of its. We give corresponding examples to illustrate the correctness of such approximations. The relationships between the set-valued picture hesitant fuzzy relations with the upper (resp., lower) rough approximations of soft sets and fuzzy sets are investigated. Especially, it is shown that every non-rough soft set and non-rough fuzzy set can be induced by set-valued picture hesitant fuzzy reflexive relations and set-valued picture hesitant fuzzy antisymmetric relations. By processing the approximations and advantages in the new existing tools, some terms and products have been applied to semigroups. Then, we provide attractive results of upper (resp., lower) rough approximations of prime idealistic soft semigroups over semigroups and fuzzy prime ideals of semigroups induced by set-valued picture hesitant fuzzy relations on semigroups.</p></abstract>

show abstract

Generalizations of rough sets induced by binary relations approach in semigroups

Cited by 2 publications

References 24 publications

Development of sports industry under the influence of COVID-19 epidemic situation based on big data

Development of sports industry under the influence of COVID-19 epidemic situation based on big data

Roughness of soft sets and fuzzy sets in semigroups based on set-valued picture hesitant fuzzy relations

Contact Info

Product

Resources

About