Strong Riesz summability of Fourier series

The notion of strong summability was introduced by Fekete (Math. És Termesz Ertesitö, 34 (1916), 759-786). Dealing with Nörlund summability of Fourier series Mittal (J. Math. Anal. Appl. 314 (2006), 75-84) has established a result on strong summability. We have established a new result on sufficient condition for strong Riesz summability of Fourier series.

A new factor theorem for generalized absolute Riesz summability

The aim of this paper is to consider an absolute summability method and generalize a theorem concerning $\left|\bar{N},p_{n}\right|_{k}$ summability of infinite series to ${\varphi-\mid{\bar{N},p_n;\delta}\mid}_k$ summability of infinite series by using almost increasing sequence. Furthermore, it is explained that a well known result dealing with $\left|\bar{N},p_{n}\right|_{k}$ summability is obtained when this generalization is restricted under special conditions.

On absolute Riesz summability factors of infinite series and their application to Fourier series

In this paper, some known results on the absolute Riesz summability factors of infinite series and trigonometric Fourier series have been generalized for the {\lvert\bar{N},p_{n};\theta_{n}\rvert_{k}} summability method. Some new and known results are also obtained.

On a new application of quasi power increasing sequences

In the present paper, absolute matrix summability of infinite series has been studied. A new theorem concerned with absolute matrix summability factors, which generalizes a known theorem dealing with absolute Riesz summability factors of infinite series, has been proved under weaker conditions by using quasi $\beta$-power increasing sequences. Also, a known result dealing with absolute Riesz summability has been given.

Comparison of Medical Decision-Making with Intuitionistic Fuzzy Parametrized Fuzzy Soft Set and Riesz Summability

In the present study, for the medical decision-making problem, the proposed techniques related to the intuitionistic fuzzy parametrized soft sets and Riesz mean methods were used. The results of the given methods were compared. The values obtained from the methods were ordered and the success of the measurement techniques of the methods were evaluated. The real dataset which is called Cleveland heart disease dataset was applied in this problem.