Search

Abstract: In this paper, we introduce the notion of intuitionistic -fuzzy subalgebras in -algebras and study their fundamental properties. We get a fuzzy subalgebra from an intuitionistic -fuzzy subalgebra. Also the notion of intuitionistic -fuzzy (closed) filters of -algebras is introduced. We investigate the relationship between intuitionistic -fuzzy subalgebras and intuitionistic -fuzzy (closed) filters of -algebras. [Copyright &y& Elsevier]

Abstract: In this paper, we define the Smarandache hyper -algebra, and Smarandache hyper -ideals of type 1, 2, 3 and 4. We state and prove some theorems in Smarandache hyper -algebras, and then we determine the relationships between these hyper ideals. [Copyright &y& Elsevier]

Abstract: In this paper we define the Smarandache BL-algebra, Q-Smarandache ideal and Q-Smarandache implicative ideal, we obtain some related results. After that by considering the notions of these ideals we determine relationships between ideals in BL-algebra and Q-Smarandache (implicative) ideals of BL-algebra. Finally we construct quotient of Smarandache BL-algebras via MV-algebra and prove some theorems. [Copyright &y& Elsevier]

One of the important issues in computer networks is Active Queue Management (AQM) that increases the performance of the network. Autoregressive Integrated Moving Average (ARIMA) as an Active Queue Management can improve methods such as congestion control and flow control by predicting the state of the queue in the networks. In current complications of queue theory in computer networks, due to the lack of linear constant trend and other issues such as packets bursting and non-periodic fluctuations of queue length, the present methods of prediction are being challenged. In this paper, a new anticipation ploy is proposed, which improved the prototype of ARIMA by considering available problems and requirements in computer networks. The subscribed algorithm that is called Queue-based ARIMA (QARIMA), can present predictions which are closer to true data, by uplift input data models. [ABSTRACT FROM AUTHOR]