In this paper, subclasses of  the function class  ∑  of analytic and bi-univalent functions associated with operator  L_q^(k,  λ)  are introduced and defined in the open unit disk △ by applying quasi-subordination. We obtain some results about the corresponding bound estimations of the coefficients  a_(2 )  and a_(3 ).

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

In this research, some probability characteristics functions (probability density, characteristic, correlation and spectral density) are derived depending upon the smallest variance of the exact solution of supposing stochastic non-linear Fredholm integral equation of the second kind found by Adomian decomposition method (A.D.M)

The objective of this work is to study the concept of a fuzzy -cone metric space And some related definitions in space. Also, we discuss some new results of fixed point theorems. Finally, we apply the theory of fixed point achieved in the research on an integral type.

The concern of this article is the calculation of an upper bound of second Hankel determinant for the subclasses of functions defined by Al-Oboudi differential operator in the unit disc. To study special cases of the results of this article, we give particular values to the parameters A, B and λ

There are many factors effect on the spread of infectious disease or control it,
some of these factors are (immigration and vaccination). The main objective of this
paper is to study the effect of those factors on the dynamical behavior of an SVIR
model. It is assumed that the disease is spread by contact between members of
populations individuals. While the recovered individuals gain permanent immunity
against the disease. The existence, uniqueness and boundedness of the solution of
this model are investigated. The local and global dynamical behaviors of the model
are studied. The local bifurcations and Hopf bifurcation of the model are
investigated. Finally, in order to confirm our obtained results and specify t

In this paper a mathematical model that analytically as well as numerically
the flow of infection disease in a population is proposed and studied. It is
assumed that the disease divided the population into five classes: immature
susceptible individuals (S1) , mature individuals (S2 ) , infectious individual
(I ), removal individuals (R) and vaccine population (V) . The existence,
uniqueness and boundedness of the solution of the model are discussed. The
local and global stability of the model is studied. Finally the global dynamics of
the proposed model is studied numerically.

Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.

The main objectives of this pepper are to introduce new classes. We have attempted to obtain coefficient estimates, radius of convexity, Distortion and  Growth theorem and other related results for the classes