The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

In the present work, the nuclear shell model with Hartree–Fock (HF) calculations have been used to investigate the nuclear structure of ²⁴Mg nucleus. Particularly, elastic and inelastic electron scattering form factors and transition probabilities have been calculated for low-lying positive and negative states. The sd and sdpf shell model spaces have been used to calculate the one-body density matrix elements (OBDM) for positive and negative parity states respectively. Skyrme-Hartree-Fock (SHF) with different parameterizations has been tested with shell model calculation as a single particle potential for reproducing the experimental data along with a harmonic oscillator (HO) and Woods-Saxo

         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

The aim of this study is to show the concepts of nuclear shape and the geometrical picture to the even-even nuclei of 164,166,168E isotopes in the context of the Interacting boson Model IBM-1. The energy spectra were calculated and the effective charge values (eB) of the electromagnetic transition strength were obtained and used to calculate the B(E2) values of the electromagnetic transitions and the quadrupole moment Q of 2+ -states. The Hamiltonian parameters were calculated by taking in account the properties of these nuclei. Comparison were made with the available experimental data and included in tables. The geometrical picture of these nuclei were looked at by calculating the deformation which were represented by the potentia

The proton-neutron interacting boson model (IBM-2) has been used to make a schematic study of the Ruthenium ( ) isotopes of mass region around with and . For each isotope of the values of the IBM-2 Hamiltonian parameters, which yield an acceptable results for excitation energies in comparison with those of experimental data, have been determined. Fixed values of the effective charges ( ) and of the proton and neutron g factors ( and ) have been chosen for all isotopes under study. The calculated electric quadrupole moments of state, transitions, the magnetic dipole moments transitions and mixing ratios are in reasonable agreement with the experimental data.

The nuclear structure for the positive ( ) States and negative ( ) states of ^36,40Ar nuclei have been studied via electromagnetic transitions within the framework of shell model. The shell model analysis has been performed for the electromagnetic properties, in particular, the excitation energies, occupancies numbers, the transition strengths B(CL) and the elastic and inelastic electron scattering longitudinal form factors. Different model spaces with different appropriate interactions have been considered for all selected states. The deduced results for the (CL) longitudinal form factors and other properties have been discussed and compared with the available experimental data. The inclusion of the effective

The paper presents a neural synchronization into intensive study in order to address challenges preventing from adopting it as an alternative key exchange algorithm. The results obtained from the implementation of neural synchronization with this proposed system address two challenges: namely the verification of establishing the synchronization between the two neural networks, and the public initiation of the input vector for each party. Solutions are presented and mathematical model is developed and presented, and as this proposed system focuses on stream cipher; a system of LFSRs (linear feedback shift registers) has been used with a balanced memory to generate the key. The initializations of these LFSRs are neural weights after achiev

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained