The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modifie

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

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

Background: Decalcification of surface enamel adjacent to fixed orthodontic appliances, in the form of white spot lesions, is a wide spread and familiar well-known side effect of orthodontic treatment. The present study was carried out to evaluate the effect of enamel protective agent (Clinpro white varnish) on shear and tensile bond strength of Dentaurum orthodontic stainless steel brackets by using 3M Unitek and Ormco as orthodontic adhesive agents. Materials and methods: Sixty-four extracted human upper first premolar teeth were selected and randomly divided into two groups with 32 teeth each, representing the shear and tensile bond strength testing groups. Then according to the type of bonding adhesive and the addition of Clinpro before

This study investigates the impact of varying glass fiber-reinforced polymer (GFRP) stirrup spacing on the performance of doubly GFRP-reinforced concrete beams. The research focuses on assessing the behavior of GFRP-reinforced concrete beams, including load-carrying capacity, cracking, and deformability. It explores the feasibility and effectiveness of GFRP bars as an alternative to traditional steel reinforcement in concrete structures. Six concrete beams with a cross-section of 300 mm (wide) × 250 mm (deep), simply supported on a 2100 mm span, were tested. The beams underwent four-point bending with two concentrated loads applied symmetrically at one-third of the span length, resulting in a shear span (a)-to-depth (h) ratio of 2.

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.

Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio