The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

This paper presents a numerical analysis using ANSYS finite element program to simulate the reinforced concrete slabs with spherical voids. Six full-scale one way bubbled slabs of (3000mm) length with rectangular cross-sectional area of (460mm) width and (150mm) depth are tested as simply supported under two-concentrated load. The results of the finite element model are presented and compared with the experimental data of the tested slabs. Material nonlinearities due to cracking and crushing of concrete and yielding of reinforcement are considered. The general behavior of the finite element models represented by the load-deflection curves at midspan, crack pattern, ultimate load, load-concrete strain curves and failure m

Abstract

In this paper presents two dimensional turbulent flow of different nanofluids and ribs configuration in a circular tube have been numerically investigation using FLUENT 6.3.26. Two samples of CuO and, ZnO nanoparticles with 2% v/v concentration and 40 nm as nanoparticle diameter combined with trapezoidalribs with aspect ratio of p/d=5.72 in a constant tube surface heat flux were conducted for simulation. The results showed that heat flow as Nusselt number for all cases raises with Reynolds number and volume fraction of nanofluid, likewise the results also reveal that ZnO with volume fractions of 2% in trapezoidal ribs offered highest Nusselt number at Reynolds number of Re= 30000.

Key

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

This work is a trial to ensure the absolute security in any quantum cryptography (QC) protocol via building an effective hardware for satisfying the single-photon must requirement by controlling the value of mean photon number. This was approximately achieved by building a driving circuit that provide very short pulses (≈ 10 ns) for laser diode -LD- with output power of (0.7-0.99mW) using the available electronic components in local markets. These short pulses enable getting faint laser pulses that were further attenuated to reach mean photon number equal to 0.08 or less.