Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

The reason for conducting this study is to prolong release of miconazole in the ocular site of action by ocular-based gels (OBGs) formulations. The formulation factors affecting on the release from OBG should be studied using various gelling agents in various concentrations to achieve the improvement in retention and residence time in response to prolonged release. In this study, the formulations were prepared using carbopol 940, pectin, sodium alginate, poloxamer 407, and poly(methacrylic acid) at 0.5%, 1%, and 1.5% w/v, respectively. Hydroxypropyl methylcellulose E5 (HPMC E5) 1% was added as thickening agent/viscosity builder. The formulation containing carbopol 940, pectin and sodium alginate at 1.5% w/v, displayed a noticable im

   This study includes Estimating scale parameter, location parameter  and reliability function  for Extreme Value (EXV) distribution by two methods, namely: -
- Maximum Likelihood Method (MLE).
- Probability Weighted Moments Method (PWM).

 Used simulations to generate the required samples to estimate the parameters and reliability function of different sizes(n=10,25,50,100) , and give real values for the parameters are and , replicate the simulation experiments (R_P=1000)

Machine scheduling problems (MSP) are     considered as one of the most important classes of combinatorial optimization problems. In this paper, the problem of job scheduling on a single machine is studied to minimize the multiobjective and multiobjective objective function. This objective function is: total completion time, total lead time and maximum tardiness time, respectively, which are formulated as  are formulated. In this study, a mathematical model is created to solve the research problem. This problem can be divided into several sub-problems and simple algorithms have been found to find the solutions to these sub-problems and compare them with efficient solutions. For this problem, some rules that provide efficient solutio

The purpose of this research was to prepare, characterize, and evaluate the new antimicrobial peptide  KSL peptide encapsulated in poly(D,L-lactide-co-glycolide) (PLGA)composite microspheres. KSL was loaded in poly(acryloyl hydroxyethyl) starch (acHES) micropar-ticles, and then the peptide-containing microparticles were encapsulated in the PLGA matrix by a solvent extraction /evaporation method.

 KSL-loaded PLGA microspheres were also prepared without the starch hydrogel microparticle microspheres for comparison study. KSL peptide microspheres were characterized for drug content, surface morphology, microspheres size determination, polymers stability , in vitro microspheres degradation and in vitro release. KSL peptide

In this paper reliable computational methods (RCMs) based on the monomial stan-dard polynomials have been executed to solve the problem of Jeffery-Hamel flow (JHF). In addition, convenient base functions, namely Bernoulli, Euler and Laguerre polynomials, have been used to enhance the reliability of the computational methods. Using such functions turns the problem into a set of solvable nonlinear algebraic system that MathematicaⓇ12 can solve. The JHF problem has been solved with the help of Improved Reliable Computational Methods (I-RCMs), and a review of the methods has been given. Also, published facts are used to make comparisons. As further evidence of the accuracy and dependability of the proposed methods, the maximum error remainder

Abstract

Knowing the amount of residual stresses and find technological solutions to minimize and control them during the production operation are an important task because great levels of deformation which occurs in single point incremental forming (SPIF), this induce highly non-uniform residual stresses. In this papera propose of a method for multilayer single point incremental forming with change in thickness of the top plate (0.5, 0.7, 0.9) mm and lubrication or material between two plates(polymer, grease, grease with graphite, mos₂) to knowing an effect of this method  and parameters on residual stresses for the bottom plates. Also compare these results for the

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.

 Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.

We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).

The results proved that the (ANN) estimator is the best nonlinear estimator am