This article aim to estimate the Return Stock Rate of the private banking sector, with two banks, by adopting a Partial Linear Model based on the Arbitrage Pricing Model (APT) theory, using Wavelet and Kernel Smoothers. The results have proved that the wavelet method is the best. Also, the results of the market portfolio impact and inflation rate have proved an adversely effectiveness on the rate of return, and direct impact of the money supply.

Nano particles of Cadmium Oxide (CdO) thin films were prepared by spray pyrolysis technique. The synthesized film is annealed at (200 , 300, 450) o C for 3 hours .  The XRD and AFM for the analysis of its structural and micro-structural characteristic has been preformed. The average grain size was found to be about 32.50 nm .There is a preferred orientation along (200)   plane  with texture coefficient 1.79, 1.644, 1.763 and 1.792  for deposited and annealed films, corresponding to grain size 57,58 ,51 and 51 nm. The variations of stress with temperature is ranged from 0.157 - 0.376 GPa .
 

In this paper, The transfer function model in the time series was estimated using different methods, including parametric Represented by the method of the Conditional Likelihood Function, as well as the use of abilities nonparametric are in two methods  local linear regression and cubic smoothing spline method, This research aims to compare those capabilities with the nonlinear transfer function model by using the style of simulation and the study of two models as output variable and one model as input variable in addition t

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

This research has been prepared to isolate and diagnose one of the most important vegetable oils from the plant medical clove is the famous with Alaeugenol oil and used in many pharmaceuticals were the isolation process using a technique ultrasonic extraction and distillation technology simple