Phishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency.  This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing websi

Background: This in vitro study evaluated the effect of canal dryness and flaring on the accuracy of two electronic apex locators for working length (WL) determination. Materials and methods: Sixty extracted teeth were used, after access opening was done, the occlusal surface was flattened to create stable reference point. The teeth were randomly divided into two equal main groups of flared and unflared group each one of 30 teeth. The flaring was done with Gates Glidden drills. The two main groups were further subdivided into two subgroups: dry canal and wet canal using 5.25% sodium hypochlorite groups, Electronic WL of each sample was determined using both Root ZX and i-Root apex locator. Consequently, histologic WL was determined by shav

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

The aim of this study was to identify the rate of return of the stock through the financial information disclosed by the financial statements of companies both services and insurance included in Iraqi market for securities . The study used a descriptive statistical methods and the correlation matrix for the independent factors , in addition to a regression model for data  analysis and hypothesis . Model included a number of independent variables , which was measured in the size of company (sales or revenue) , and the leverage , in addition to the structure of assets and the book value of owners'  equity in the company , as well as the general price index .Based on the data of (11)companies and for three years, showed the result

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storag

Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,

Thin films of bulk heterojunction blend Ni-Phthalocyanine
Tetrasulfonic acid tetrasodium salt and dpoly
(3, 4-ethylenedioxythiophene) poly (styrenesulfonate) (NiPcTs:
PEDOT: PSS) with different (PEDOT:PSS) concentrations (0.5, 1, 2)
are prepared using spin coating technique with thickness 100 nm on
glass and Si substrate. The X-Ray diffraction pattern of NiPcTs
powder was studied and compared with NiPc powder, the pattern
showed that the structure is a polycrystalline with monoclinic phase.
XRD analysis of as-deposited (NiPcTs/PEDOT:PSS) thin films
blends in dicated that the film appeared at(100), (102) in
concentrations (0.5, 1) and (100) in concentration (2). The grain size
is increased with increasing

Thin films of iridium doped indium oxide (In2O3:Eu)with different doping ratio(0,3,5,7,and 9%) are prepared on glass and single crystal silicon wafer substrates using spray pyrolysis method. The goal of this research is to investigate the effect of doping ratio on of the structural, optical and sensing properties . The structure of the prepared thin films was characterized at room temperature using X-ray diffraction. The results showed that all the undoped and doped (In2O3:Eu)samples are polycrystalline in structure and nearly stoichiometric. UV-visible spectrophotometer in the wavelength range (200-1100nm)was used to determine the optical energy gap and optical constants. The optical transmittance of 83% and the optical band gap of 5.2eV