In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

     The novel coronavirus 2019 (COVID-19) is a respiratory syndrome with similar traits to common pneumonia. This major pandemic has affected nations both socially and economically, disturbing everyday life and urging the scientific community to develop solutions for the diagnosis and prevention of COVID-19. Reverse transcriptase-polymerase chain reaction (RT–PCR) is the conventional approach used for detecting COVID-19. Nevertheless, the initial stage of the infection is less predictable in PCR tests, making early prediction challenging. A robust and alternative diagnostic method based on digital computerised technologies to support conventional methods would greatly help society. Therefore, this paper reviews recent research bas

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

     Lenticular galaxy NGC3 has been chosen to study the surface photometry using griz filter. The data where obtained from the seventh Sloan Digital Sky Survey (SDSS) Data Release seven (DR7), and main the image reduction was done by the pipeline of SDSS. The work was achieved by the ELLIPS task from the STSDAS ISOPHOTE package in the Image Reduction and Analysis Facility (IRAF).The overall structure of the galaxy (a bulge, a bar, isophotal contour maps, together with a bulge to disk decomposition of the galaxy images where achieved, Also, the photometric properties (the disk position angle, ellipticity, B4 and inclination of the galaxy) where estimated.

In this paper, a new 5G Passive Optical Network (5G-PON) employing all-optical orthogonal frequency division multiplexing (AO-OFDM) is proposed in hybrid bidirectional standard single mode fiber (SSMF)/free space optical (FSO). Additionally, an optical frequency generator (OFG) source is utilized. The proposed model is simulated using VPI photonics software. Analytical modeling and simulations have been conducted for a new approach to generate OFG by cascaded two-frequency modulators and one electro-absorption modulator. A sinusoidal RF signal source is utilized to drive all these modulators. The results reveal that 64 optical multiplexed carriers with a frequency spacing of 30 GHz are generated. These optical carriers have power variations

by in situ polymerization of aniline monomer, conducting polyaniline (PANI) nanocomposites containing various concentrations of carboxylic acid functionalized multi-walled carbon nanotubes (f-MWCNT) were synthesized. The morphological and electrical properties of pure PANI and PANI /MWCNT nanocomposites were examined by using Fourier transform- infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Atomic Force Microscopy (AFM) respectively. FTIR spectra shows that the carboxylic acid groups formed at the both ends of the sidewalls of the MWCNTs. The aniline monomers were polymerized on the surface of MWCNTs, depending on the -* electron interaction between aniline monomers and MWCNTs and hydrogen bonding into interaction between t

The electric submersible pump, also known as ESP, is a highly effective artificial lift method widely used in the oil industry due to its ability to deliver higher production rates compared to other artificial lift methods. In principle, ESP is a multistage centrifugal pump that converts kinetic energy into dynamic hydraulic pressure necessary to lift fluids at a higher rate with lower bottomhole pressure, especially in oil wells under certain bottomhole condition fluid, and reservoir characteristics. However, several factors and challenges can complicate the completion and optimum development of ESP deployed wells, which need to be addressed to optimize its performance by maximizing efficiency and minimizing costs and uncertainties. To