The detection for Single Escherichia Coli Bacteria has attracted great interest and in biology and physics applications. A nanostructured porous silicon (PS) is designed for rapid capture and detection of Escherichia coli bacteria inside the micropore. PS has attracted more attention due to its unique properties. Several works are concerning the properties of nanostructured porous silicon. In this study PS is fabricated by an electrochemical anodization process. The surface morphology of PS films has been studied by scanning electron microscope (SEM) and atomic force microscope (AFM). The structure of porous silicon was studied by energy-dispersive X-ray spectroscopy (EDX). Details of experimental methods and results are given and discussed

The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5-trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion ( ) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional theory (DFT) calcul

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5- trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion (𝐶􀬶𝑂􀬶 􀬶􀬿) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional th

Discriminant analysis is a technique used to distinguish and classification an individual to a group among a number of  groups based on a linear combination of a set of relevant variables know discriminant function. In this research  discriminant analysis used to analysis data from repeated measurements design. We  will  deal  with the problem of  discrimination  and  classification in the case of  two  groups by assuming the Compound Symmetry covariance structure  under  the  assumption  of  normality for  univariate  repeated measures data.

An innovative desalination method called electrosorption or capacitive deionization (CDI) has significant benefits for wastewater treatment. This process is performed by using a carbon fiber electrode as a working electrode to remove hexavalent chromium ions from an aqueous solution. The pH, NaCl concentration, and cell voltage were optimized using the Box-Behnken experimental design (BDD) in response surface methodology (RSM) to study the effects and interactions of selected variables. To attain the relationship between the process variables and chromium removal, the experimental data were subjected to an analysis of variance and fitted with a quadratic model. The optimum conditions to remove Cr(VI) ions were: pH of 2, a cell voltage of 4.