Purpose: To synthesize and characterize novel Schiff base-chitosan composites and determine their antimicrobial activity. Methods: Novel Schiff-base chitosan composites were synthesized by grafting polyvinylpyrrolidone and then replacing it with different aldehydes. The resulting composites were characterized using advanced analytical techniques, including thermogravimetric analysis (TGA), x-ray diffraction (XRD) studies and Fourier transform infrared spectroscopy (FT-IR). Results: The FT-IR results revealed that the Schiff-base chitosan was successfully produced during mixing and the crystallinity change of the samples was explained by the XRD patterns. Thermogravimetric analysis (TGA) of compounds A1, A3 and A4 showed weight loss

In this study, thin films of pure titanium dioxide (TiO2) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO)x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO)x. This trend reversed at x=0.20, where a decrease in roughness occurred. Interestingly, a sub

In this study, thin films of pure titanium dioxide (TiO 2 ) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO) x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO) x . This trend reversed at x = 0.20, where a decrease in roughness occurred. Interestin

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.

Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The

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