With time progress importance of hiding information become more and more and all steganography applications is like computer games between hiding and extracting data, or like thieves and police men always thieve hides from police men in different ways to keep him out of prison. The sender always hides information in new way in order not to be understood by the attackers and only the authorized receiver can open the hiding message. This paper explores our proposed random method in detail, how chooses locations of pixel in randomly , how to choose a random bit to hide information in the chosen pixel, how it different from other approaches, how applying information hiding criteria on the proposed project, and attempts to test out in code, and

Abstract To estimate the seroprevalence of HCV infection among HIV-infected haemophiliacs and to demonstrate the most prevalent HCV genotype, 47 HIV-infected haemophilia patients were screened for anti-HCV antibodies. By performing polymerase chain reaction and DNA enzyme immunoassay, HCV-RNA was detected with subsequent genotyping. Seroprevalence of anti-HCV antibodies was 66.0%. Of 31 HCV/HIV co-infected patients, 21 (67.7%) had no history of blood transfusion. We detected 4 HCV genotypes: 1a, 1b, 4 and 4 mixed with 3a, HCV-1b being the most frequent. Contaminated factor VIII (clotting factor) could be responsible for disease acquisition.

Background: The anterior loop of mental nerve is commonly described as that part of the neurovascular bundle that transverses anterior and inferior to the mental foramen only to loop back to exit the mental foramen. The aim of the study is to evaluate the incidence and extension of anterior loop of mental nerve by using digital panoramic imaging system to avoid nerve damage during different surgical procedures in dentistry. Materials and Method: Panoramic image was taken for all 400 patients and stored in the computer. Then Horizontal and Vertical for the anterior loop extension when exist was measured and recorded in a special case sheet prepared for each subject. Results: Results indicated that out of 400 patients there were only 25 pat

Dyspepsia is a significant public health issue that affects the entire world population. In this work, we formulate and analyze a deterministic model for the population dynamics of Gut bacteria in the presence of antibiotics and Probiotic supplements. All the possible equilibria and their local stability are obtained. The global stability around the positive equilibrium point is established. Numerical simulations back up our analytical findings and show the temporal dynamics of gut microorganisms.

This study presents a mathematical model describing the interaction of gut bacteria in the participation of probiotics and antibiotics, assuming that some good bacteria become harmful through mutations due to antibiotic exposure. The qualitative analysis exposes twelve equilibrium points, such as a good-bacteria equilibrium, a bad-bacteria equilibrium, and a coexisting endemic equilibrium in which both bacteria exist while being exposed to antibiotics. The theory of the Sotomayor theorem is applied to study the local bifurcation around all possible equilibrium points. It’s noticed that the transcritical and saddle-node bifurcation could occur near some of the system’s equilibrium points, while pitchfork bifurcation cannot be accrued at

: Porous silicon (n-PS) films can be prepared by photoelectochemical etching (PECE) Silicon chips n - types with 15 (mA /cm2), in15 minutes etching time on the fabrication nano-sized pore arrangement. By using X-ray diffraction measurement and atomic power microscopy characteristics (AFM), PS was investigated. It was also evaluated the crystallites size from (XRD) for the PS nanoscale. The atomic force microscopy confirmed the nano-metric size chemical fictionalization through the electrochemical etching that was shown on the PS surface chemical composition. The atomic power microscopy checks showed the roughness of the silicon surface. It is also notified (TiO2) preparation nano-particles that were prepared by pulse laser eradication in e

Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings