Introduction: Melanin is a high-molecular weight pigment produced through the oxidative polymerization of phenolic or indolic compounds and plays a perfect role in UV-light shielding, as well as in photoprotection. Among biopolymers, melanin is unique in many aspects. This study is designed to screen Production, extraction and characterizes of an extracellular melanin pigment from clinically isolated P. aeruginosa. Objective: The aim of the current study is isolation and diagnosis of P.aeruginosa using vitek-2 compact system and screening the ability to produce melanin and characterization of extracted melanin by UV-vis, FTIR, XRD and SEM. Materials and methods: the samples swab inoculated on cetrimide agar as selective media and incubated

      The problem of text recognition and its applicability as part of images captured in the wild has gained a significant attention from the computer vision community in recent years. In contrast to the recognition of printed documents, scene text recognition is a difficult problem. Contrary to recognition of printed documents, recognizing a scene text is a challenging problem. Many researches focus on the problem of recognizing text extracted from natural scene images. Significant attempts have been made to address this problem in recent past. However, many of these attempts work on utilizing availability of strong context, which naturally limits the dictionary. This paper presents a review of recent papers related to scene text

Breast cancer is the second deadliest disease infected women worldwide. For this
reason the early detection is one of the most essential stop to overcomeit dependingon
automatic devices like artificial intelligent. Medical applications of machine learning
algorithmsare mostly based on their ability to handle classification problems,
including classifications of illnesses or to estimate prognosis. Before machine
learningis applied for diagnosis, it must be trained first. The research methodology
which isdetermines differentofmachine learning algorithms,such as Random tree,
ID3, CART, SMO, C4.5 and Naive Bayesto finds the best training algorithm result.
The contribution of this research is test the data set with mis

The gaps and cracks in an image result from different reasons and affect the images. There are various methods concerning gaps replenishment along with serious efforts and proposed methodologies to eliminate cracks in diverse tendencies. In the current research work a color image white crack in-painting system has been introduced. The proposed inpainting system involved on two algorithms. They are Linear Gaps Filling (LGF) and the Circular Gaps Filling (CGF). The quality of output image depends on several effects such as: pixels tone, the number of pixels in the cracked area and neighborhood of cracked area and the resolution the image. The quality of the output images of two methods (linear method: average Peak Signal to Noise Ratio (PS

Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Re

       Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two ty

Medical image segmentation is a frequent processing step in image medical understanding and computer aided diagnosis. In this paper, development of range operator in image segmentation is proposed depending on dermatology infection. Three different block sizes have been utilized on the range operator and the developed ones to enhance the behavior of the segmentation process of medical images. To exploit the concept of range filtering, the extraction of the texture content of medical image is proposed. Experiment is conducted on different medical images and textures to prove the efficacy of our proposed filter was good results.

 Active worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.

     In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of societie

Nanofluid treatment of oil reservoirs is being developed to enhance oil recovery and increase residual trapping capacities of CO2 at the reservoir scale. Recent studies have demonstrated good potential for silica nanoparticles for enhanced oil recovery (EOR) at ambient conditions. Nanofluid composition and exposure time have shown significant effects on the efficiency of EOR. However, there is a serious lack of information regarding the influence of temperature on nanofluid performance; thus the effects of temperature, exposure time and particle size on wettability alteration of oil-wet calcite surface were comprehensively investigated; moreover, the stability of the nanofluids was examined. We found that nanofluid treatment is more efficie