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

 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.

This study explored the development and qualities of the response of electrochemical properties of enrofloxacin-selective electrodes using precipitation based on producing phosphotungstic, after utilizing a matrix of polyvinyl chloride (PVC) and dibutyl phthalate or dibutyl phosphate as a plasticizer. The resulting membrane sensors were an enrofloxacin-phosphotungstic electrode (sensors 1) and an ENR-DOP-PTA electrode (sensors 2). Linear responses of (ENR-DBPH-PTA) and (ENR-DOP-PTA) within the concentration ranges of 2.1×10^-6-10^-1 and 3.0×10^-6-10^-2 mol. L^-1, respectively, for both sensors were observed. Slopes of 51.61±0.24 and 39.40± 0.16 mV/decade and pH ranges equal to 2.5-8.5

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

Remote sensing provide the best means to monitoring change in vegetation over a wide range of temporal scales over large areas. In this study, the vegetation index which has been applied known as the Stress Related Vegetation Index (STVI) on in the area around the Euphrates River and part of Al-Habbaniyah lake which located at western side of the river in Ramadi city, Al-Anbar province at Iraq to study the vegetation cover changes and detect the areas of changes, using two satellite sensors multispectral images such as TM and ALI, after geometric correction procedure to rectifying these images. The STVI-4 index result was the best than other vegetation indices (STVI-1 and STVI-3) to discriminate the vegetable cover distribution. The diff

     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

Load balancing in computer networks is one of the most subjects that has got researcher's attention in the last decade. Load balancing will lead to reduce processing time and memory usage that are the most two concerns of the network companies in now days, and they are the most two factors that determine if the approach is worthy applicable or not. There are two kinds of load balancing, distributing jobs among other servers before processing starts and stays at that server to the end of the process is called static load balancing, and moving jobs during processing is called dynamic load balancing. In this research, two algorithms are designed and implemented, the History Usage (HU) algorithm that statically balances the load of a Loaded

Genetic polymorphisms of genes whose products are responsible for activities, such as xenobiotic metabolism, mutagen detoxification and DNA-repair, have been predicted to be associated with the risk of developing lung cancer (LC). The association of LC with tobacco smoking has been extensively investigated, but no studies have focused on the Arab ethnic- ity. Previously, we examined the association between genetic polymorphisms among Phase I and Phase II metabolism genes and the risk of LC. Here, we extend the data by examining the correlation of OGG1 Ser326Cys combined with CYP1A1 (Ile462Val and MspI) and GSTP1 (Ile105Val and Ala103Val) polymorphisms with the risk of LC. Polymerase chain reaction- restriction fragment length polymorphism (

The extracting of personal sprite from the whole image faced many problems in separating the sprite edge from the unneeded parts, some image software try to automate this process, but usually they couldn't find the edge or have false result. In this paper, the authors have made an enhancement on the use of Canny edge detection to locate the sprite from the whole image by adding some enhancement steps by using MATLAB. Moreover, remove all the non-relevant information from the image by selecting only the sprite and place it in a transparent background. The results of comparing the Canny edge detection with the proposed method shows improvement in the edge detection.