Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we modified the Correlation Feature Selection (CFS) with Best First Search (BFS) established on the Discriminant Index (DI) so as to reduce the complexity of time and get high accuracy. Secondly, Bayesian Rough Set (BRS) classifier is applied to predict the breast cancer and help the inexperienced doctors to make decisions without need the direct discussion with the specialist doctors. The result of experiments showed the proposed system give high accuracy with less time of predication the disease.
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The purpose of this study was to examine the role of cortisol, and it is related to BMI in the chronic diseases which may increase early cardiovascular disease (CVD) in old Iraqi. The subjects were 116 adults, aged 51-71 years. Body Mass Index (BMI), Waist Circumferences (WC) and Waist Hip Ratio (WHR) were used as a measure of adiposity. Investigation showed highly significant difference between patients in BMI ranges, most of male were in an obese weight range (48.5%), as well in women. There were no significant correlations between serum cortisol concentration and age both gender groups. While there were highly significant correlations between cortisol level and BMI, waist, and WHR (except in female subjects), also there were highly signi
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In this work, a step-index fiber with core index and cladding index has been designed. Single-mode operation can be obtained by using a fiber with core diameters 4–13 µm operating at a wavelength of 1.31 µm and by 4–15 µm at 1.55 µm. The fundamental fiber mode properties such as phase constant, effective refractive index, mode radius, effective mode area and the power in the core were calculated. Distributions of the intensity and the amplitude were shown.
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The aim of this paper is to introduce and study the concept of SN-spaces via the notation of simply-open sets as well as to investigate their relationship to other topological spaces and give some of its properties.
Cadastral map environment is directed, more than ever before, towards Artificial Intelligence use to produce fast and accurate maps and keep up with the huge population growth. The traditional approach currently in production of maps is expensive and effort-intensive in addition to be considered as highly time-consuming process. UAV-based cadastral mapping imagery that use automatic techniques are newly being exploited to accelerate the process of production or updating. The state-of-the-art intelligent algorithms are capable to extract land boundaries from images better than conventional techniques. This paper presents an automatic workflow of cadastral map production based on land boundary and automatic f
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med
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In this article, an attempt has been made to introduce the concept of Neutrosophic d-Filter and Neutrosophic Prime d-Filter of d-Algebra by generalizing the notion of Intuitionistic Fuzzy d-Filter of d-Algebra. Besides, we establish different properties of them. Further, we study several relations on this notion from the point of view of Neutrosophic d-Algebra.
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In this article, we introduce a new type of soft spaces namely, soft spaces as a generalization of soft paces. Also, we study the weak forms of soft spaces, namely, soft spaces, soft spaces, soft space, and soft spaces. The characterizations and fundamental properties related to these types of soft spaces and the relationships among them are also discussed.
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In this paper two modifications on Kuznetsov model namely on growth rate law and fractional cell kill term are given. Laplace Adomian decomposition method is used to get the solution (volume of the tumor) as a function of time .Stability analysis is applied. For lung cancer the tumor will continue in growing in spite of the treatment.
Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.