The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of 88% and an Accuracy of almost 89%. We also came to the conclusion that the Fibroid mass is small and less white than the Fatty mass
In this research weights, which are used, are estimated using General Least Square Estimation to estimate simple linear regression parameters when the depended variable, which is used, consists of two classes attributes variable (for Heteroscedastic problem) depending on Sequential Bayesian Approach instead of the Classical approach used before, Bayes approach provides the mechanism of tackling observations one by one in a sequential way, i .e each new observation will add a new piece of information for estimating the parameter of probability estimation of certain phenomenon of Bernoulli trials who research the depended variable in simple regression linear equation. in addition to the information deduced from the past exper
... Show MoreThe research aims at identifying The Effect of Woods model in the Acquisition of Grammatical Concepts on students at the Sixth Primary. The total number of students is (49) male and female. students are distributed in two groups . The First group is the experimental group which is taught the( Woods Model) and it is (25) students . The second group is the control group which is taught according to the traditional method of teaching and it is (24) students . The researcher has matched between the two groups by the following variables : the age (in months), their intelligence, their parents, academic level of education . The research has constructed multiple choice test of (20) items . The reliability of The test has been calculated and it
... Show MoreThe influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreEffective management of advanced cancer requires systemic treatment including small molecules that target unique features of aggressive tumor cells. At the same time, tumors are heterogeneous and current evidence suggests that a subpopulation of tumor cells, called tumor initiating or cancer stem cells, are responsible for metastatic dissemination, tumor relapse and possibly drug resistance. Classical apoptotic drugs are less effective against this critical subpopulation. In the course of generating a library of open-chain epothilones, we discovered a new class of small molecule anticancer agents that has no effect on tubulin but instead kills selected cancer cell lines by harnessing reactive oxygen
In this paper has been one study of autoregressive generalized conditional heteroscedasticity models existence of the seasonal component, for the purpose applied to the daily financial data at high frequency is characterized by Heteroscedasticity seasonal conditional, it has been depending on Multiplicative seasonal Generalized Autoregressive Conditional Heteroscedastic Models Which is symbolized by the Acronym (SGARCH) , which has proven effective expression of seasonal phenomenon as opposed to the usual GARCH models. The summarizing of the research work studying the daily data for the price of the dinar exchange rate against the dollar, has been used autocorrelation function to detect seasonal first, then was diagnosed wi
... Show MoreThis research aims to present a proposed model for disclosure and documentation when performing the audit according to the joint audit method by using the questions and principles of the collective intelligence system, which leads to improving and enhancing the efficiency of the joint audit, and thus enhancing the confidence of the parties concerned in the outputs of the audit process. As the research problem can be formulated through the following question: “Does the proposed model for disclosure of the role of the collective intelligence system contribute to improving joint auditing?”
The proposed model is designed for the disclosure of joint auditing and the role
... Show MoreReceipt Date:10/11/2021 Acceptance Date:29/12/2021 Publication Date:31/12/2021
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The study aimed to clarify the conceptual explanations and the theoretical rooting of the concept of the populist phenomenon. And explore the political and cultural implications and connotations contained in populist political discourse. And to stand on the foundations and meanings on w
... Show MoreIn Indonesia, cattle feces (CF) and water hyacinth (WH) plants are abundant but have not been widely revealed. The use of microorganisms as decomposers in the fermentation process has not been widely applied, so researchers are interested in studying further. This study was to evaluate the effect of the combination of CF with WH on composting by applying white-rot fungal (WRF) (Ganoderma sp) microorganism as a decomposer. A number of six types of treatment compared to R1(ratio of CF:WH)(25%:75%)+WRF; R2(ratio of CF:WH)(50%:50%)+WRF; R3(ratio of CF:WH)(75%:25%)+WRF; R4(ratio of CF:WH)(25%:75%) without WRF; R5(ratio of CF:WH)(50%:50%) without WRF; R6(ratio of CF:WH)
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