Among the issues that preoccupied ancient and modern grammarians is the phenomenon of grammatical disagreement among grammarians and their differences in many grammatical issues that many of them go back to the phenomenon of dialectical difference, so this filled our thinking with a lot of things that led us to look at the side of the reasons for this grammatical difference, and here lies the significance of research to know that it is a linguistic phenomenon dealing with the living language used among the Arabs and linguistic phenomena that reflect linguistic reality as well. Thus our chagrin determination in research and investigation in this issue is the scarcity of dialectical studies at the compositional grammar side, and the lack of a competent study on grammatical disagreement which are taken dialects as the main instrument and main reason in the field of study. In addition, lack of a research to talk about multifaceted grammatical rules; the multiplicity of cases of syntax, or to the different syntactic positions, hence the significance of our research is to indicate the reason for multifaceted grammatical rules, which is the dialectical difference. Therefore, the research is to study this problem explaining the main reasons behind the grammatical disagreement and its impact on the multiplicity of grammatical rules, namely the dialectical differences. Thus, the research is divided into three sections: the first section is devoted to the research terms, the second one is devoted to the definition of dialectical difference and to know the its causes and consequences, the third and the last one is about dialectical difference issues that have had an impact on the multiplicity of grammatical rules that reached twenty-one issues.
Key words: Multiple, object, provisions, disagreement, discord, grammatical
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
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