Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.

This study aims to deliver the woman’s image and to unveil on how to be introduced in the TV series. The research is based on the survey method-using content analysis tool. The research sample represented in the TV series produced by the IMN, which were displayed in 2014 and used the pattern of Margaret Gallagher to analyze the content of the series in accordance with the frame analysis theory.
The study came up with declination of the woman’s representation compared with man in Iraqi TV drama, also the study finds that the series introduced the woman according to the personal, social, political, and economic frames in a standardizing method. It focuses on the characteristics always attributed to it as showing her obedient of the

Objectives: The main objective of this study is to examine the crucial role that Russian writers and intellectuals played in catalyzing the 1905 Revolution in Russia. Specifically, the study sought to analyze how their literary works, philosophical ideas, and political writings challenged Tsarist autocracy, depicted the suffering of the masses, and inspired opposition movements. The study aimed to highlight the defiant writings that contributed to the rise of political figures and the eventual overthrow of the repressive regime. Methods: The historical narrative and analytical methods were employed, conducting a comparative analysis of prominent works by renowned authors such as Tolstoy, Gorky, and Chekhov. This analysis focused on

IH Abdul-Abbas, QJ Rashid, M RasimYounus, PalArch's Journal of Archaeology of Egypt/Egyptology, 2021 - Cited by 9

The current study aims to identify the needs in the stories of the Brothers Grimm. The research sample consisted of (3) stories, namely: 1- The story of the Thorn Rose (Sleeping Beauty) 2- The story of Snow White 3- The story of Little Red Riding Hood. The number of pages analyzed reached (15.5) pages, and to achieve the research objectives, Murray's classification of needs was adopted, which contains (36) basic needs that are further divided into (129) sub-needs. The idea was adopted as a unit of analysis and repetition as a unit of enumeration, Reliability was extracted in two ways: 1- Agreement between the researcher and himself over time, where the agreement coefficient reached 97%. The second was agreement between the researcher and tw

The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To