The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.

  In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.

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This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between ev

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every

Importance of the research:
The importance of any educational or scientific research through its intellectual arena of facts supply the individual and society, Supports Knowledge and Science Group, which raised will be locked in these topics in the future.
This research seeks to shed light on the image and the role of women and men in the books of the Arabic language in primary education (primary), To illustrate the negative effects of the phenomenon of sexism in textbooks, And its negative impact on emerging, And stay away as much as possible about the distinction between the sexes in the roles and qualities in textbooks traditional stereotypes and remove that put both sexes templates hinder the development of the individual and t

The research aimed to study the role that the media play in shaping the public knowledge of human rights issues among the people of Kirkuk, which will be the focus of the study. The research was conducted by applying a survey panel to a random sample of the city's audience. The research dealt with the theoretical aspect of a theory that relied on the media, and the loans provided by the theory, on the basis of which the research was conducted and the research problem was determined based on a major question: What is the role that the mass media play in developing the knowledge of members of the public on human rights and the relationship between the intensity of view in that, as well as the identification of the effect of two variables G

The research topic is summarized in the importance of studying the measuring the extent of the university youth’s exposure in the Emirati Society to those series and the resulting achieved satisfaction. The most important results and recommendations of the study are as follows: a high rate of the respondents’, sample individuals, exposure to the dubbed Turkish series since it is evident that almost three-fourths of the study individuals watch the dubbed Turkish series,.”. The most significant positive aspects of the dubbed Turkish series are: “they focus on the most important tourist attractions in Turkey” and “ improving the audience›s knowledge and information on the traditions of the Turkish society”. The most apparent

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t