Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

The impact of COVID-19 pandemic on education models was mainly through the expansion of technology use in the different educational programs. Earlier impact of COVID-19 was manifested in the complete and sudden transition to distance education regardless of institution preparedness status. Gradually, many institutions are moving back to on-campus face-to-face education. However, others including all higher education institutions in Iraq are adopting the hybrid education model. This report presents part of the end of semester evaluation survey conducted at the University of Baghdad College of Pharmacy for the Spring 2021 semester. The survey aims to address points of strength and weakness associated with the hybrid education model and spe

In the article we consider features of official style, its functions and factors which influence the definition of the style. The topicality of the issue can be explained by rapid development of market economy which affects in its turn business correspondence. In this regard, there are a lot of cliché, terms and professionalisms appeared recently. Only correct usage of them can serve as a key to successful communication in Russian as well as other languages. This work highlights documents that are part of the diplomatic style such as declarations, credentials, notes, resolutions and other documents. The administrative style can include orders and instructions.

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  This work intends to illustrate the methods of using the authentic literary text in the process of spreading Italian, especially in Baghdad where there is a strong propensity to learn the Italian language. The concept of the language that arises from literature is an idea closely linked to the mentality of the Arab learner towards Italian culture: an idea also created by the first Arabisations of literary texts in the early years of the previous century. The research was carried out in Baghdad by two researchers, an Italianist from Baghdad and an Italian mother language linguist, with the aim of bringing together the two sectors in favor of the diffusion of the Italian language. The study also aims to clarify the models from Italian l

A confluence of forces has brought journalism and journalism education to a precipice. The rise of fascism, the advance of digital technology, and the erosion of the economic foundation of news media are disrupting journalism and mass communication (JMC) around the world. Combined with the increasingly globalized nature of journalism and media, these forces are posing extraordinary challenges to and opportunities for journalism and media education. This essay outlines 10 core principles to guide and reinvigorate international JMC education. We offer a concluding principle for JMC education as a foundation for the general education of college students.

In our research, we dealt with one of the most important issues of linguistic studies of the Holy Qur’an, which is the words that are close in meaning, which some believe are synonyms, but in the Arabic language they are not considered synonyms because there are subtle differences between them. Synonyms in the Arabic language are very few, rather rare, and in the Holy Qur’an they are completely non-existent. And how were these words, close in meaning, translated in the translation of the Holy Qur’an by Almir Kuliev into the Russian language.

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

There are Islamic sources and books that deal with the different doctrinal and differential framework of the Islamic religion with a monistic approach, describing them with embarrassing jurisprudential titles such as: excommunication and expiation, and epithets that carry the characteristics of hatred and rejection, in addition to removing them from the sect and religion, and the reason for that is that the doctrine (A) or (b) considers itself the doctrine that represents only the Islamic religion, and other doctrines are misguided and disbelieving. the reasons behind this - besides the attempt to establish doctrinal and fiqih rooting in claiming truth and escaping from within the foundational differential doctrinal system – is the pol

      Cyberbullying is one of the biggest electronic problems that takes multiple forms of harassment using various social media. Currently, this phenomenon has become very common and is increasing, especially for young people and adolescents. Negative comments have a significant and dangerous impact on society in general and on adolescents in particular. Therefore, one of the most successful prevention methods is to detect and block harmful messages and comments. In this research, negative Arabic comments that refer to cyberbullying will be detected using a support vector machine algorithm. The term frequency-inverse document frequency vectorizer and the count vectorizer methods were used for feature extraction, and the results wer

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD