The research is a contemporary stylistic study of the text that expresses the psychological
meanings in Nahj Al-Balaghah.
The research is divided to three chapters.
The first chapter : characteristics of psychological text.
It is about the power to influence the addressee ,that is to say ,the text influence power
connects with vulnerability and emotions inside the addressee spirit and his response to
violent effects shaking his feelings and that is represented in outbreaking of words vitality and
homogeneously. The Imam's wrath , grief , love to rightness , hatred of falsehood and
deviation from Islam arouses the words and emotions through :
1. Reduction in words and expansion in meaning by means of inspiration of the intentional
meaning.
2. Consistency and coherence: illustrates the connection between words and forms of
consistency.
3. Homogeneity between the components of the sentence to illustrate the closeness and
attachment of words by formatives and grammatical links.
4. Script overlap between The Holy Quran and Nahj Al-Balaghah in those texts.
The second chapter : coherence in The Holy Quran text whether among It's words or phrases .
The most important links are : conjunctions , conductive names , linking by meaning change ,
linking by pronouns , linking by demonstrative pronouns and linking by repetition.
The third chapter : structural phenomena in psychological text which consist : elaboration and
detailing in meaning or structural expansion of the the sentences , repetition , sequence ,
diversification in syntax composition , conformity and violation. All these phenomena are
common in the texts of Nahj Al-Balaghah.
Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
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... Show MoreObjectives: To assess the woman satisfaction with nursing care during labor.
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Results: The study sample indicated that in general the women were satisfied in nursing care that provided during labor.
Recommendations: The study recommended educational tra
... Show MoreEdward Albee, as a playwright, indicates that art should be useful and have a message. Therefore, his work foregrounds and critically examines issues concerning the Neurosis of Blackness and psychological trauma. Albee uses cruelty of racism in reflecting psychological trauma and emotional abuse of American black identity in his plays. Race, social inequality, and gender still sustain to engender controversy audience consciously. Racial discrimination is one of the major issues that affect the American Society. Albee challenges and exposes the presumptive dreams of equality of American society and institutional racism. Therefore, one of the main problems of the twentieth century in America is skin color. It
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