Arabic language processing with artificial intelligence has evolved significantly in the past decades, from traditional rule- and dictionary-based techniques, through statistical models to modern deep and transformer models. This review intends to present an overview of the most well-known Arabic models as well as datasets used for its training, and the main practical applications such as sentiment analysis, machine translation, speech recognition, and smart assistant. AI-based Arabic NLP has had good progress in the previous decades, from rule and dictionary-based approaches to statistical methods and deep transformative learning models nowadays. In addition to it, the most popular state-of-the-art models that are fine-tuned for the Arabic language and their corpora of training data will be considered as well as major applications such as Sentiment Analysis, Machine Translation, Speech Recognition, and Virtual Assistant. This article review outlines the necessity for investment in language resources and advanced models to improve AI systems’ ability to accurately understand Arabic natural language, a contribution that will support real-life applications and smart services associated with its present formalized variant of AI model capabilities.
This study Arabic dialect prevailing in the province of Khuzestan [southwest Islamic Republic of Iran] as one of the Arabic dialects abundant qualities and characteristics of linguistic entrenched in the foot, which includes among Tithe thousands composed of vocabulary and structures and phrases classical that live up to the pre-Islamic era, if what Tasha researcher and reflect accurately the find of a large number of phrases and vocabulary and acoustic properties by nature accent, and formal, and nature of the synthetic, and characteristics semantic and contextual in this dialect studied without being something of them heavy on the tongue and without displays her tune or Tasha or distortion and so on all of which constitute a catalyst i
... Show MoreThis paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark
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Arabic language the timeless language of the Qur'an ((I made it read Arabic you may understand)) (Al-Zukhruf No 3). It is a way to express his thoughts and feelings and capable of giving constant absorbed the needs of the times and meet the General requirements and interactive needs of life to achieve social and intellectual aspects.
The aim of current research to identify what teaching the contemporary trends in teaching, what the nature of knowledge and teaching methods, how do you rate the modern Arabic language teaching methods, the criteria for evaluating the effectiveness of teaching where the researcher has reached some recommendations: 1. establish the basics of environmental ed
... Show MoreAbstract. This research examines the role of artificial intelligence (AI) in improving and sustaining agricultural production in the Arab world, with a particular focus on the challenges facing Iraq in implementing this technology. It reviews several developments, such as big data, the Internet of Things (IoT), and drone technology, which improve irrigation, increase soil fertility, and reduce waste. The study highlights the diverse applications of AI in crop production and pest control, while revealing Iraq's infrastructure challenges and the need for better training for farmers, To find effective solutions, the research recommends collaboration between governments and the private sector.
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
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The problem of the research is indicated by answering the following questions: 1) what are the attitudes of the Arabic language teachers towards homework?
2) Do the Arabic language teachers differ in their attitudes towards homework according to their specialization? 3) Do the Arabic language teachers differ in their attitudes towards homework according to their gender (male and female)? According to the three questions asked above, we may state the following hypotheses. There is no statistically significant difference between the average of the marks of Arabic language teachers in the real practice of the scale of the attitudes towards their homework and their average in the hypothetical practice of the
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Background: The rapid integration of Artificial Intelligence (AI) into healthcare necessitates that nursing education evolves to equip students with essential technological competencies. Objectives: To explore pediatric nursing students' perceptions of AI in nursing and analyze associations with sociodemographic factors and prior AI knowledge. Methods: A descriptive cross-sectional study was conducted from December 2024 to March 2025 across five universities in Baghdad. A non-probability sample of 500 pediatric nursing students completed the Shinners Artificial Intelligence Perception (SAIP) tool. Data were analyzed using descriptive statistics and inferential comparisons (t-tests/ANOVA) via SPSS. Results: Participants had a mean ag
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
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