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

Atrial fibrillation is associates with elevated risk of stroke. The simplest stroke risk assessment schemes are CHADS₂ and CHA₂DS₂-VASc score. Aspirin and oral anticoagulants are recommended for stroke prevention in such patients.

The aim of this study was to  assess status of CHADS₂ and CHA₂DS₂-VASc scores in Iraqi atrial fibrillation patients and to report current status of stroke prevention in these patients with either warfarin or aspirin in relation to these scores.

This prospective cross-sectional study was carried out at Tikrit, Samarra, Sharqat, Baquba, and AL-Numaan hospitals from July 2017 to October 2017. CHADS₂

Summarized the idea of research is marked by "changes in the process of mass communication by using the international network of information" by specifying what data networking and mass communication is the transformation processes in the mass communication network where research aims to:1. Diagnostic data and transformations in the process of mass communication network.2. Provide a contact form commensurate with the characteristic mass of the International Network of electronic information, and research found to provide a communicative model called the (human contact network). In short (HCN) Humanity Communication Net also reached conclusions concerning the search process and communicative transformations and changes that have taken pla

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 Arabi

The aim of the research is to find out the effect of applying classroom assessment techniques (CATs) on both mathematical and logical thinking among fourth-grade scientific students. In pursuit of the research objectives, the experimental method was used, and the quasi-experimental design was used for two equivalent groups, one control group taught in the traditional way and the other experimental taught according to the techniques of classroom structural evaluation. The research sample consisted of (44) students from the fourth scientific grade who were intentionally chosen after ensuring their equivalence in several factors, most notably chronologi-cal age and the level of mathematics, and they were distributed equally among the t

A solar cell was manufactured from local materials and was dyed using dyes extracted from different organic plants. The solar cell glass slides were coated with a nano-porous layer of Titanium Oxide and infused with two types of acids, Nitric acid and Acetic acid. The organic dyes were extracted from Pomegranate, Hibiscus, Blackberry and Blue Flowers. They were then tested and a comparison was made for the amount of voltage they generate when exposed to sunlight. Hibiscus sabdariffa extract had the best performance parameters; also Different plants give different levels of voltage.

Economic organizations operate in a dynamic environment, which necessitates the use of quantitative techniques to make their decisions. Here, the role of forecasting production plans emerges. So, this study aims to the analysis of the results of applying forecasting methods to production plans for the past years, in the Diyala State Company for Electrical Industries.

The Diyala State Company for Electrical Industries was chosen as a field of research for its role in providing distinguished products as well as the development and growth of its products and quality, and because it produces many products, and the study period was limited to ten years, from 2010 to 2019. This study used the descriptive approa

Five representative sampling stations were selected in upper region of Euphrates river. Bimonthly sampling were collected from December 2000 to December 2001. Rotifera showed high density in December 2000 while high density of cladocera which recorded in October .The results of relative abundance index showed that rotifera: Polyarthera dolichoptera , Keratella cochlearis , K. valga, Cephalodella auriculata and cladocera: Bosmina longirostris , B.coregoni ,Chydorus spharicus, were more abundant in study stations. The results of constancy index showed 4 taxa belonged to rotifera and 2 taxa belonged to cladocera which were considered constant in the Euphrates river, where the other species varied between accessory and accidental speci

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri