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Iraqi Sentiment and Emotion Analysis Using Deep Learning
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Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) collected from Facebook are used to evaluate the model. Experiments showed that the model obtained good results, as the accuracy of the model was 91.1, 92.4, and 92.5% for IADS, ACMID, and IAD, respectively. The results of the model outperformed previous works for all datasets.

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Publication Date
Thu May 31 2018
Journal Name
International Journal Of Control And Automation
Power Flow Control of Iraqi International Super Grid with Two-Terminal HVDC Techniques Using PSS/E
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Publication Date
Sat Dec 11 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn: 1683 - 3597 , E-issn : 2521 - 3512)
Assessment of Health Beliefs Among Iraqi Breast Cancer Patients in Baghdad using either Tamoxifen or Trastuzumab
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Breast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer. This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients. The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study. The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there were

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Publication Date
Sat Dec 11 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn: 1683 - 3597 , E-issn : 2521 - 3512)
Assessment of Health Beliefs Among Iraqi Breast Cancer Patients in Baghdad using either Tamoxifen or Trastuzumab
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Breast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer. This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients. The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study. The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there were

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Scopus (9)
Crossref (8)
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Publication Date
Sat Dec 11 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Assessment of Health Beliefs Among Iraqi Breast Cancer Patients in Baghdad using either Tamoxifen or Trastuzumab
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Breast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer.

This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients.

The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study.

The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there wer

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Publication Date
Fri Jan 01 2021
Journal Name
Clinical Epidemiology And Global Health
Association of overweight/obesity with the severity of periodontitis using BPE code in an iraqi population
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Publication Date
Mon Nov 03 2025
Journal Name
Frontiers In Dental Medicine
Effects of cavity depth (moderate vs. deep with pulp exposure) on the release of prostaglandin E2 and nitric oxide in rat mandibular incisors
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Background/objectives: Inflammatory mediators such as prostaglandin E2 (PGE2) and nitric oxide (NO) are key indicators of pulp response to mechanical trauma. However, the influence of cavity depth on their release dynamics remains unclear. This study aimed to evaluate the effects of different cavity depths—moderate (without pulp exposure) and deep (with pulp exposure)—on the release of PGE2 and NO in the pulp tissue of rat mandibular incisors at two time intervals (3 and 9 h).Methods: In total, 40 male Wistar rats were divided into two main groups (n = 20) based on cavity depth. A split-mouth design was used, with cavities of different depths prepared on the left mandibular incisors, leaving the right incisors without cavities as

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Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

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Publication Date
Fri Jul 30 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm
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The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

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Publication Date
Sat Feb 01 2025
Journal Name
Journal Of Energy Storage
Massive energy reduction and storage capacity relative to PCM physical size by integrating deep RL clustering and multi-stage strategies into smart buildings to grid reliability
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