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Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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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 ADR.

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Publication Date
Mon Dec 13 2021
Journal Name
International Journal Of Reproductive Biomedicine (ijrm)
Differences in perinatal outcomes in teenage mothers with their first and third pregnancies and predictors of adverse neonatal events: A cross-sectional study
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Background: Repeated teenage pregnancy is a major burden on the healthcare system worldwide. Objective: We aimed to compare teenagers with their first and third pregnancies and to evaluate the likelihood of neonatal complications. Materials and Methods: This cross-sectional study was performed on female teenagers (aged ≤ 19 yr) with singleton pregnancies. The subjects (n = 298) were screened over 12 months. Ninety-six women were excluded, based on the exclusion criteria. The remaining subjects (n = 202) were divided into two groups: teenagers with first pregnancy (n = 96) and teenagers with third pregnancy (n = 47). The subjects were observed throughout pregnancy and delivery. The final sample size of the first and thi

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Publication Date
Thu Mar 31 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Permeability Prediction in One of Iraqi Carbonate Reservoir Using Hydraulic Flow Units and Neural Networks
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Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.

   This paper will try to develop the permeability predictive model for one of  Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).

   Histogram analysis, probability analysis and Log-Log plot of Reservoir Qua

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Publication Date
Fri Jul 26 2019
Journal Name
Dental Materials Journal
Semi-interpenetrating network composites reinforced with Kevlar fibers for dental post fabrication
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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Engineering
A Realistic Aggregate Load Representation for A Distribution Substation in Baghdad Network
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Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based

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Publication Date
Wed Jun 01 2016
Journal Name
Lap Lambert Academic Publishing
Schiff Base And Ligand Metal Complexes of Some Amino Acids and Drug
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Schiff Base And Ligand Metal Complexes of Some Amino Acids and Drug

Publication Date
Wed Jan 01 2020
Journal Name
Iraqi National Journal Of Nursing Specialties
Effectiveness of an educational program on nurses' knowledge regarding management of extravasation vesicant intravenous chemotherapy at oncology centers in Baghdad city
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Objectives: to determine the effectiveness of an Education Program on Nurses' Knowledge regarding management of extravasation vesicant intravenous chemotherapy

Methodology: quiz-experimental study (single-group pretest-posttest1 and posttest2) was directed in Amal oncology center and national oncology center in Baghdad city from 13th, December 2018 to the 7 of February 2019. The program and tool have been created by the researcher for the purpose of the study. A non- probability purposive sample of (40) nurses who employed in Baghdad oncology centers. Validity and reliability of the instrument were determined through a pilot study. Data were analyzed through the use of Statistical Pack

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Publication Date
Mon Apr 30 2018
Journal Name
Journal Of Theoretical And Applied Information Technology
An efficient artificial fish swarm algorithm with harmony search for scheduling in flexible job-shop problem
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Flexible job-shop scheduling problem (FJSP) is one of the instances in flexible manufacturing systems. It is considered as a very complex to control. Hence generating a control system for this problem domain is difficult. FJSP inherits the job-shop scheduling problem characteristics. It has an additional decision level to the sequencing one which allows the operations to be processed on any machine among a set of available machines at a facility. In this article, we present Artificial Fish Swarm Algorithm with Harmony Search for solving the flexible job shop scheduling problem. It is based on the new harmony improvised from results obtained by artificial fish swarm algorithm. This improvised solution is sent to comparison to an overall best

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Sun Jan 01 2017
Journal Name
Proceeding Of Second Thermal And Fluids Engineering Conference
Solidification Enhancement in Triplex-Tube Latent Thermal Energy Storage System Using a Combination of Nanoparticles and Fins
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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Intelligent Systems And Applications In Engineering
Artificial Intelligence Based Statistical Process Control for Monitoring and Quality Control of Water Resources: A Complete Digital Solution
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