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Sentiment Analysis in Social Media using Machine Learning Techniques
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Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

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Publication Date
Sun Dec 05 2021
Journal Name
Iraqi Journal Of Science
Climatic Analysis and Climatic Water Balance Determination for Al- Yusufiyah Area, Southern Baghdad, Iraq
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The Climatic parameters for the years (1985-2015) were collected from Baghdad
meteorological station and then were applied to evaluate the climatic conditions for
the Al-Yusufyiah area south Baghdad. The total annual rainfall is (119.65 mm),
while the total annual evaporation is (3201.7 mm), relative humidity is (43.62%),
sunshine (8.76 h/day), temperature (23.28 C◦) and wind speed (3.06 m/sec). Climate
of the study area is described as an arid according to classification of (Kettaneh and
Gangopadhyaya, 1974), (Mather, 1973), and (Al-Kubaisi, 2004). Mean monthly
water surplus for the period (1985-2015) was recorded in the study area about (4.7
mm) in November, (11.67 mm) in December, (20.56 mm) in January and (6

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Publication Date
Tue Dec 13 2022
Journal Name
Mathematical Modelling Of Engineering Problems
Dynamic Response and Reliability Analysis of Stochastic Multi-Story Frame Structures under Random Excitation
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In earthquake engineering problems, uncertainty exists not only in the seismic excitations but also in the structure's parameters. This study investigates the influence of structural geometry, elastic modulus, mass density, and section dimension uncertainty on the stochastic earthquake response of a multi-story moment resisting frame subjected to random ground motion. The North-south component of the Ali Gharbi earthquake in 2012, Iraq, is selected as ground excitation. Using the power spectral density function (PSD), the two-dimensional finite element model of the moment resisting frame's base motion is modified to account for random ground motion. The probabilistic study of the moment resisting frame structure using stochastic fin

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Publication Date
Mon Feb 02 2015
Journal Name
Journal Of Engineering, University Of Baghdad
'Impact Analysis of Reinforced Concrete Columns with Side Openings Subjected to Eccentric Axial Loads
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Publication Date
Sat Oct 31 2020
Journal Name
Iraqi Journal Of Agricultural Sciences
PERFORMANCE AND MOLECULAR ANALYSIS OF POTATO LINES DEVELOPED FROM GAMMA RAYS AND EMS APPLICATIONS
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An experiment was conducted in a greenhouse - research station B - College of Agricultural Engineering Sciences, University of Baghdad, during the fall season of 2018 with the aim of propagating and initially studying the field performance of 18 and 20 potential potato lines derived from Rivera and Arizona cv. after in vitro exposure of nodal segments to different dosages of gamma rays (0, 10, 20, and 30 Gray) and EMS (0, 10, 20, and 30 mM). Each control cultivar and their derived lines were independently cultured in plastic bags according to the RCBD, with three replications. The results showed that the highest plant height and number of leaves were obtained from Arizona derived lines which gave 60.11 cm and 25.30 leaves.plant-1 in

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Publication Date
Tue Jan 31 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Survey on intrusion detection system based on analysis concept drift: Status and future directions
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

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Publication Date
Sat Feb 08 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Finite element stress analysis study for stresses around mandibular implant retained overdenture MIR-OD
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Background: It has been well known that the success of mandibular implant- retained overdenture heavily depends on initial stability, retention and long term osseointegration this is might be due to optimal stresses distribution in surrounding bones. Types of mandibular implant- retained overdenture anchorage system and number of dental implants play an important role in stresses distribution at the implant-bone interface. It is necessary to keep the stresses below the physiologic tolerance level of the bone .since. And it is difficult to measure these stresses around bone in vivo. In the present study, finite element analysis used to study the stresses distribution around dental implant supporting Mandible implant retained overdenture Mate

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Publication Date
Wed Feb 03 2021
Journal Name
Structural Concrete
Finite element analysis of rectangular RC beams strengthened with FRP laminates under pure torsion
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction
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Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

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Publication Date
Fri Dec 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
Performance Analysis for Hybrid Massive MIMO FSO/RF Links Based on Efficient Channel Codes
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Buckling and Pre Stressed Dynamics Analysis of Laminated Composite Plate with Different Boundary Conditions
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Critical buckling and natural frequencies behavior of laminated composite thin plates subjected to in-plane uniform load is obtained using classical laminated plate theory (CLPT). Analytical investigation is presented using Ritz- method for eigenvalue problems of buckling load solutions for laminated symmetric and anti-symmetric, angle and cross ply composite plate with different elastic supports along its edges. Equation of motion of the plate was derived using principle of virtual work and solved using modified Fourier displacement function that satisfies general edge conditions. Various numerical investigation were studied to exhibit a convergence and accuracy of the present solution for considering some design parameters such as edge

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