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Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning models for a variety of tasks under the control of a unified architecture for each proposed model.

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Publication Date
Thu Mar 31 2022
Journal Name
American Journal Of Economics And Business Management
Measuring and Analyzing the Factors Affecting the Behavior of Government Spending in Iraq Using the Co-Integration Test for the Period 2000-2020
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Government spending is the tool that the state uses to achieve its various goals. The research aims to identify the most important determinants of government spending in Iraq and to indicate the type and nature of the relationship between government spending and its determinants, which will contribute to understanding the movement of government spending. The results of the co-integration test using the border test methodology showed that the variables of population growth and oil prices have a long-term effect on government spending while inflation is not significant in the long run, and that 47% of the equilibrium imbalance (short-term imbalance) in government spending in the previous period (t-) can be corrected in the current period (t)

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals
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Publication Date
Sun Dec 01 2024
Journal Name
Alexandria Engineering Journal
Rutting and fatigue behavior of neat and nanomodified asphalt mixture with SiO2 and TiO2
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Modern asphalt technology has adopted nanomaterials as an alternative option to assert that asphalt pavement can survive harsh climates and repeated heavy axle loading during service life and prolong pavement life. This work aims to elucidate the behavior of the modified asphalt mixture fracture model and assess the fatigue and Rutting performance of Hot Mix Asphalt (HMA) mixes using the outcomes of indirect Tensile Strength (IDT), Semicircular bend (SCB) and rutting resistance; for this, a single PG (64−16) nanomodified asphalt binder with 5 % SiO2 and TiO2 have been investigated through a series of laboratory tests, including: Resilient modulus, Creep compliance, and tensile strength, SCB, and Flow Number (FN) to study their potential

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Publication Date
Tue Jun 30 2015
Journal Name
International Journal Of Computer Techniques
Multifractal-Based Features for Medical Images Classification
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This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

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Publication Date
Wed Aug 17 2022
Journal Name
Aip Conference Proceedings
The effect of using Gaussian, Kurtosis and LogCosh as kernels in ICA on the satellite classification accuracy
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This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

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Publication Date
Mon Oct 10 2016
Journal Name
Iraqi Journal Of Science
Satellite image classification using KL-transformation and modified vector quantization
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In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

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Publication Date
Wed Jan 01 2025
Journal Name
Fusion: Practice And Applications
Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

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Publication Date
Fri Sep 15 2023
Journal Name
Journal Of Baghdad College Of Dentistry
Evaluating the effect of natural, industrial juices and beverage on orthodontic bonding composite (in-vitro study)
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Background: Dental erosion is a common oral condition which results due to consumption of high caloric and low pH acidic food such as carbonated drinks and fruit juices. It is expected that these food types can cause irreversible damage to dental hard tissues and early deterioration of the dental restorations. So, this study aimed to evaluate and compare the erosive potential effects of orange fruit juice and Miranda orange drink on the microhardness of an orthodontic composite material. Materials and methods: Thirty discs with a thickness of 2 mm and a diameter of 10 mm were prepared from orthodontic bonding composite. The prepared discs were equally divided into three groups (n=10). Microhardness analysis was carried out both prior to

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Publication Date
Sun Jul 01 2018
Journal Name
Journal Of Construction Engineering And Management
Measuring and Evaluating Safety Maturity of Construction Contractors: Multicriteria Decision-Making Approach
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Publication Date
Tue Jan 08 2019
Journal Name
Iraqi Journal Of Physics
Effect of TiO2 on the sintering behavior and microstructure of stoichiometric spinel (MgAl2O4)
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In this work, magnesium aluminate spinel (MA) (MgO 28 wt%, Al2O3 72 wt%) stoichiometric compound , were synthesized via solid state reaction (SSR) Single firing stage, and the impact of sintering on the physical properties and thermal properties as well as the fine structure and morphology of the ceramic product were examined. The Spinel samples were pressed at of (14 MPa) and sintering soaking time (2h). The effect of adding oxide titania (TiO2) was studied. The obtained powders were calcined at a temperature range of 1200 and 1400 °C. The calcined samples spinel were characterized by XRD, it showed the presence of developed spinel phase end also showed that the best catalyst is titania. The SEM image showed the high sintering temperat

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