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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
Sat Jan 01 2022
Journal Name
1st Samarra International Conference For Pure And Applied Sciences (sicps2021): Sicps2021
Circularity segmentation for the groups 𝒫𝒮𝓛(2,23) and 𝒫𝒮𝓛(2,29)
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Publication Date
Sat Aug 23 2025
Journal Name
University Of Anbar Sport And Physical Education Sciences
Anxiety and its relationship to the performance of the front hand hop followed by an anterior spherical flip with a half lap on the jumping platform
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
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Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar

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Publication Date
Thu Oct 10 2019
Journal Name
Plant Archives
A study of qualitative, classification soil algae in some areas from Baghdad, Iraq
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A study of taxonomic quality of soil algae was conducted with some environmental variables in three sites of local gardens (Kadhimiya, Adhamiya and Dora) within the governorate of Baghdad for the period from October 2016 to March 2017. The study identified 28 species belonging to 16 species in which the predominance of blue green algae (18 species) Followed by Bacillarophyta algae (7 species) and three types of Chlorophyta. The study showed an increase in species of Oscillatoria. The results showed no significant differences between sites in temperature, pH and relative humidity, while there were clear differences between sites for salinity and nutrient The study showed a difference of irrigation water quality and use of different fertilize

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Publication Date
Wed Feb 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network
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<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

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Publication Date
Wed Feb 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering
Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network
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<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

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Publication Date
Thu Mar 30 2023
Journal Name
Iraqi Journal Of Science
A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems
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      Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulate

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Publication Date
Sat Feb 01 2020
Journal Name
Journal Of Economics And Administrative Sciences
Effect of Job Burnout on Employee Performance: Field Research in the General Company for Vegetable Oil Industry
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Abstract

      This research aims to know the effect of job burnout in the worker’s performance. The researcher presented a theoretical basis for job burnout and the worker's performance. In order to achieve the objectives of the research, a hypothesis was drawn up that determines the nature of the relationship between the independent variable of job burnout and its dimensions (reduced personal accomplishment, depersonalization, Emotional Exhaustion) and variable dependent performance of workers dimensions (productivity, job satisfaction, organizational commitment, creativity), And to represent the volume of this community according to (de Morgan, D. Morgan) glo

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Publication Date
Mon Feb 01 2016
Journal Name
Swarm And Evolutionary Computation
Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks
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Publication Date
Sat Aug 01 2015
Journal Name
Journal Of Engineering
Applying Penman-Monteith Equation to Evaluate the Performance of Atmometer Apparatus in Greenhouse for Estimating Reference Evapotranspiration
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In this paper an atmometer apparatus were used in the greenhouses for estimating reference evapotranspiration values. Experimental work was conducted in the agriculture research center in the College of Agriculture-University of Baghdad west of the city of Baghdad. One atmometer was used in eggplant greenhouse and in cucumber greenhouse through the winter growing season 2013-2014. FAO Penman-Monteith equation was applied outside the greenhouse and used only 65% from the value of ETo in the greenhouses for estimating the reference evapotranspiration in the greenhouse. Moreover, Penman-Monteith equation was applied in greenhouses for the evaluating the performance of the atmometer. The results show that the erro

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