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Generative Adversarial Network for Imitation Learning from Single Demonstration
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Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing considered tasks despite the limitation in the number of expert demonstrations, which clearly indicate the potential of our model.

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Publication Date
Mon Jan 02 2017
Journal Name
Journal Of Educational And Psychological Researches
The effectiveness of the structural model of learning in the acquisition of geographical concepts among students of the first grade average)
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The current research aims to find out ( the effectiveness of the structural model of learning in the acquisition of geographical concepts at the first grade average students ) , and achieving the goals of research has been formulating the null hypothesis of the following :

    " There is no difference statistically significant when Mistoi (0.5 ) between the mean scores of the collection of students in the experimental group that is studying the general geographical principles " Bonmozj constructivist learning " and the mean scores of the control group , which is considering the same article ," the traditional way " to acquire concepts.

The researcher adopted th

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Publication Date
Sun Dec 03 2017
Journal Name
Al-khwarizmi Engineering Journal
A theoretical study for increasing production of electric power from Iraqi steam power plants.
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This article conclude a theoretical study for the possibility to produce additional electric power from Iraqi steam power plants by  cutting – off high-pressure feed water heaters .  Three separated steam power plants which Dura , south –Baghdad and Nasria were studied . The investigation showed the possibity of increasing the electric power from 10 to 15% for Dura and Nasria , whereas 6% for south – Baghdad . According to the nowadays of operation to Iraqi steam power plants , the results showed that by cutting–off high  pressure feed water heaters we can generate additional electric power about 250 MW during 3-4 hrs. daily. In addition, the fuel consumption can be reduced in comparison with diesel generat

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Publication Date
Thu Feb 01 2024
Journal Name
Baghdad Science Journal
Alpha Tracks Examination for Female Urine Samples who are Suffering from Urinary Tract Infections
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Urine samples had been gathered from females living in Baghdad city. The sample consisted of 30 females who suffered from U.T.I (Urinary tract infections) and 20 healthy females. The type of urine U.T.I was specified by the emergency lab in Al-Kindy hospital, and alpha tracks were determined by the nuclear track detector CR-39. The concentrations of alpha in 30 urine samples taken from females who had U.T.I ranged from 0.327ppm-1.583ppm, with an average of 0.94965 ppm. The maximum value 1.583 ppm is belonging to females with an aged 57 years old. The results of healthy female concentration ranged from 0.022 ppm-0.459ppm with an average of (0.30855ppm). The findings revealed that alpha emitter concentrations differed from woman to woman,

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Publication Date
Sun Dec 28 2025
Journal Name
Journal Of Physical Education
The Effect of Varied Teaching Strategies on Learning Backstroke Swimming for Students
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Publication Date
Thu Nov 17 2022
Journal Name
Journal Of Information And Optimization Sciences
Hybrid deep learning model for Arabic text classification based on mutual information
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Publication Date
Mon Oct 30 2023
Journal Name
Traitement Du Signal
A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning
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       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
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Publication Date
Thu Aug 31 2023
Journal Name
Journal Européen Des Systèmes Automatisés​
An IoT and Machine Learning-Based Predictive Maintenance System for Electrical Motors
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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

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Publication Date
Wed Jun 16 2021
Journal Name
Cognitive Computation
Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps
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Abstract <p>Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b</p> ... Show More
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