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.
This paper aims to identify the approaches used in assessment the credit applications by Iraqi banks, as well as which approach is most used. It also attempted to link these approaches with reduction of credit default and banks’ efficiency particularly for the Gulf Commercial Bank. The paper found that the Gulf Bank widely relies on the method of Judgment Approach for assessment the credit applications in order to select the best of them with low risk of default. In addition, the paper found that the method of Judgment Approach was very important for the Gulf Bank and it driven in reduction the ratio of credit default as percentage of total credit. However, it is important to say that the adoption of statistical approaches for
... Show MoreBackground: Educational environment is one of the most important determinants of an effective curriculum. Students' perceptions of their educational environment have a significant impact on their behavior and academic progress. Objective: 1. To identify students’ perception to the educational environment.2. To identify any gender or class level differences in the students’ perception.Type of the study: This is a descriptive cross-sectional studyMethodology: The study was carried out on convenient sample of 150 students of 2nd and 5th grade. This study was done in Al Kindy Medical College, Baghdad, Iraq and conducted during the period from the 1st of October 2013 till the end of March 2014, by using DREEM questionnaire a validated uni
... Show MoreThe consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying
... Show MoreThe field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura
... Show MoreIn this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi
... Show MoreThe objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.
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Objective(s): To evaluate blended learning in nursing education at the Middle Region in Iraq.
Methodology: A descriptive study, using evaluation approach, is conducted to evaluate blended learning in nursing education in Middle Region in Iraq from September 26th, 2021 to March 22nd, 2022. The study is carried out at two Colleges of Nursing at the University of Baghdad and University of Tikrit in Iraq. A convenient, non-probability, sample of (60) undergraduate nursing students is selected. The sample is comprised of (30) student from each college of nursing, Self-report questionnaire is constructed from the literature, for e
... Show MoreThis paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue
... Show MoreThis research attempts to dust briefly the concept of deliberativeness in Arabic rhetoric, which was only understood in light of modern Western linguistics, starting with its definition (conformity of speech to the necessity of the case with its eloquence) which contains the elements of the entire creative process as shown by this brief study, in the first chapter that He was labeled (deliberative in Western and Arab thought), and we had started it with deliberation in western thought in the first topic in an attempt to highlight the western concepts of this term so that we could discern its originality in the Arab rhetorical heritage that came next in the second topic (deliberative in Arab thought), explaining the appropriate Th
... Show MoreTo expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo
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