The vast amount of unstructured data spread on a daily basis rises the need for developing effective information retrieval and extraction methods. Named Entity Recognition is a challenging ...classification task for structuring data into pre-defined labels, and is even more complicated when being applied on the Arabic language due to its special traits and complex nature. This article presents a novel Deep Learning approach for Standard Arabic Named Entity Recognition that proved its out-performance when being compared to previous works. The main aim of building a new model is to provide better fine-grained results for use in the Natural Language Processing fields. In our proposed methodology we utilized transfer learning with deep neural networks to build a Pooled-GRU model combined with the Multilingual Universal Sentence Encoder. Our proposed model scored about 17% enhancement when being compared to previous work.
The increasing interactive content in the Internet motivated researchers and data scientists to conduct Aspect-Based Sentiment Analysis (ABSA) research to understand the various sentiments and the ...different aspects of a product in a single user’s comment. Determining the various aspects along with their polarities (positive, negative, or neutral) from a single comment is a challenging problem. To this end, we have designed and developed a deep learning model based on Gated Recurrent Units (GRU) and features extracted using the Multilingual Universal Sentence Encoder (MUSE). The proposed Pooled-GRU model trained on a Hotels’ Arabic reviews to address two ABSA tasks: (1) aspect extraction, and (2) aspect polarity classification. The proposed model achieved high results with 93.0% F1 score in the former task and 90.86% F1 score in the latter task. Our experimental results show that our proposed model outperforms the baseline model and the related research methods evaluated on the same dataset. More precisely, our proposed model showed 62.1% improvement in the F1 score over the baseline model for the aspect extraction task and 15% improvement in the accuracy over the baseline model for the aspect polarity classification task.
Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness ...worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high ...infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.
Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer ...among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.
This study aimed to determine the level of students’ test wiseness during remote learning. The study sample included (391) students from Amman Arab University. A scale consisting of (28) items was ...deployed to the participants after assuring its validity and reliability. The results showed a high level of students’ test wiseness. Moreover, there were no statistically significant differences at (α=0.05) between the arithmetic means of the total degree of test wiseness attributed to the study variables, while there were statistically significant differences at (α=0.05) between the means of the dimension “Using of Time” on favor of male students. In addition, the findings elucidated that there were statistically significant differences at (α=0.05) between the arithmetic means of the dimension “Avoidance of Error” attributed to faculty in favor of the humanities faculties. Finally, the results showed statistically significant differences at (α=0.05) between the arithmetic means of the dimension “Deductive Thinking” attributed to the academic program in favor of master and bachelor students.
This study aimed at comparing the effect of two test item formats (multiple-choice and complete) on estimating person's ability, item parameters and the test information function (TIF).To achieve the ...aim of the study, two format of mathematics(1) test have been created: multiple-choice and complete, In its final format consisted of (31) items. The test has been applied on (300) students in Tabuk University. The responses of the examinees were analyzed by BILOG-MG 3 programs for each item form according to the two parameter logistic model. The study findings revealed the following: there were statistically significant differences at the level of significance (alpha=0.05) among the standard error means for estimating item parameters (difficulty and discrimination) due to the format of test item in favor of the complete test. And the results showed there were statistically significant differences at the level of significance (alpha=0.05) among the standard error means for estimating person's ability due to the format of test item in favor of the complete test. On the other hand, the results showed there were statistically significant differences (alpha=0.05) among the standard error means of test information function (TIF) due to the format of test item in favor of the complete test. The study recommends educating teachers to diversify the forms of items of the tests they use so that they contain the two forms (multiple-choice and complete), and using complete items on a larger scale. than it is now being more discrimination.
This research aims to identify science and mathematics teachers’ attitudes towards integrating Information and Communication Technology (ICT) in their educational practice through applying the ...Unified Theory of Acceptance and Use of Technology (UTAUT). A questionnaire instrument was developed based on the constructs of the UTAUT (performance expectancy, effort expectancy, social influence and facilitating conditions) and attitudes scale. The study sample consisted of a group of mathematics and science teachers in governorate of Ma’an. The participants were randomly selected. Descriptive and regression analysis were used to analyze the data. The results showed the attitudes of science and mathematics teachers towards integrating information and communication technology in the educational process were high and positive. In addition, the results showed that science and mathematics teachers had positive and high perceptions of integrating information and communication technology in the educational process in all dimensions (performance expectancy, effort expectancy, social influence, and facilitating conditions). Furthermore, the unified theory of acceptance and use of technology was valid in explaining the attitudes of Science and Mathematics teachers toward the integration of ICT in the in their educational practice.
This study aims at identifying the effect of multiple-choice test items' difficulty degree on the reliability coefficient and the standard error of measurement depending on the item response theory ...IRT. To achieve the objectives of the study, (WinGen3) software was used to generate the IRT parameters (difficulty, discrimination, guessing) for four forms of the test. Each form consisted of (30) items with different difficulty coefficients averages (-0.24, 0.24, 0.42, 0.93). The resulting items parameters were utilized to generate the ability and responses of (3000) examinees based on the three-parameter model. These data were converted into a readable file using the (SPSS) and the (BILOG-MG3) software. Then the reliability coefficients for the four test forms, the items parameters, and the items information function were calculated, and dependence on the information function values to calculate the standard error of measurement for each item.The results of the study showed that there are statistically significant differences at the level of significance (α ≤ 0.05) between the averages of the values of the standard error of measurement attributed to the difference in the difficulty degree of the items in favor of the test with the higher difficulty coefficient. The results also found that there are apparent differences between the test reliability parameters attributed to the difficulty degree of the test according to the three-parameter model in favor of the form with the average difficulty degree.
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