Abdelbaset R E Almasri

Abdelbaset R E Almasri

Al Azhar University-Gaza

H-index: 10

Asia-Palestinian Territory


Abdelbaset R E Almasri, With an exceptional h-index of 10 and a recent h-index of 10 (since 2020), a distinguished researcher at Al Azhar University-Gaza, specializes in the field of Data mining, Decision Support System.

Professor Information


Al Azhar University-Gaza


Faculty of Engineering and Information Technology



Citations(since 2020)


Cited By




hIndex(since 2020)




i10Index(since 2020)



University Profile Page

Al Azhar University-Gaza

Research & Interests List

Data mining

Decision Support System

Top articles of Abdelbaset R E Almasri

Prediction of Instructor Performance using Machine and Deep Learning Techniques

The quality of instructors’ performance mainly influences the quality of educational services in higher educational institutions. One of the major challenges of higher educational institutions is the accumulated amount of data and how it can be utilized to boost the academic programs quality. The recent advancements in Artificial Intelligence techniques, including machine and deep learning models, have led to the expansion in practical prediction for various fields. In this paper, a dataset was collected from UCI Repository, University of California, for the prediction of instructor performance. In order to find how effective the instructor in the higher education systems is, a group of machine and deep learning algorithms were applied to predict instructor performance in higher education systems. The best machine-learning algorithm was Extra Trees Regressor with Accuracy (98.78%), Precision (98.78%), Recall (98.78%), F1-score (98.78%); however, the proposed deep learning algorithm achieved Accuracy (98.89%), Precision (98.91%), Recall (98.94%), and F1-score (98.92%).


Basem S Abunasser,Mohammed Rasheed J AL-Hiealy,Alaa M Barhoom,Abdelbaset R Almasri,Samy S Abu-Naser

Published Date


Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms

Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task ie it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can …


Ali MA Barhoom,Abdelbaset Almasri,Bassem S Abu-Nasser,Samy S Abu-Naser

Published Date


Instructor Performance Modeling For Predicting Student Satisfaction Using Machine Learning-Preliminary Results

The use of machine learning techniques in higher education can be beneficial in optimizing teaching and providing higher institutions with the solutions they need, like monitoring student satisfaction with the instructor's performance. In this study, ten machine learning classification methods are employed on a dataset to predict selected aspects of student satisfaction: Logistic Regression, Linear Discriminant Analysis, Kneighbors, Decision Tree, Naïve Bayesian, Support Vector Machine, Extra Trees, Gradient Boosting, Random Forest, and Multilayer Perceptron. The dataset consists of 5,820 instances obtained from the UCI machine learning repository, and it demonstrates how students rated their instructors in terms of course structure, and behavior. As a result, it was observed that the ten classifiers had better performance in terms of prediction accuracy after balancing the dataset. On the balanced dataset, the ten classifiers were 4% more accurate on average than when they were trained on the imbalanced dataset. In addition, the Extra Trees classifier achieved the highest performance rate based on all the evaluation metrics used in predicting all the targeted features, especially with the balanced dataset. This paper also included the finding of the most important attributes/variables affecting the predictability of the student-satisfaction aspects. As this finding demonstrated, the majority of the important variables were related to instructor characteristics. Moreover, in all cases of the predictions, one variable related to course characteristics (practice-based activities: laboratory work, fieldwork, and group discussions) frequently appeared as the most …


ABDELBASET R Almasri,NA Yahaya,SAMY S Abu-Naser


Journal of Theoretical and Applied Information Technology

Published Date


Mining Educational Data to Improve Teachers’ Performance

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract the knowledge necessary to optimize the effectiveness of the teaching process. With normal educational system work, it’s often unlikely to accomplish fine system optimisation due to the large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness of Data Mining is explained in the migration of the educational data into knowledge. The’s experiment goal at first was to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance on Data Mining solutions in a real application.


Abdelbaset Almasri,Tareq Obaid,Mohanad SS Abumandil,Bilal Eneizan,Ahmed Y Mahmoud,Samy S Abu-Naser

Published Date


Professor FAQs

What is Abdelbaset R E Almasri's h-index at Al Azhar University-Gaza?

The h-index of Abdelbaset R E Almasri has been 10 since 2020 and 10 in total.

What are Abdelbaset R E Almasri's research interests?

The research interests of Abdelbaset R E Almasri are: Data mining, Decision Support System

What is Abdelbaset R E Almasri's total number of citations?

Abdelbaset R E Almasri has 745 citations in total.

What are the co-authors of Abdelbaset R E Almasri?

The co-authors of Abdelbaset R E Almasri are Samy S. Abu-Naser.


H-index: 125
Samy S. Abu-Naser

Samy S. Abu-Naser

Al Azhar University-Gaza


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