Preferred Language
Articles
/
jperc-1005
The Use Of Computerized Curriculum Individually and Cooperatively In the Achievement of Ninth Grade Students in Mathematics
...Show More Authors

The study aimed to investigate the effect of using the intructional computer individually or through the cooperative groups on the achievement of the ninth grade students in mathematics compared to the traditional method. The experimental method adapted three groups out of three schools were chosen, two groups of the students where applied the computer method. The comtrol group used the simple random method, and it used the diagnostic test as tool for the study.The result showed that there is a statistically significant difference between the mean scores of the experimental groups and the control group on the post-test for the two experimental groups.

View Publication Preview PDF
Quick Preview PDF
Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
...Show More Authors

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

... Show More
Publication Date
Fri Nov 21 2025
Journal Name
Journal Of Advances In Information Technology
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
...Show More Authors

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

... Show More
View Publication Preview PDF
Crossref