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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Abdullah Alsaeedi
Taibah University
Computer Science & IT Electrical & Electronics Engineering

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Enhancing multi-class web video categorization model using machine and deep learning approaches Wael M. S. Yafooz; Abdullah Alsaeedi; Reyadh Alluhaibi; Abdel-Hamid Mohamed Emara
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3176-3191

Abstract

With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.
A systematic review of non-functional requirements mapping into architectural styles Muhammad Nouman; Muhammad Azam; Ashraf Mousa Saleh; Abdullah Alsaeedi; Hayfa Yousef Abuaddous
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4081

Abstract

Fortunately, the software attracted enough businesses to the market, allowing them to earn money in less time with less work and more accurate results. Software development life cycle (SDLC) is used for software development as it is responsible for system functionality, efficiency, maintainability, and any other non-functional system requirements. Each stage of the SDLC process is critical. However, software requirements and software architecture are both fundamental activities that play a vital role in all other SDLC stages. Non-functional requirements are critical to the success of any software because they explain all system quality attributes such as complexity, reliability, security, and maintainability, among others. The architectural styles assist you in determining which architecture may be best for your project requirements. This paper discusses several of the most important architectural styles that are best suited for mapping desired non-functional requirements for software development, as well as their comparison based on various quality attributes (non-functional requirements).
Co-Authors Abdel-Hamid Mohamed Emara Ashraf Mousa Saleh Hayfa Yousef Abuaddous Muhammad Azam Muhammad Nouman Reyadh Alluhaibi Wael M. S. Yafooz