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All Journal International Journal of Electrical and Computer Engineering
Alsayaydeh, Jamil
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Computer Science & IT Electrical & Electronics Engineering

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An autopilot-based method for unmanned aerial vehicles trajectories control and adjustment Mochurad, Lesia; Alsayaydeh, Jamil; Yusof, Mohd Faizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4154-4166

Abstract

In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.
Artificial intelligence for automatic moderation of textual content in online chats and social networks Liaskovska, Solomiia; Bacarra, Rex; Martyn, Yevhen; Baidych, Volodymyr; Alsayaydeh, Jamil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3396-3409

Abstract

The article explores fundamental techniques for converting text into numerical data for machine learning algorithms. It meticulously examines various methods, including word vector representation via neural networks like Word2Vec, and explains the principles behind linear models such as logistic regression and support vector machines. Convolutional neural networks (CNN) and long short-term memory (LSTM) methods are also discussed, covering their components, mechanisms, and training processes. The research extends to developing and testing software for spam detection, hate speech identification, and recognizing offensive language. Using two datasets—one for labeled text messages and another for Twitter posts—the study analyzes data to address challenges like imbalanced data. A comparative analysis among linear models, deep neural networks, and single-layer models, using pre-trained bidirectional encoder representations from transformers (BERT) network, reveals promising results. The convolutional neural network stands out with a remarkable accuracy of 0.95. The study also adapts neural network architectures for hate speech and offensive language classification.
Co-Authors Bacarra, Rex Baidych, Volodymyr Liaskovska, Solomiia Martyn, Yevhen Mochurad, Lesia Yusof, Mohd Faizal