Scopus Research — Osama Ali Awad Al-Khateeb

Recent years have witnessed a widespread of scientific applications related to robot science, especially in the field of interaction between real humans and robots. The service robot application is considered one of the best daily life applications because of the great service it provides to make living easier and more accurate in dealing with many of the daily actions that a person performs. In this work, a robot is designed and implemented that operates on the principle of movement by using voice commands to determine its path in movement. Track programming technology was also used to control the movement of this robot. The design used to implement these commands is a robot with four wheels that are moved using DC motors. The work consists of three main stages: The first stage has seen several sources for such applications and what are the latest and most important of these sources to determine the features of the initial form of the proposed design. In the second stage, the mathematical model for assembling the robot was built, as well as the mathematical model for controlling the movement of the robot. The commands were programmed, loaded on the microcontroller, and examined through the Proteus program. The third stage is when the scientific model was built and orders were applied to this model. Multiple results were obtained, the most important of which was controlling the movement of the design through voice commands, then programming the robot's path as required, and the possibility of bypassing the obstacles that are in the path. The obtained results are characterized by high accuracy, which confirms the possibility of adopting the proposed design as an applied practical model. © 2025 Author(s).

Affective Computing plays a crucial role in the Human-Computer Interaction (HCI) field by enabling computers to recognize, interpret, process, and simulate emotions evoked by positive/negative events, objects, or situations. It presents new avenues for applications ranging from personalized user interfaces to mental health monitoring. In this study, an affective computing system for human emotion recognition is developed by measuring the electrical activity of brain, Electroencephalogram (EEG) signals for 15 participants were recorded. Using Machine Learning (ML) algorithms to classify emotional states into three emotion dimensions: valence, arousal, and dominance. Two ML algorithms, Naïve Bayes (NB) and Artificial Neural Network (ANN) are exploited. The system also validated with EEG data from the public DREAMER dataset and compared with recorded data. Results demonstrate the feasibility and effectiveness of the proposed EEG-based affective computing system in accurately identifying and categorizing emotions, leveraging the designed ANN classifier; a maximum accuracy of 95.61% is achieved. © 2024 IEEE.