Osama Ali Awad

Scopus Publications

High performance for services robot with intelligent system based on IoT techniques
Yousif Al Mashhadany, Mohanad A. Al-Askari, Osama A. Awad, Abdullah Ahmed Jassim, Anas Mohsen Ali, Omar Ayad Naowaf
Aip Conference Proceedings, 2025
An Affective Computing Electroencephalogram-based System with Machine Learning Algorithms
Israa Laith Salim, Osama Ali Awad, Ali Sadeq Abdulhadi
Proceedings of the International Conference on Computer Engineering Network and Intelligent Multimedia Cenim 2024, 2024
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.
Real-time optimized wireless networked control system with cooperative network protocols
Yousif Safaa Alobaidy, Osama Ali Awad
Indonesian Journal of Electrical Engineering and Computer Science, 2023
In this paper, we present a real-time optimized fuzzy fuzzy proportional integral derivative (FPID)-controlled wireless networked system for a high-torque direct current (DC) motor. The main challenge faced by such systems is the delay in the wireless networked control system (WNCS). We employed a powerful FPID controller tuned using particle swarm optimization (PSO) technique to compensate for the delay. The system is tested on a network using the TrueTime simulator with different parameters. The results show that the system exhibits a very stable response, with the FPID controller compensating for the delay effectively. Increasing the number of nodes negatively impacts the system's performance, resulting in higher overshoot, longer settling time, and longer rise time. Moreover, the choice of bandwidth share and sampling time significantly affects the system's stability and real-time response. The use of transmission control protocol/internet protocol (TCP/IP) or user datagram protocol (UDP) protocols with Node MCU is necessary to transfer data from the Arduino Microcontroller to MATLAB, as MATLAB TrueTime simulator does not support direct serial communication. In conclusion, this study highlights valuable insights into the performance of the proposed system, demonstrating the need for further improvements in the system's design and control algorithms to achieve stable operation.
Improving Lung Cancer Relapse Prediction Using the Developed Optuna_XGB Classification Model
Rana Dhia'a Abdu-Aljabar, Osama A. Awad, P Choi, S Jeong, S Yoon, et al.
International Journal of Intelligent Engineering and Systems, 2023
Lung cancer is more likely to relapse in the first five years following surgery; even though the operation may have been a complete success, there remains a chance that the lung cancer could return. This return may lead the patient to die after a successful surgery. Because there are no symptoms of lung cancer in its early stage, many researchers use intelligent systems to predict the relapse of lung cancer in its early stages. The outcome of previous works considering this issue still suffers from low prediction accuracy. This study proposed a method to predict lung cancer relapse more accurately. This method has multiple stages: 1st optimization system, feature selection stage, 2nd optimization stage, and extreme gradient boost (XGBoost) classifications stage. It used two datasets (GSE8894 and GSE68465) of a gene expression microarray for NSCLC with its clinical information on relapse state. We obtained three probes (3 genes) with clinical data combinations that can get good prediction results. These genes included 225389_at (BTBD6), 220239_at (KLHL7), and 204832_s_at (BMPR1A). A comparison between the proposed model and the original XGBoost with PSO and Hyperopt as hyperparameter optimization for the XGBoost classification model is performed. Extensive comparisons with four machine learning algorithms, including Deep Forest, K-nearest neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes, are conducted. The proposed model accuracies are 0.93 for the GSE8894 dataset and 0.81 for the GSE68465 dataset.
Enhanced Real-Time Fuzzy PID Controlled WNCS Over TCP/UDP Protocol
Yousif Safaa Sadi, Osama Ali Awad
2023 3rd International Scientific Conference of Engineering Sciences Isces 2023 Proceedings, 2023
Real-Time wireless networked control systems (WNCS) are commonly used in various applications, including wheelchairs. However, these systems often suffer from a delay issue caused by the randomly generated delay accompanying the network, which can make the system response sluggish and unstable in some cases. This delay issue is a significant problem that needs to be addressed because it affects the system's performance and reliability. Therefore, this paper proposes a solution to the delay issue by utilizing a Fuzzy Proportional-Integral-Derivative (PID) controller to control a high-torque Real-Time Direct Current motor, commonly used in wheelchairs while also checking for wireless network connectivity. To transfer the data from Arduino Microcontroller to MATLAB, Transmission Control Protocol/Internet Protocol (TCP/IP) or User Datagram Protocol (UDP) with Node MCU will be used since MATLAB Truetime, which will be used to simulate Wireless Network, does not work with Direct Serial Communication. The simulation results show that the proposed system can efficiently control the Real-Time DC motor in real-time with the best settling time of 0.5371 for 0.01 sec sampling time and 0.5371-sec settling time with 0.001 sampling time. The proposed solution addresses a significant problem in WNCS and can contribute to developing more reliable and efficient Real-Time wireless networked control systems.
EEG-Based Emotion Recognition Using DWT and Artificial Neural Network: A Case Study on Autism Spectrum Disorder
Israa Laith Salim, Osama Ali Awad, Ali Sadeq Abdulhadi Jalal
2023 IEEE 20th International Conference on Smart Communities Improving Quality of Life Using AI Robotics and Iot Honet 2023, 2023
Autism Spectrum Disorder (ASD) impacts brain development, leading to social communication challenges and interaction. Researchers are increasingly exploring using Artificial Intelligence (AI) to diagnose ASD, interpret their emotions, and search for effective change interventions. This study investigates computer-aided ASD emotion recognition using electroencephalography (EEG) signals. The proposed method implements a four-level Discrete Wavelet Transform (DWT) for feature extraction and an Artificial Neural Network (ANN) to classify three dimensions of emotions: valence, arousal, and dominance. The model achieved 83% accuracy for valence and 96% for arousal and dominance. These findings hold potential for developing an adaptable closed-loop ASD intervention system. In conclusion, EEG-based emotion recognition using DWT and ANN appears promising for identifying emotional challenges in autism. However, further research is needed, considering limitations like sample size and static stimuli.
Parallel extreme gradient boosting classifier for lung cancer detection
Rana Dhia’a Abdualjabar, Osama A. Awad
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.
Fuzzy PID gain scheduling controller for networked control system
Osama Ali Awad, Isra'a Laith Salim
Iraqi Journal of Science, 2021
The use of a communication network in the closed loop control systems has many advantages such as remotely controlling equipment, low cost, easy to maintenance, efficient information transmission, etc. However, the Networked Control System (NCS) has many drawbacks, such as network-induce end-to-end time delay and packet loss, which lead to significant degradation in controller performance and may result in instability. Aiming at solving performance degradation in NCS, this paper propose to take the advantages and strength of the conventional Proportional-Integral-Derivative (PID), Fuzzy Logic (FL), and Gain Scheduling (GS) fundamentals to design a Fuzzy-PID like-Gain Scheduling (F-PID-GS) control technique, which has been proved to be effective in obtaining better performance. The True Time toolbox is used to establish the simulation model of the NCS. Ethernet as a communication network is simulated for different load conditions and random packet loss. The design approach is tested on a second order stepper motor. The results obtained show the effectiveness of the proposed approach in improving the overall system performance.
Evaluation of a Wireless ZigBee with PID Controller for Networked Control System Using True Time Toolbox
O. A. Awad, Mais Monqith
Isecure, 2020
Wireless Networked Control System (WNCS) consists of a control system and communication network system. The insertion of the communication network in control systems has enormous advantages but on the other hand, it brings several issues like network induced delays or packet dropouts that bring negative impact on the performance of the system and may lead to instability, the delay caused by wireless network transmission may have bad impacts on the system, so We need to know the behaviour of networked control systems to design better and optimum control that reduces the effect of the delay. In this paper the wireless networked control system is simulated using TrueTime. TrueTime is a Matlab/Simulink-based simulation toolbox which is used to design a wireless network model of ZigBee, using PID control for DC motor system. The evaluation tests show that the PID controller cannot stabilize the system when the data rare of ZigBee network is 20kbps.
Design of active fractional PID controller based on whale's optimization algorithm for stabilizing a quarter vehicle suspension system
Zeyad Abdulwahid Karam, Osama A. Awad
Periodica Polytechnica Electrical Engineering and Computer Science, 2020
Improving the dynamic performance of an automobile suspension system is considered as the main demand for comfortable and safe passenger travelling. From all previously proposed and implemented works, it is noticed that there are other factors that need to be considered to raising the car holding and stability in the road for improved passenger comfort when travelling. The minimization of car body displacement and oscillation time after exposure to road disturbances have been adopted in this work due to their contribution in raising the car holding and stability. The improvement in these features was maintained via a robust control methodology. The Fractional Order PID controller tuned by the Whales Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm is suggested in this work as a robust controller to reduce the effect of these demerits. In this paper, an active quarter car suspension nonlinear system is designed for the presented goals using a robust controller. Minimizing the displacement of the car body and reducing the damping frequency are achieved via a nonlinear control strategy using the fractional order PID controller, which can maintain the required characteristics. Tuning the parameters of the FOPID controller is performed by using the Whales Optimization Algorithm (WOA). Robustness of the FOPID controller is examined and proved to withstand a system parameter variation of ±12 % in all system parameters and a maximum of ±80 % in controller parameter variation. Simulation outcomes also indicate a considerably improved performance of the active suspension system with the fractional order PID controller over the traditional PID.
Gain Scheduling Fuzzy PID Controller for Distributed Control Systems
Osama A. Awad, Israa Laith
Communications in Computer and Information Science, 2020
Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm
Subhi Aswad Mohammed, Osama Ali Awad, Abdulkareem Merhej Radhi
Indonesian Journal of Electrical Engineering and Computer Science, 2020
A Smart real-time tracking system using GSM/GPRS technologies
Ali Mustafa, Mohammed I. A al-Nouman, Osama A. Awad
1st International Scientific Conference of Computer and Applied Sciences CAS 2019, 2019

Osama Ali Awad

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Scopus Publications