ALGABRI REDHWAN

@skku.edu

Sejong University

ALGABRI REDHWAN


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https://researchid.co/redhwan
Redhwan Algabri received the B. Eng., M. Eng., and Ph.D. degrees in Mechanical Engineering in 2011, 2015, and 2022, from Al-Baath University (Syria), Cairo University (Egypt), and Sungkyunkwan University (South Korea), respectively.
He was a postdoctoral researcher at the Research Institute of Engineering and Technology (RoCogMan Lab), Hanyang University, ERICA Campus, Ansan 15588, Republic of Korea.
He is currently working as an Assistant Professor in the Department of Computer Science and Engineering, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea.
His research interests in machine and deep learning, as well as intelligent robotics, include person tracking, object pose estimation, and identification for mobile robots.
27

Scopus Publications

453

Scholar Citations

10

Scholar h-index

10

Scholar i10-index

Scopus Publications

  • ContQuat: Continuous quaternion representation for head pose estimation
    Ahmed Abdu, Ji-Hun Bae, Sungon Lee, Redhwan Algabri
    Information Sciences, 2026
  • An optimized hybrid deep learning approach with uncertainty quantification for accurate transformer winding hotspot temperature forecasting
    Ali Abdo, Hongshun Liu, Yuqing Wang, Jiali Liu, Fuqiang Ren, Qingquan Li, Redhwan Algabri
    Engineering Applications of Artificial Intelligence, 2026
  • Multi-AAVs Collision-Free Flocking and Navigation in Dynamic Environments
    Junling Shi, Hanyu Li, Ammar Hawbani, Liang Zhao, Ammar Muthanna, Redhwan Algabri
    IEEE Transactions on Aerospace and Electronic Systems, 2026
    Multiple autonomous aerial vehicles (AAVs) system is consisting of multi-AAVs through collaborative techniques to efficiently accomplish various tasks in complex scenarios. Collision free flocking and navigation strategies for multi-AAVs in complex dynamic environments are still challenging in several aspects: real-time collision avoidance of dynamic obstacles, complexity of collaborative multi-AAV path planning, and group stagnation problem due to local optimization in dynamic environments. Traditional flocking algorithms usually rely on strict rules with limited adaptability to different environments. While Reinforcement Learning (RL), as a flexible and model-free framework, is able to solve some of the problems through autonomous exploration, it still suffers from challenges such as sample inefficiency, unstable training process, easy to fall into local optimums of the policy, and difficult to converge in sparse reward scenarios. In order to improve the training efficiency, we propose an improved algorithm called Curriculum-based Heuristic Guidance TD3 (CHTD3) for solving collision-free flocking and navigation of multiple AAVs in dynamic environments. The core idea is to decompose the task of AAV collision avoidance and navigation in dynamic environments into multiple sub-tasks and gradually increase the number of sub-tasks in a staged manner, while combining the heuristic rules with the Twin Delayed Deep Deterministic policy gradient (TD3) to solve the problem of prolonged learning cycle commonly found in the traditional RL methods. In addition, to better cope with complex and dynamic environments, we customize a risk assessment criterion and introduce a Human-AAV Collaboration (HAC) mechanism to enable the human operator to intervene at necessary moments to assist the AAV's motion decision. The experimental results demonstrate the effectiveness of the CHTD3-HAC approach in multi-AAVs collision-free flocking and navigation.
  • MITD-Net: Markov image-based threat detection network
    Malek Algabri, Firdaus Alhrazi, Cavazos Quero Luis, Ahmed Abdu, Yeong Hyeon Gu, Redhwan Algabri
    Scientific Reports, 2025
    The increasing sophistication of malicious activities within applications emphasizes the need for advanced predictive technologies. Malicious user behavior (MUB) is a concern in organizations, as it is a significant source of security breaches caused by employees within the organization. Although previous studies in user activity detection have demonstrated some success, these technologies have been insufficient in identifying new or unfamiliar security threats. To improve the detection of insider threats, this study introduces MITD-Net, a novel method based on a MobileNet convolutional neural network (CNN) architecture to predict the MUB effectively and efficiently. MITD-Net is faster and accurate than its counterparts, leveraging the computational efficiency and adaptability of deep neural networks in low-resource environments. Our model addresses the challenge of predicting harmful behavior. MITD-Net contributes to the proactive identification and mitigation of potential threats, thereby enhancing overall system security. The proposed method aims to extract features from the CERT r4.2 dataset, converting them into a Markov image to detect the MUB from authorized parties. Experimental evaluations conducted on CERT r4.2 datasets demonstrate the effectiveness of the proposed model. Moreover, this paper compares the results of previous studies. The experimental findings show that the proposed approach outperforms or achieves state-of-the-art techniques. Ablation studies were also performed to evaluate the significance of each individual component of the model.
  • Deep multi-metrics learning for mobile app defect prediction using code and process metrics
    Ahmed Abdu, Hakim A. Abdo, Inam Ullah, Jawad Khan, Yeong Hyeon Gu, Redhwan Algabri
    Scientific Reports, 2025
    The rise in mobile apps necessitates precise defect prediction to aid developers in resource allocation. The efficacy of defect prediction relies on data representation and model selection. However, existing research relies on isolated data sources, limiting models' ability to capture the complex interplay of code and process metrics in app development. This paper addresses this limitation by proposing a Deep Multi-Metrics Learning Model (DMLM), which leverages code metrics from the current code version and process metrics from previous releases. A deep convolutional neural network (CNN) is employed to capture intricate patterns within these metrics, enabling more accurate predictions. Experimental evaluations using nine real-world Android apps from Git Android repositories demonstrate that DMLM outperforms state-of-the-art approaches under non-effort-aware conditions regarding Area Under the Curve (AUC), F1 scores, and Matthews correlation coefficient (MCC). The experimental evaluation also demonstrates that the DMLM model outperforms existing baseline methods in PofB20 under effort-aware conditions. The findings highlight the efficacy of DMLM in enhancing mobile app defect prediction performance across both non-effort-aware and effort-aware contexts.
  • From prediction to sustainability: AI for smart energy management in wastewater treatment plants
    Saeed Hamood Alsamhi, Ammar Hawbani, Mohammed A. A. Al-qaness, Niall O’Brolchain, Liang Zhao, Ahmed Al-Dubai, Mamoona Asghar, Redhwan Algabri, Mohsen Guizani
    Scientific Reports, 2025
    In wastewater treatment plants (WWTPs), accurate energy forecasting is crucial for optimizing operations, promoting self- sufficiency, and ensuring sustainability. We compare and evaluate the performance of Machine Learning (ML) techniques for energy self-consumption (i.e., long-term memory (LSTM), support vector machines (SVM), recurring neural networks (RNN), gated recurrent units (GRU), and XGBoost), to forecast energy generation (EG) and energy consumption (EC) in WWTPs. The performance of models is evaluated using metrics (i.e., mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE)), based on a solid dataset of daily operating records. The findings show that GRU achieves the highest performance, with an RMSE of 0.102, MAE of 0.085, and R² of 0.978, followed by LSTM, GRU, and RNN. The models demonstrate temporal prediction capabilities, as well as driving energy efficiency and reducing operational costs in WWTPs. This paper offers insights into implementing ML for sustainable energy management in WWTPs to energy forecasting, enhancing energy self-consumption, and boosting operational efficiency and environmental sustainability.
  • WQuatNet: Wide range quaternion-based head pose estimation
    Redhwan Algabri, Hyunsoo Shin, Ahmed Abdu, Ji-Hun Bae, Sungon Lee
    Journal of King Saud University Computer and Information Sciences, 2025
    Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360 $$^{\circ }$$ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks; instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model.
  • Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning
    Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon
    Engineering Science and Technology an International Journal, 2025
    This paper introduces a neural network model designed for autonomous navigation in complex environments. It combines DRL methodologies to capture critical environmental features in the neural network. These features encompass data about the robot, humans, static obstacles, and path constraints. The representation, combined with weighted features from humans and environmental limitations, is processed through three multi-layer perceptrons (MLP) to calculate the value function and optimal policy, thereby enhancing navigation tasks. A novel reward function is proposed to accommodate path constraints and steer the robot’s navigation policies during neural network training. Additionally, common metrics like success rate, collision avoidance, time to reach the goal, and new comprehensive log information are included to provide an overview of the robot’s performance. The model’s efficacy is demonstrated through navigation in simulation scenarios involving curved and cross pathways, with the agents’ random position and velocity occasionally exceeding the maximum robot speed, as well as real experiments in limited spaces. The paper provides a GitHub repository that includes comparative performance videos with state-of-the-art models in path-constrained scenarios, along with strategies for reward functions. Link: https://github.com/nabihandres/Wallproximity_DRL .
  • MCK-DDPG: A Deep Reinforcement Learning Framework for Jointly Optimizing On-Board, Platoon, and Vehicular Edge Computing
    Hao Yang, Ammar Hawbani, Redhwan Algabri
    Proceedings 2025 IEEE International Symposium on Parallel and Distributed Processing with Applications Ispa 2025, 2025
    Enhancing road safety and traffic efficiency in Intelligent Transportation Systems (ITS) demands a move beyond isolated computational approaches. Strategically integrating the immediate responsiveness of local vehicle processing, the cooperative power of platoon computing, and the broader capabilities of Vehicular Edge Computing (VEC) is paramount. This paper proposes a deep reinforcement learning-based framework that jointly optimizes local computing, platoon computing, and VEC. An enhanced multi-center Kmeans (MCK) algorithm is designed to dynamically form platoons based on spatial and communication features. Meanwhile, a Deep Deterministic Policy Gradient (DDPG) algorithm is applied to learn optimal task offloading strategies in real time, considering link quality, vehicular mobility, and edge resource availability. The proposed MCK-DDPG framework enables collaborative decision-making among vehicles and improves both task execution efficiency and platoon stability. Extensive simulation results demonstrate that our method outperforms several baselines in terms of task latency, computation cost, and offloading rate. These results confirm the effectiveness and adaptability of the proposed approach in dynamic vehicular edge computing scenarios.
  • ConSFL: A Lightweight Contrastive Learning-Driven Split Federated Learning for Heterogeneous LEO Constellations
    Hengzhong Du, Liang Zhao, Ammar Hawbani, Redhwan Algabri, Zhi Liu, Qiang He
    Proceedings of the International Conference on Parallel and Distributed Systems ICPADS, 2025
    The advancement of Low Earth Orbit (LEO) satellite technology has enabled rapid progress in on-orbit machine learning. However, limited on-board computational resources hinder large-scale model training on individual satellites. Furthermore, the highly dynamic network topology and resource heterogeneity of LEO satellite constellations make collaborative training prone to single-point failures and privacy risks. To address these issues, this paper proposes ConSFL, a lightweight Contrastive learning-driven Split Federated Learning framework. ConSFL enables local feature extraction from unlabeled remote sensing data under resource-constrained conditions, while preserving both model completeness and data privacy. By performing federated learning across heterogeneous submodels, the training of the global model can focus on learning features within specialized semantic dimensions, thereby enhancing overall performance. Additionally, we introduce a spatial attention pooling (SAP) method into ConSFL to aggregate intermediate features with larger feature map sizes from submodel outputs. Simulation results show that ConSFL achieves higher Top-1 accuracy across submodels compared to the best baseline, while SAP enhances ConSFL's ability to capture feature-space information and improves submodel performance under earlyexit mechanisms.
  • Cross-project software defect prediction based on the reduction and hybridization of software metrics
    Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Sungon Lee, Mohammed A. Al-masni, Yeong Hyeon Gu, Redhwan Algabri
    Alexandria Engineering Journal, 2025
  • YAREN: Humanoid Torso Robot Platform for Research, Social Interaction, and Educational Applications
    Daniela Sánchez-Orozco, Rhandall Valdez, Yandri Uchuari, Marcelo Fajardo-Pruna, Luis Cavazos Quero, Redhwan Algabri, Emiliano Quinones Yumbla, Francisco Yumbla
    IEEE Access, 2025
  • An Open-Source 3D Printed Three-Fingered Robotic Gripper for Adaptable and Effective Grasping
    Francisco Yumbla, Emiliano Quinones Yumbla, Erick Mendoza, Cristobal Lara, Javier Pagalo, Efraín Terán, Redhwan Algabri, Myeongyun Doh, Tuan Luong, Hyungpil Moon
    Biomimetics, 2025
  • Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model
    Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri, Mohammed A. Al-masni, Mannan Saeed Muhammad, Yeong Hyeon Gu
    Scientific Reports, 2024
  • Deep learning and machine learning techniques for head pose estimation: a survey
    Redhwan Algabri, Ahmed Abdu, Sungon Lee
    Artificial Intelligence Review, 2024
  • Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic Graph
    Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri
    IEEE Transactions on Reliability, 2024
  • Real-time 6DoF full-range markerless head pose estimation[Formula presented]
    Redhwan Algabri, Hyunsoo Shin, Sungon Lee
    Expert Systems with Applications, 2024
  • Head Pose Estimation Based on 5D Rotation Representation
    Redhwan Algabri, Sungon Lee
    IEEE Symposium on Wireless Technology and Applications Iswta, 2024
  • HHLP-SSA: Enhanced Fault Diagnosis in Industrial Robots Using Hierarchical Hyper-Laplacian Prior and Singular Spectrum Analysis
    Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, Mugahed A. Al-Antari
    8th International Artificial Intelligence and Data Processing Symposium Idap 2024, 2024
  • Development of an artificial intelligence-PLC temperature controller for a cement factory for decreasing contamination
    Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, Mugahed A. Al-Antari
    8th International Artificial Intelligence and Data Processing Symposium Idap 2024, 2024
  • SSA-Sparse MHD: Singular Spectrum Analysis Paired with Sparse Maximum Harmonics Deconvolution for Detecting Feeble Defect Signals in Industrial Robots
    Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, Mugahed A. Al-Antari
    8th International Artificial Intelligence and Data Processing Symposium Idap 2024, 2024
  • Graph-Based Feature Learning for Cross-Project Software Defect Prediction
    Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri, Sungon Lee
    Computers Materials and Continua, 2023
  • Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots
    Redhwan Algabri, Mun-Taek Choi
    Sensors, 2022
  • Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey
    Ahmed Abdu, Zhengjun Zhai, Redhwan Algabri, Hakim A. Abdo, Kotiba Hamad, Mugahed A. Al-antari
    Mathematics, 2022
  • Target recovery for robust deep learning-based person following in mobile robots: Online trajectory prediction
    Redhwan Algabri, Mun-Taek Choi
    Applied Sciences Switzerland, 2021
  • Robust Person Following under Severe Indoor Illumination Changes for Mobile Robots: Online Color-Based Identification Update
    Redhwan Algabri, Mun-Taek Choi
    International Conference on Control Automation and Systems, 2021
  • Deep-learning-based indoor human following of mobile robot using color feature
    Redhwan Algabri, Mun-Taek Choi
    Sensors Switzerland, 2020

RECENT SCHOLAR PUBLICATIONS

  • An optimized hybrid deep learning approach with uncertainty quantification for accurate transformer winding hotspot temperature forecasting
    A Abdo, H Liu, Y Wang, J Liu, F Ren, Q Li, R Algabri
    Engineering Applications of Artificial Intelligence 176, 114621 , 2026
    2026
  • ContQuat: Continuous quaternion representation for head pose estimation
    A Abdu, JH Bae, S Lee, R Algabri
    Information Sciences, 123621 , 2026
    2026
  • 쿼터니언 표현을 기반으로 한 머리 자세 추정 네트워크
    배지훈
    제 16 차 대한의료로봇학회 학술대회 , 2025
    2025
  • Deep multi-metrics learning for mobile app defect prediction using code and process metrics
    A Abdu, HA Abdo, I Ullah, J Khan, YH Gu, R Algabri
    Scientific Reports 15 (1), 38620 , 2025
    2025
    Citations: 3
  • MITD-Net: Markov image-based threat detection network
    M Algabri, F Alhrazi, CQ Luis, A Abdu, YH Gu, R Algabri
    Scientific Reports 15 (1), 35389 , 2025
    2025
  • YAREN: humanoid torso robot platform for research, social interaction, and educational applications
    D Sanchez-Orozco, R Valdez, Y Uchuari, M Fajardo-Pruna, LC Quero, ...
    IEEE Access , 2025
    2025
    Citations: 3
  • Wquatnet: Wide range quaternion-based head pose estimation
    R Algabri, H Shin, A Abdu, JH Bae, S Lee
    Journal of King Saud University Computer and Information Sciences 37 (3), 24 , 2025
    2025
    Citations: 7
  • Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning
    N Pico, E Montero, A Amirbek, E Auh, J Jeon, MS Alvarez-Alvarado, ...
    Engineering Science and Technology, an International Journal 64, 101993 , 2025
    2025
    Citations: 6
  • An open-source 3D printed three-fingered robotic gripper for adaptable and effective grasping
    F Yumbla, E Quinones Yumbla, E Mendoza, C Lara, J Pagalo, E Terán, ...
    Biomimetics 10 (1), 26 , 2025
    2025
    Citations: 6
  • Cross-project software defect prediction based on the reduction and hybridization of software metrics
    A Abdu, Z Zhai, HA Abdo, S Lee, MA Al-masni, YH Gu, R Algabri
    Alexandria Engineering Journal 112, 161-176 , 2025
    2025
    Citations: 19
  • 2024 Index IEEE Transactions on Reliability Vol. 73
    HA Abdo, A Abdu, D Agiakatsikas, Y Ait-Ameur, R Al Kontar, R Algabri, ...
    IEEE Transactions on Reliability 73 (4) , 2024
    2024
  • Deep learning and machine learning techniques for head pose estimation: a survey.
    R Algabri, A Abdu, S Lee
    Artificial Intelligence Review 57 (10) , 2024
    2024
    Citations: 19
  • HHLP-SSA: enhanced fault diagnosis in industrial robots using hierarchical hyper-Laplacian prior and singular spectrum analysis
    RNA Algburi, HSS Aljibori, Z Al-Huda, KM Sowoud, R Algabri, ...
    2024 8th international artificial intelligence and data processing symposium … , 2024
    2024
    Citations: 4
  • Development of an artificial intelligence-PLC temperature controller for a cement factory for decreasing contamination
    RNA Algburi, HSS Aljibori, Z Al-Huda, KM Sowoud, R Algabri, ...
    2024 8th International Artificial Intelligence and Data Processing Symposium … , 2024
    2024
  • SSA-sparse MHD: singular spectrum analysis paired with sparse maximum harmonics deconvolution for detecting feeble defect signals in industrial robots
    RNA Algburi, HSS Aljibori, Z Al-Huda, KM Sowoud, R Algabri, ...
    2024 8th International artificial intelligence and data processing symposium … , 2024
    2024
    Citations: 4
  • Head pose estimation based on 5d rotation representation
    R Algabri, S Lee
    2024 IEEE Symposium on Wireless Technology & Applications (ISWTA), 195-199 , 2024
    2024
    Citations: 3
  • Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model
    A Abdu, Z Zhai, HA Abdo, R Algabri, MA Al-Masni, MS Muhammad, ...
    Scientific Reports 14 (1), 14771 , 2024
    2024
    Citations: 54
  • Real-time 6dof full-range markerless head pose estimation
    R Algabri, H Shin, S Lee
    Expert Systems with Applications 239, 122293 , 2024
    2024
    Citations: 26
  • Software defect prediction based on deep representation learning of source code from contextual syntax and semantic graph
    A Abdu, Z Zhai, HA Abdo, R Algabri
    IEEE Transactions on Reliability 73 (2), 820-834 , 2024
    2024
    Citations: 55
  • Graph-based feature learning for cross-project software defect prediction
    A Abdu, Z Zhai, HA Abdo, R Algabri, S Lee
    Computers, Materials and Continua 77 (1) , 2023
    2023
    Citations: 14

MOST CITED SCHOLAR PUBLICATIONS

  • Deep-learning-based indoor human following of mobile robot using color feature
    R Algabri, MT Choi
    Sensors 20 (9), 2699 , 2020
    2020
    Citations: 116
  • Software defect prediction based on deep representation learning of source code from contextual syntax and semantic graph
    A Abdu, Z Zhai, HA Abdo, R Algabri
    IEEE Transactions on Reliability 73 (2), 820-834 , 2024
    2024
    Citations: 55
  • Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model
    A Abdu, Z Zhai, HA Abdo, R Algabri, MA Al-Masni, MS Muhammad, ...
    Scientific Reports 14 (1), 14771 , 2024
    2024
    Citations: 54
  • Deep learning-based software defect prediction via semantic key features of source code—systematic survey
    A Abdu, Z Zhai, R Algabri, HA Abdo, K Hamad, MA Al-antari
    Mathematics 10 (17), 3120 , 2022
    2022
    Citations: 47
  • Target recovery for robust deep learning-based person following in mobile robots: Online trajectory prediction
    R Algabri, MT Choi
    Applied Sciences 11 (9), 4165 , 2021
    2021
    Citations: 42
  • Real-time 6dof full-range markerless head pose estimation
    R Algabri, H Shin, S Lee
    Expert Systems with Applications 239, 122293 , 2024
    2024
    Citations: 26
  • Cross-project software defect prediction based on the reduction and hybridization of software metrics
    A Abdu, Z Zhai, HA Abdo, S Lee, MA Al-masni, YH Gu, R Algabri
    Alexandria Engineering Journal 112, 161-176 , 2025
    2025
    Citations: 19
  • Deep learning and machine learning techniques for head pose estimation: a survey.
    R Algabri, A Abdu, S Lee
    Artificial Intelligence Review 57 (10) , 2024
    2024
    Citations: 19
  • Graph-based feature learning for cross-project software defect prediction
    A Abdu, Z Zhai, HA Abdo, R Algabri, S Lee
    Computers, Materials and Continua 77 (1) , 2023
    2023
    Citations: 14
  • Robust person following under severe indoor illumination changes for mobile robots: online color-based identification update
    R Algabri, MT Choi
    2021 21st International Conference on Control, Automation and Systems (ICCAS … , 2021
    2021
    Citations: 13
  • Wquatnet: Wide range quaternion-based head pose estimation
    R Algabri, H Shin, A Abdu, JH Bae, S Lee
    Journal of King Saud University Computer and Information Sciences 37 (3), 24 , 2025
    2025
    Citations: 7
  • Online boosting-based target identification among similar appearance for person-following robots
    R Algabri, MT Choi
    Sensors 22 (21), 8422 , 2022
    2022
    Citations: 7
  • Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning
    N Pico, E Montero, A Amirbek, E Auh, J Jeon, MS Alvarez-Alvarado, ...
    Engineering Science and Technology, an International Journal 64, 101993 , 2025
    2025
    Citations: 6
  • An open-source 3D printed three-fingered robotic gripper for adaptable and effective grasping
    F Yumbla, E Quinones Yumbla, E Mendoza, C Lara, J Pagalo, E Terán, ...
    Biomimetics 10 (1), 26 , 2025
    2025
    Citations: 6
  • Deep learning-based software defect prediction via semantic key features of source code-systematic survey. Mathematics 10 (17): 3120
    A Abdu, Z Zhai, R Algabri, HA Abdo, K Hamad, MA Al-antari
    2022
    Citations: 5
  • HHLP-SSA: enhanced fault diagnosis in industrial robots using hierarchical hyper-Laplacian prior and singular spectrum analysis
    RNA Algburi, HSS Aljibori, Z Al-Huda, KM Sowoud, R Algabri, ...
    2024 8th international artificial intelligence and data processing symposium … , 2024
    2024
    Citations: 4
  • SSA-sparse MHD: singular spectrum analysis paired with sparse maximum harmonics deconvolution for detecting feeble defect signals in industrial robots
    RNA Algburi, HSS Aljibori, Z Al-Huda, KM Sowoud, R Algabri, ...
    2024 8th International artificial intelligence and data processing symposium … , 2024
    2024
    Citations: 4
  • Deep multi-metrics learning for mobile app defect prediction using code and process metrics
    A Abdu, HA Abdo, I Ullah, J Khan, YH Gu, R Algabri
    Scientific Reports 15 (1), 38620 , 2025
    2025
    Citations: 3
  • YAREN: humanoid torso robot platform for research, social interaction, and educational applications
    D Sanchez-Orozco, R Valdez, Y Uchuari, M Fajardo-Pruna, LC Quero, ...
    IEEE Access , 2025
    2025
    Citations: 3
  • Head pose estimation based on 5d rotation representation
    R Algabri, S Lee
    2024 IEEE Symposium on Wireless Technology & Applications (ISWTA), 195-199 , 2024
    2024
    Citations: 3