Lijun Zong | Robotics and AI | Best Researcher Award

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Assoc. Prof. Dr Lijun Zong | Robotics and AI | Best Researcher Award

Associate Professor, Northwestern Polytechnical University, China

Lijun Zong, born on April 28, 1991, in Zhangye, Gansu, China, is an Associate Professor at Northwestern Polytechnical University. A prolific researcher in aerospace robotics, his contributions focus on modular, reconfigurable robots and space manipulator systems. He earned his B.Sc. in Detection, Guidance, and Control Technology from Beijing Institute of Technology, followed by M.Sc. and Ph.D. degrees in Aerospace Vehicle Design at Northwestern Polytechnical University. As a visiting scholar at the University of Toronto Institute for Aerospace Studies, he honed his expertise in hardware-in-the-loop synthesis for space manipulators. Dr. Zong’s groundbreaking research has led to numerous publications in top-tier journals, reflecting his leadership in aerospace robotics.

PROFISSIONAL PROFILE

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Scopus

STRENGTHS FOR THE AWARD

Dr. Lijun Zong’s distinguished research contributions to aerospace robotics, particularly in the domain of space manipulators and their control systems, make him an outstanding candidate for the Best Researcher Award. His work on reactionless control, trajectory optimization, and hardware-in-the-loop simulations addresses critical challenges in modern aerospace engineering.

  1. Pioneering Publications: Dr. Zong has authored impactful papers in high-ranking journals such as IEEE Transactions on Aerospace and Electronic Systems and Aerospace Science and Technology. Key works include advancements in reactionless control for free-floating space manipulators and concurrent rendezvous control of underactuated manipulators.
  2. Global Research Exposure: As a visiting scholar at the University of Toronto Institute for Aerospace Studies, Dr. Zong collaborated internationally, enhancing the global applicability and validation of his research.
  3. Advanced Methodologies: His research employs cutting-edge approaches, such as mixed-integer predictive control, concurrent learning, and game-theoretic optimization, to address practical and theoretical aerospace challenges.
  4. Proven Impact: His work has been cited frequently, reflecting its relevance and influence in academia and industry. Topics like modular and reconfigurable robotics demonstrate innovative solutions for future aerospace missions.
  5. Leadership in Aerospace Research: As an Associate Professor at Northwestern Polytechnical University, Dr. Zong has demonstrated his capability in leading research teams, publishing prolifically, and mentoring future aerospace engineers.

AREAS FOR IMPROVEMENT

  1. Industry Collaboration: While Dr. Zong’s academic achievements are remarkable, deeper collaborations with aerospace industries could further validate his methodologies in real-world applications.
  2. Public Engagement: Increasing the visibility of his work through outreach programs or public talks could help bridge the gap between cutting-edge research and societal understanding of aerospace advancements.
  3. Interdisciplinary Expansion: Expanding his research to include intersections with artificial intelligence and machine learning could further enhance the robustness of his control systems for aerospace applications.

EDUCATION 

  • Ph.D. in Aerospace Vehicle Design (2015–2020)
    Northwestern Polytechnical University, Xi’an, China
    Thesis: “Optimal Trajectory Planning and Coordinated Control for Space Manipulators Capturing a Tumbling Target” | Advisor: Prof. Jianjun Luo
  • Visiting Scholar (2016–2018)
    University of Toronto Institute for Aerospace Studies, Toronto, Canada
    Subject: “Hardware-in-the-loop Synthesis and Analysis of Space Manipulators”
  • M.Sc. in Aerospace Vehicle Design (2013–2015)
    Northwestern Polytechnical University, Xi’an, China
    Thesis: “Occasion Determination and Control for Space Manipulators Capturing Tumbling Targets”
  • B.Sc. in Detection, Guidance, and Control Technology (2009–2013)
    Beijing Institute of Technology, Beijing, China

EXPERIENCE 

  • Associate Professor (Present)
    Northwestern Polytechnical University, Xi’an, China
    Specializing in aerospace robotics, modular systems, and trajectory optimization.
  • Visiting Researcher (2016–2018)
    University of Toronto Institute for Aerospace Studies, Toronto, Canada
    Conducted research in hardware-in-the-loop simulations for space manipulators under Prof. M. Reza Emami.
  • Postdoctoral Researcher (2020)
    Focused on control strategies for space manipulators and robotic systems.
  • Early Research Experience (2013–2020)
    Developed concurrent learning and control techniques for space manipulators and obstacle-avoidance strategies during doctoral and master’s studies.

AWARDS AND HONORS 

  • Best Researcher Award in Aerospace Robotics (2023)
  • IEEE Outstanding Contribution Award (2021)
  • Young Scientist Award by Northwestern Polytechnical University (2019)
  • Journal of Aerospace Excellence Reviewer Recognition (2018)
  • Top 10 Innovators in Robotics by China Robotics Forum (2017)

RESEARCH FOCUS 

Dr. Zong’s research focuses on aerospace robotics, including modular and reconfigurable robots, reactionless control mechanisms, and trajectory optimization. His pioneering work addresses critical challenges in space manipulator systems—particularly in the rendezvous and capture of tumbling targets. He is advancing technologies in hardware-in-the-loop simulations, obstacle avoidance strategies, and predictive control mechanisms. Dr. Zong is also investigating energy-efficient robotic systems and adaptive learning techniques for aerospace applications, driving the future of modular robotic designs and dynamic system stability.

PUBLICATION TOP NOTES

  1. 🚀 Concurrent Rendezvous Control of Underactuated Space Manipulators
  2. 🌌 Parameters Concurrent Learning and Reactionless Control in Post-capture of Unknown Targets by Space Manipulators
  3. 🤖 Reactionless Control of Free-floating Space Manipulators
  4. 🛰️ Concurrent Base-Arm Control of Space Manipulators with Optimal Rendezvous Trajectory
  5. 🌍 Obstacle Avoidance Handling and Mixed Integer Predictive Control for Space Robots
  6. 🌠 Optimal Capture Occasion Determination and Trajectory Generation for Space Robots Grasping Tumbling Objects
  7. 🔧 Optimal Concurrent Control for Space Manipulators Rendezvous and Capturing Targets under Actuator Saturation
  8. 🔬 Kinematics Modeling and Control of Spherical Rolling Contact Joint and Manipulator
  9. ⚙️ Control Verifications of Space Manipulators Using Ground Platforms
  10. ✨ Energy Sharing Mechanism for a Freeform Robotic System-Freebot

CONCLUSION

Dr. Lijun Zong’s expertise and impactful contributions to aerospace robotics position him as a strong contender for the Best Researcher Award. His innovative work addresses pivotal challenges in space exploration, offering practical and theoretical solutions that elevate the field of aerospace engineering. With continued advancements and increased interdisciplinary collaborations, Dr. Zong is well-poised to maintain his trajectory as a leader in aerospace research.

Bin Yang – AI for Everything – Best Researcher Award

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Bin Yang - AI for Everything - Best Researcher Award

Chongqing University of Posts and Telecommunications - China

AUTHOR PROFILE

SCOPUS

ORCID

🧑‍🏫 ACADEMIC BACKGROUND AND RESEARCH PASSION

Dr. Bin Yang, also known as Sean Bin Yang, is an Assistant Professor at Chongqing University of Posts and Telecommunications. With a deep passion for leveraging big data and artificial intelligence (AI) to address urban challenges, he has been making significant contributions to the field. He is also a member of the Chongqing Key Laboratory of Image Cognition, working closely with Prof. Xinbo Gao.

🎓 EDUCATION AND GLOBAL COLLABORATIONS

Dr. Yang obtained his Ph.D. in Computer Science from Aalborg University in 2022, under the guidance of Prof. Bin Yang and Associate Prof. Jilin Hu. During his doctoral studies, he collaborated with renowned researchers at the Center for Data-Intensive Systems (Daisy) and the Machine Learning Group. He also spent time at the Mila-Quebec AI Institute in Canada, working with Associate Prof. Jian Tang.

📚 PROLIFIC PUBLICATION RECORD

Dr. Yang has authored more than 20 peer-reviewed publications in prestigious international journals and conferences, including KDD, ICML, and TKDE. His work, such as the development of lightweight path representation models, has gathered over 452 citations, with an h-index of 13. His innovative research in data mining, machine learning, and AI continues to push the boundaries of knowledge in these fields.

💡 INNOVATIVE PATENTS AND TECHNOLOGY APPLICATIONS

Dr. Yang's commitment to practical applications of his research is demonstrated by his filing of over 10 patents in China. These patents reflect his dedication to advancing technology through innovation, particularly in the fields of AI-driven solutions for urban and transportation challenges.

🎓 SUPERVISION AND MENTORSHIP

As a dedicated mentor, Dr. Yang has supervised numerous student research projects, including those on construction waste management through AI techniques. His guidance has led to the publication of impactful research articles, helping his students make meaningful contributions to the field of artificial intelligence and urban problem-solving.

🔬 RESEARCH IN AI AND URBAN CHALLENGES

Dr. Yang's research focuses on using AI to tackle complex urban issues, such as waste management, transportation optimization, and infrastructure development. His work in path representation learning, unsupervised learning, and predictive autoscaling has significantly contributed to the advancement of smart city technologies.

🏅 CONFERENCE AND JOURNAL INVOLVEMENT

Dr. Yang is an active member of the research community, serving as a Program Committee member for top conferences like ICML, KDD, and IJCAI. His expertise is frequently sought as a reviewer for leading journals such as IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Intelligent Transportation Systems, highlighting his influence in the AI and big data research domains.

NOTABLE PUBLICATION

Title:Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve
Authors: Y. Li, B. Yang, T. Zheng, Y. Li, M. Cui, S. Peeta
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2015

Title: Unsupervised path representation learning with curriculum negative sampling
Authors: S.B. Yang, C. Guo, J. Hu, J. Tang, B. Yang
Journal: arXiv preprint arXiv:2106.09373

Title: Context-aware path ranking in road networks
Authors: S.B. Yang, C. Guo, B. Yang
Journal: IEEE Transactions on Knowledge and Data Engineering
Year: 2020

Title: Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve
Authors: B. Yang, M. Liu, H. Kim, X. Cui
Journal: Journal of Process Control
Year: 2018

Title: An extended continuum model incorporating the electronic throttle dynamics for traffic flow
Authors: Y. Li, H. Yang, B. Yang, T. Zheng, C. Zhang
Journal: Nonlinear Dynamics
Year: 2018

Jiaming Zhong – Artificial intelligence – Best Researcher Award

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Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing

Senbagavalli – Artificial Intelligence – Best Researcher Award

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Senbagavalli - Artificial Intelligence - Best Researcher Award

Alliance University - India

AUTHOR PROFILE

SCOPUS

EXPERT IN OPINION MINING AND FEATURE SELECTION

Senbagavalli's groundbreaking research in opinion mining of health data for cardiovascular disease diagnosis using an unsupervised feature selection algorithm spans five years. Her Ph.D. work is a testament to her dedication to leveraging data for medical advancements.

FACIAL RECOGNITION INNOVATOR

With a master's degree in engineering, Senbagavalli developed a face recognition system using Laplacian faces, showcasing her expertise in computer vision and pattern recognition. This project exemplified her ability to apply complex algorithms to practical applications within six months.

PIONEER IN UNICODE FILE SYSTEMS

During her undergraduate studies, Senbagavalli created a file system using the Unicode character set, a project completed in just six months. Her work in this area highlights her proficiency in software development and system design.

CREATOR OF GRAPHIC GAMING SYSTEMS

In her mini-project as an undergraduate, she developed a gaming system using graphics within three months. This early project laid the foundation for her interest in interactive and visual computing systems.

SEASONED ACADEMIC AND PROFESSOR

With 18 years and 7 months of teaching experience, Senbagavalli has held positions at prestigious institutions, including Alliance University and Kuppam Engineering College. Her extensive experience has made her a respected figure in the academic community.

VERSATILE SUBJECT EXPERT

Senbagavalli has taught a wide range of subjects to undergraduate, postgraduate, and Ph.D. students, including Data Modeling and Optimization, Object-Oriented Programming, and Software Engineering. Her comprehensive knowledge spans multiple domains of computer science.

ACTIVE RESEARCHER AND REVIEWER

An active member of various academic councils and editorial boards, Senbagavalli reviews for renowned publishers like Bentham Science and Elsevier. Her involvement in curriculum development, project evaluation, and seminar organization reflects her commitment to academic excellence and continuous learning.

NOTABLE PUBLICATION

Identification of Biomarker for Autism Spectrum Disorder Using EEG: A Review.
Authors: K. Lalli, M. Senbagavalli
Year: 2023
Conference: Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, pp. 45–50

Facemask Detection System Using CNN Model.
Authors: M. Senbagavalli, S. Debnath, R. Rajagopal, K. Ghildial
Year: 2023
Conference: International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

An Evaluation of Machine Learning Techniques for Detecting Banking Frauds.
Authors: R. Rajagopal, M. Senbagavalli, S. Debnath, K. Darshan, K.S. Varun Tejas
Year: 2023
Conference: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 359–365

Deep Learning Model for Flood Estimate and Relief Management System Using Hybrid Algorithm.
Authors: M. Senbagavalli, V. Sathiyamoorthi, S.K. Manju Bargavi, S. Shekarappa G., T. Jesudas
Year: 2023
Book: Artificial Intelligence and Machine Learning in Smart City Planning, pp. 29–44

An Effective Model for Predicting Agricultural Crop Yield on Remote Sensing Hyper-Spectral Images Using Adaptive Logistic Regression Classifier.
Authors: V. Sathiyamoorthi, P. Harshavardhanan, H. Azath, A.M. Viswa Bharathy, B.S. Chokkalingam
Year: 2022
Journal: Concurrency and Computation: Practice and Experience, 34(25), e7242

Everton – Artificial Intelligence – Best Researcher Award

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Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112

Ali Haider Khan – AI – Best Researcher Award

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Ali Haider Khan - AI - Best Researcher Award

Ziauddin University - Pakistan

AUTHOR PROFILE

SCOPUS

EXPERIENCE

Ali Haider Khan is a seasoned biomedical engineer with extensive experience across various hospitals and enterprises in Pakistan. His tenure at Bahawal Victoria Hospital involved working with clinical laboratory equipment, ensuring their optimal functionality. At Al-Qadir Enterprises, his role in Radiology, Ultrasound, and Operating Theaters showcased his commitment to enhancing patient care. His earlier internships at Doctor’s Hospital and Ziauddin Hospital provided him with a solid foundation in biomedical engineering, covering aspects from service and safety to supply chain management.

UNIVERSITY PROJECTS

Ali's academic projects demonstrate his innovative spirit and technical expertise. His pioneering work on the real-time recognition of Pakistani Sign Language through a web application highlights his proficiency in using CNN and LSTM models. Additionally, his research on the trans-vascular transport efficiency of nanoparticles in tumor microenvironments via Computational Fluid Dynamics (CFD) shows his adeptness in using Ansys software. His project on anxiety detection and massage-based control through pulse oximetry exemplifies his ability to combine technology and healthcare for therapeutic solutions.

SKILLS & INTERESTS

Ali is well-versed in frontend development, Python, Microsoft Azure, and various other technical tools like Visual Studio and Jupyter Notebook. His creative skills extend to Photoshop and Canva, and he is an effective communicator. Outside of his professional interests, Ali enjoys playing cricket and table tennis and has a keen interest in researching economic issues.

ACHIEVEMENTS

Ali's accomplishments are a testament to his dedication and continuous learning. He completed a crash course on Python by Google and participated in the 7th All Pakistan DUHS-DICE Health Innovation Exhibition. His proficiency in the German language was honed at the Goethe Institute, Karachi. He also attended the IEEE Asia Pacific Comsoc Summer School on Autonomous Systems and completed courses on web development and artificial intelligence from prestigious institutions like the University of Michigan and Accenture.

MEMBERSHIPS

Ali is an active member of several esteemed organizations, including the Harvard Business School Club of Pakistan, the Institute of Electrical and Electronics Engineers (IEEE), and the Society for Peace and Harmony. These memberships reflect his commitment to professional development and community engagement.

PUBLICATIONS

Ali has contributed significantly to the field of biomedical engineering and computer science through his publications. His work on the recognition and classification of the Urdu sign language dataset was published in PeerJ Computer Science. He also co-authored papers on deep learning approaches to Pakistani Sign Language recognition and the simulation of transvascular transport of nanoparticles in tumor microenvironments, published in top journals like Scientific Reports and Engineering Applications of Artificial Intelligence.

FUTURE DIRECTIONS

Looking ahead, Ali aims to further his research in biomedical engineering, focusing on innovative solutions that bridge technology and healthcare. His future projects will continue to explore advanced applications of artificial intelligence and computational methods to improve patient care and medical outcomes.

NOTABLE PUBLICATION

Simulation of Transvascular Transport of Nanoparticles in Tumor Microenvironments for Drug Delivery Applications

Authors: Shabbir, F., Mujeeb, A.A., Jawed, S.F., Khan, A.H., Shakeel, C.S.
Year: 2024
Journal: Scientific Reports
Volume: 14

A Neural-Network Based Web Application on Real-Time Recognition of Pakistani Sign Language

Authors: Mujeeb, A.A., Khan, A.H., Khalid, S., Mirza, M.S., Khan, S.J.
Year: 2024
Journal: Engineering Applications of Artificial Intelligence

A Computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset

Authors: Zahid, H., Rashid, M., Syed, S.A., Mujeeb, A.A., Khan, A.H.
Year: 2022
Journal: PeerJ Computer Science