Bin Liu | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

Prof. Bin Liu | Computer Science | Research Excellence Award

Professor | Northwest A&F University | China

Prof. Bin Liu is a researcher at Northwest A&F University, Yangling, China, with expertise in artificial intelligence, computer vision, agricultural informatics, and large-scale model training. He has published 69 Scopus-indexed documents, receiving approximately 2,949 citations and achieving an h-index of 18, reflecting sustained academic impact. His recent work focuses on multi-source data fusion, multimodal learning, remote sensing change detection, and efficient parallel training pipelines for large models, with publications in reputable venues such as IEEE Transactions on Computers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and Applied Sciences. Liu has collaborated with over 140 co-authors, demonstrating strong interdisciplinary and international research engagement. His research contributes to societal needs by advancing intelligent agricultural disease diagnosis, improving crop monitoring, and enhancing the efficiency of large-scale AI systems, supporting sustainable agriculture and data-driven environmental management.

Citation Metrics (Scopus)

2949
2200
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0

Citations

2,949

Documents

69

h-index

18

Citations

Documents

h-index

View Scopus Profile
 View Scopus Profile

Featured Publications


MDS-Net: An image-text enhanced multimodal dual-branch Siamese network for remote sensing change detection


– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025

PRT: An efficient pipeline reuse technology for large models training


– IEEE International Conference on Cluster Computing (CLUSTER), 2025

VMF-SSD: A novel V-space based multi-scale feature fusion SSD for apple leaf disease detection


– IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023

Aleeza Adeel | Computer Science | Research Excellence Award

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Mrs. Aleeza Adeel | Computer Science | Research Excellence Award

The University of Waikato | New Zealand

Mrs. Aleeza Adeel is a Ph.D. student at the School of Computing and Mathematical Sciences, University of Waikato, New Zealand, specializing in digital twin frameworks, sustainable energy systems, and user-centered computing solutions. Her research focuses on developing interoperable and scalable digital twin technologies to optimize energy system management, enhance operational efficiency, and support sustainable resource utilization. She has contributed to peer-reviewed publications, including a recent article in Energies on an interoperable user-centered digital twin framework, demonstrating her commitment to integrating advanced computational models with real-world energy systems. Aleeza collaborates with interdisciplinary researchers, including experts in energy management and computational modeling, to ensure her work addresses both technical rigor and societal relevance. Her research contributes to sustainable energy transitions by providing data-driven, user-centric solutions that improve system performance, reduce environmental impact, and support informed decision-making in complex energy infrastructures.

Profile: View ORCID Profile 

Featured Publication


An Interoperable User‑Centred Digital Twin Framework for Sustainable Energy System Management

– Adeel, A., Apperley, M., & Walmsley, T. G., Energies, 2026, 19(2), Article 333

Miroslaw Kozielski | Computer Science | Best Researcher Award

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Mr. Miroslaw Kozielski | Computer Science | Best Researcher Award

Kazimierz Wielki University | Poland

Mr. Mirosław Kozielski is a researcher at Kazimierz Wielki University in Bydgoszcz, Poland, specializing in computer science, with a strong focus on natural language processing (NLP), industrial informatics, and Industry 4.0/5.0 technologies. His research addresses the use of intelligent language-based systems for automated industrial documentation, knowledge representation, and digital transformation in modern manufacturing environments. He has authored 7 peer-reviewed publications, which have accumulated 35 citations, and holds an h-index of 3, reflecting a focused and emerging academic impact. Dr. Kozielski collaborates with interdisciplinary teams, contributing to the integration of artificial intelligence with industrial and organizational processes. His work supports the development of efficient, human-centric, and sustainable industrial systems, with societal impact through improved documentation quality, enhanced knowledge accessibility, and the practical adoption of advanced AI-driven solutions in contemporary industrial ecosystems.

Citation Metrics (Scopus)

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25
15
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35

Documents

7

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3

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View Scopus Profile
 View ORCID Profile

Featured Publications

Christian Schachtner | Computer Science | Research Excellence Award

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Prof. Dr. Christian Schachtner | Computer Science | Research Excellence Award

Professor of Business Informatics | RheinMain University of Applied Sciences | Germany

Dr. Christian Schachtner is a researcher at Fachhochschule Wiesbaden, Germany, specializing in safety culture, social impact assessment, and sustainable development in technical and organizational systems. His work bridges corporate social responsibility, environmental management, and smart district development, emphasizing practical solutions to complex societal challenges. According to Scopus, he has authored 23 scholarly publications, received 9 citations, and holds an h-index of 2. His recent research includes open-access work on the determinants of social impact through safety culture in technical organizations and scholarly contributions to book chapters on smart regional and district development initiatives. Dr. Schachtner actively collaborates with international researchers, supporting interdisciplinary perspectives and knowledge exchange. His research contributes to improving organizational governance, enhancing safety performance, and promoting socially responsible and sustainable practices across technical and socio-economic domains.

Citation Metrics (Scopus)

23
15
10
5
0

Citations

9

Documents

23

h-index

2

Citations

Documents

h-index

View Google Scholar Profile
 View Scopus Profile
 View ORCID Profile

Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

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Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Shougui Zhang | Computational mathematics | Best Researcher Award

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Prof. Dr. Shougui Zhang | Computational mathematics | Best Researcher Award

Teacher| Chongqing Normal Univercity | China

Dr. Xin Li is an accomplished scholar and educator specializing in clinical medicine and sports health sciences, currently serving as an Associate Professor at Tianjin Normal University since 2006. She obtained her Doctor of Medicine (MD) in Clinical Medicine from Tianjin Medical University in July 2006. Over nearly two decades of academic and research experience, Dr. Li has established herself as a leading expert at the intersection of clinical medicine, exercise science, and health management. Her teaching portfolio encompasses a range of courses in sports health, exercise rehabilitation, and health management, with a focus on integrating clinical case studies into theoretical instruction to enhance students’ practical competencies and professional insight. Dr. Li’s research primarily addresses exercise interventions for chronic diseases, sports injury prevention, and rehabilitation science, contributing to the growing evidence base linking physical activity with disease prevention and functional recovery. She has led and participated in numerous provincial and municipal research projects, achieving notable outcomes that have advanced the application of medical principles in sports health practice. To date, Dr. Li has published over ten academic papers in peer-reviewed core journals and international conferences, several of which have been widely cited and recognized within the academic community for their methodological rigor and clinical relevance. Her collaborative approach bridges disciplines such as physiology, public health, and kinesiology, fostering innovation and interdisciplinary integration in sports medicine research. Beyond academia, Dr. Li’s work holds significant societal impact, promoting the scientific development of exercise-based health strategies for chronic disease management and population well-being. Combining solid clinical expertise, pedagogical excellence, and a strong research record, Dr. Xin Li continues to contribute meaningfully to the advancement of sports health education and evidence-based medical practice in China and beyond.

Profile: Scopus | ORCID

Featured Publications

Zhang, S. (2025). A self-adaptive alternating direction multiplier method for variational inequality in two domains. Applied Mathematics and Mechanics.

Zhang, S., & Coauthors. (2025). Analysis of a Crank–Nicolson fast element-free Galerkin method for the nonlinear complex Ginzburg–Landau equation. Journal of Computational and Applied Mathematics.

Zhang, S. (2024). Self-adaptive alternating direction method of multiplier for a fourth order variational inequality. Journal of Inequalities and Applications.

Professor Shougui Zhang’s research advances the development of efficient computational methods for complex variational inequalities and partial differential equations, strengthening the mathematical foundation for modern engineering, physics, and optimization problems. His work enhances scientific computing capabilities, supporting innovation in technology, modeling, and data-driven decision-making across academic and industrial domains worldwide.

Xiang Li | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Ms. Xiang Li | Computer Science | Best Researcher Award

PHD candidate at University of Chinese Academy of Sciences, China

Xiang Li, a Ph.D. candidate at the University of Chinese Academy of Sciences, demonstrates exceptional potential for the Best Researcher Award. With a solid academic foundation—ranking in the top 5–7% throughout his studies—he has excelled in areas such as deep learning, stochastic processes, and pattern recognition. His research focuses on cross-domain few-shot learning, addressing real-world challenges like medical lesion detection and remote sensing scene classification. He has published in the prestigious Knowledge-Based Systems journal and submitted another to IEEE Transactions on Geoscience and Remote Sensing. Xiang has also earned accolades, including the Second Prize in the National Mathematical Modeling Competition and a top-tier finish in the Huawei Software Elite Challenge. His future interests in class-incremental learning and prompt tuning highlight a clear vision for impactful research. Overall, Xiang Li’s innovative contributions, academic excellence, and commitment to advancing AI technologies make him a strong and deserving candidate for this recognition.

Professional Profile 

Education

Xiang Li has demonstrated outstanding academic performance throughout his educational journey. He earned his Bachelor’s degree in Information and Computer Science from Shandong University, graduating in July 2021 with an impressive GPA of 91.73/100, placing him in the top 7.46% of his class. His coursework included high-level subjects such as Mathematical Statistics, Operations Research, and Advanced Algebra, in which he consistently achieved top scores. Following this, he was admitted to the University of Chinese Academy of Sciences, where he completed foundational Ph.D. training from September 2021 to July 2022, ranking in the top 5% with a GPA of 87.13/100. His advanced studies covered critical areas like Matrix Analysis, Deep Learning, and Pattern Recognition. Currently, he is conducting doctoral research at the Institute of Optics and Electronics, Chinese Academy of Sciences, focusing on cross-domain few-shot learning. His educational background reflects strong technical competence and a solid foundation for innovative research.

Professional Experience

Xiang Li has accumulated valuable professional research experience during his Ph.D. studies at the Institute of Optics and Electronics, Chinese Academy of Sciences. His primary research focuses on cross-domain few-shot learning, a vital area in artificial intelligence that addresses challenges in data-scarce environments. He has led and contributed to key projects, including the development of a dynamic representation enhancement framework to improve model generalization across different domains, and the fine-tuning of general pre-trained models for few-shot remote sensing scene classification. In addition to research, Xiang has actively participated in national competitions, winning third prize in the Huawei Software Elite Challenge for designing a traffic scheduling plan and contributing to infrared small target detection strategies in another competition. These experiences highlight his strong technical problem-solving skills, teamwork, and ability to apply theoretical knowledge to real-world challenges. His professional work reflects both depth and versatility, positioning him as a highly capable and innovative young researcher.

Research Interest

Xiang Li’s research interests lie at the forefront of artificial intelligence, with a strong focus on cross-domain few-shot learning, computer vision, and representation learning. He is particularly interested in developing algorithms that enable models to perform effectively in data-scarce scenarios, addressing the challenges posed by domain shifts and limited labeled data. His current work involves enhancing the representational capacity of models to learn diverse and meaningful features across domains, with applications in medical image analysis and remote sensing. Xiang is also exploring techniques for fine-tuning general pre-trained models to adapt to new tasks without extensive retraining. Looking ahead, he is keen on advancing research in few-shot class-incremental learning, where models continuously adapt to new classes with minimal data, and in prompt tuning for vision-language pre-trained models, which integrates natural language processing with visual recognition. His interests reflect a forward-thinking approach to building intelligent systems capable of learning efficiently and generalizing across tasks.

Award and Honor

Xiang Li has received several prestigious awards and honors in recognition of his academic excellence and research capabilities. During his undergraduate and doctoral studies, he was consistently awarded scholarships from both Shandong University and the University of Chinese Academy of Sciences, reflecting his outstanding academic performance and dedication. In June 2022, he was named a Merit Student at the University of Chinese Academy of Sciences, an honor reserved for top-performing students. His strong analytical and problem-solving skills were further recognized in national competitions, where he earned the Second Prize in the National College Students’ Mathematical Modeling Competition in 2019. Additionally, he played a key role in a team that won third prize in the Huawei Software Elite Challenge, a highly competitive event involving over 300 teams. These honors highlight his ability to excel both academically and practically, reinforcing his position as a promising and accomplished young researcher in the field of computer science.

Research skill

Xiang Li possesses a strong set of research skills that make him a capable and innovative scholar in the field of artificial intelligence and computer vision. His expertise spans advanced areas such as cross-domain few-shot learning, deep learning, and representation learning. He demonstrates exceptional analytical abilities, evident in his design and implementation of dynamic representation frameworks to enhance model generalization across diverse domains. Xiang is proficient in applying theoretical concepts to practical problems, as seen in his work on fine-tuning pre-trained models for remote sensing scene classification. His skill set includes programming, algorithm development, statistical analysis, and critical thinking, which he has effectively applied in both solo research and collaborative projects. Furthermore, his ability to publish in top-tier journals, such as Knowledge-Based Systems, reflects his competence in scientific writing, experimental design, and result interpretation. These research skills enable him to tackle complex challenges and contribute meaningfully to the advancement of intelligent systems.

Conclusion

Xiang Li is a highly promising young researcher with a solid academic foundation, well-defined research focus, and impactful contributions in the field of computer vision and machine learning. His achievements in cross-domain few-shot learning, publication in a top-tier journal, and award-winning competition experience clearly demonstrate excellence in research and innovation.

Publications Top Noted

  • Title: RSGPT: A remote sensing vision language model and benchmark
    Authors: Y. Hu, Yuan; J. Yuan, Jianlong; C. Wen, Congcong; Y. Liu, Yu; X. Li, Xiang
    Year: 2025

  • Title: Uni3DL: A Unified Model for 3D Vision-Language Understanding
    Authors: X. Li, Xiang; J. Ding, Jian; Z. Chen, Zhaoyang; M. Elhoseiny, Mohamed
    Year: 2025 (Conference Paper)

  • Title: 3D Shape Contrastive Representation Learning With Adversarial Examples
    Authors: C. Wen, Congcong; X. Li, Xiang; H. Huang, Hao; Y.S. Liu, Yu Shen; Y. Fang, Yi
    Year: 2025
    Journal: IEEE Transactions on Multimedia
    Citations: 4

  • Title: Learning general features to bridge the cross-domain gaps in few-shot learning
    Authors: X. Li, Xiang; H. Luo, Hui; G. Zhou, Gaofan; M. Li, Meihui; Y. Liu, Yunfeng
    Year: 2024
    Journal: Knowledge-Based Systems
    Citations: 1

Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Published on by Global Scholar Awards

Assoc. Prof. Dr. Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Doctored at University of Saravan, Iran

Assoc. Prof. Dr. Shayesteh Tabatabaei is a distinguished computer engineering researcher, ranked among the top 2% of scientists worldwide in 2024. She holds a Ph.D. in Computer Engineering and specializes in Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms. With numerous high-impact journal publications, she has significantly contributed to intelligent routing protocols and energy-efficient networking solutions. As an Associate Professor, she teaches advanced courses in Artificial Intelligence, Fuzzy Logic, and Distributed Systems while mentoring students and researchers. Recognized as a top researcher multiple times, she has also led workshops on ISI article writing, IoT, and wireless networks. Her expertise in computational methodologies and commitment to knowledge dissemination make her a key figure in her field. Dr. Tabatabaei’s research excellence, leadership, and dedication to innovation make her a strong candidate for prestigious academic awards, with potential for further global collaborations and industry-driven research initiatives.

Professional Profile 

Education

Assoc. Prof. Dr. Shayesteh Tabatabaei holds a Ph.D. in Computer Engineering from Tehran Science and Research University, Iran, earned in 2015 with an outstanding GPA of 18.63/20. Her doctoral research focused on developing intelligent routing protocols for mobile ad-hoc networks under the supervision of Dr. M. Teshnehlab. She completed her M.Sc. in Computer Engineering at Islamic Azad University of Shabestar in 2009, where she improved the AODV routing protocol using reinforcement learning, achieving a GPA of 18.69/20. Her academic journey began with a B.Sc. in Computer Engineering from the same university, graduating in 2006 with a GPA of 17.12/20. Throughout her education, Dr. Tabatabaei demonstrated excellence in research and innovation, particularly in wireless networks and intelligent algorithms. Her strong academic background has shaped her expertise in computer engineering, making her a leading researcher and educator in the field of network optimization, IoT, and artificial intelligence.

Professional Experience

Assoc. Prof. Dr. Shayesteh Tabatabaei is a highly accomplished academic and researcher in computer engineering, currently serving as an Associate Professor in the Department of Computer Engineering at the Higher Education Complex of Saravan, Iran. With extensive teaching experience, she has instructed both undergraduate and postgraduate courses in Artificial Intelligence, Fuzzy Logic, Distributed Systems, Advanced Database Systems, and Programming Languages such as C, C++, Python, and SQL. Her research focuses on Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms, with numerous high-impact journal publications and conference presentations. She has been recognized multiple times as a top researcher and has actively contributed to academic development by organizing workshops on ISI article writing, IoT, and wireless networks. Dr. Tabatabaei’s expertise extends to computational simulations and algorithm development, making her a leading figure in her field. Her dedication to education, research, and innovation continues to influence the next generation of computer engineers.

Research Interest

Assoc. Prof. Dr. Shayesteh Tabatabaei’s research interests lie at the intersection of intelligent computing and network optimization, focusing on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), Internet of Things (IoT), and Intelligent Algorithms. Her work aims to enhance the efficiency, reliability, and security of communication networks through advanced routing protocols, optimization algorithms, and artificial intelligence techniques. She has contributed significantly to energy-aware clustering, fault tolerance mechanisms, and adaptive routing in WSNs, utilizing machine learning, fuzzy logic, and evolutionary computing. Additionally, her research explores optimization algorithms such as Genetic Algorithms, Bee Colony Optimization, and Social Spider Optimization to improve network performance. Through her extensive publications in high-impact journals and conferences, Dr. Tabatabaei continues to advance the field of computational intelligence and networked systems. Her passion for innovation drives her to develop cutting-edge solutions for real-world challenges in modern communication technologies.

Award and Honor

Assoc. Prof. Dr. Shayesteh Tabatabaei has received multiple awards and honors in recognition of her outstanding contributions to research and academia. She has been ranked among the top 2% of scientists worldwide in 2024, highlighting her global impact in computer engineering. She has been recognized as the Top Researcher at various institutions multiple times, including Islamic Azad University of Malekan Branch in 2011, 2016, and 2017, and the Higher Education Complex of Saravan in 2019, 2021, and 2022. Her achievements reflect her dedication to advancing knowledge in wireless sensor networks, optimization algorithms, and artificial intelligence. In addition to her research excellence, she has led training workshops and mentored young scholars, further solidifying her reputation as a leader in her field. Her numerous accolades demonstrate her commitment to innovation, making her a strong candidate for prestigious academic and scientific awards on both national and international levels.

Research Skill

Assoc. Prof. Dr. Shayesteh Tabatabaei possesses strong research skills in computer engineering, wireless communication, and intelligent systems. Her expertise spans algorithm design, network optimization, artificial intelligence, and data analysis, with a particular focus on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), IoT, and optimization techniques. She is proficient in developing energy-efficient routing protocols, fault-tolerant clustering methods, and machine learning-based optimization algorithms. Dr. Tabatabaei has extensive experience with simulation tools such as MATLAB, R, Opnet, and GloMoSim, which she utilizes to validate her research findings. Additionally, she is skilled in multiple programming languages, including C, C++, Python, JavaScript, SQL, and Oracle, enabling her to implement and test computational models effectively. Her ability to integrate fuzzy logic, evolutionary algorithms, and artificial intelligence into network solutions showcases her innovative approach to problem-solving, making her a highly capable and influential researcher in the field.

Conclusion

Dr. Shayesteh Tabatabaei is highly qualified for the Women Researcher Award, given her global recognition, extensive research contributions, leadership in academia, and dedication to advancing knowledge in computer engineering. Strengthening international collaborations and industry partnerships could further elevate her impact.

Publications Top Noted

  • A novel fault tolerance energy-aware clustering method via social spider optimization (SSO) and fuzzy logic and mobile sink in wireless sensor networks (WSNs).

    • Cited by: 65
    • Year: 2020

  • A novel energy-aware clustering method via Lion Pride Optimizer Algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs).

    • Cited by: 50
    • Year: 2019

  • Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (wireless sensor networks).

    • Cited by: 47
    • Year: 2021

  • A new method to find a high reliable route in IoT by using reinforcement learning and fuzzy logic.

    • Cited by: 36
    • Year: 2020

  • Reliable routing algorithm based on clustering and mobile sink in wireless sensor networks.

    • Cited by: 30
    • Year: 2019

  • A novel method for clustering in WSNs via TOPSIS multi-criteria decision-making algorithm.

    • Cited by: 23
    • Year: 2020

  • Improved routing vehicular ad-hoc networks (VANETs) based on mobility and bandwidth available criteria using fuzzy logic.

    • Cited by: 20
    • Year: 2020

  • A new routing protocol to increase throughput in mobile ad hoc networks.

    • Cited by: 20
    • Year: 2015

  • Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink.

    • Cited by: 19
    • Year: 2022

Fengyu Liu | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559