Fengyu Liu | Computer Science | Best Researcher Award

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

Volodymyr Polishchuk | Computer Science | Best Researcher Award

Prof. Volodymyr Polishchuk | Computer Science | Best Researcher Award

Uzhhorod National University, Ukraine

Volodymyr Polishchuk is a distinguished academic specializing in information technology, fuzzy systems, and decision-making models. Currently serving as a Professor at both Uzhhorod National University in Ukraine and the Technical University of KoŔice in Slovakia, he has made significant contributions to the fields of artificial intelligence, risk assessment, and sustainable tourism. With a career spanning over a decade, he has co-authored numerous publications, including journal articles and book chapters, focusing on the application of advanced decision models in various sectors. His research is internationally recognized, and he is an active member of several academic networks. He is known for his interdisciplinary approach, bridging information technology with real-world challenges such as healthcare, aviation education, and urban development.

Professional ProfileĀ 

Education

Volodymyr Polishchuk holds a prestigious Doctor of Sciences (DrSc.) degree from Uzhhorod National University, where he also completed his undergraduate and graduate education. His academic journey in information technology, mathematics, and fuzzy systems laid a strong foundation for his future research and teaching. As a professor at the university, he has guided numerous students and collaborated on innovative projects. Additionally, his academic credentials are complemented by his position at the Technical University of KoŔice in Slovakia, where he continues to contribute to cutting-edge research in his fields of expertise. His educational background supports his broad interdisciplinary approach, allowing him to address complex problems in various domains such as tourism, healthcare, and risk management.

Professional Experience

Professor Polishchuk has been a dedicated faculty member at Uzhhorod National University since 2011, where he teaches and conducts research at the Faculty of Information Technology. Over the years, he has gained recognition for his expertise in decision-making models and fuzzy systems. In addition to his role at Uzhhorod, he has been a professor at the Technical University of KoŔice, Slovakia. His professional experience extends beyond teaching, as he has collaborated on numerous international research projects and published widely in top-tier journals. He has also worked on hybrid decision models for risk assessment in sectors such as sustainable tourism, healthcare, and aviation education. His leadership in academic research has earned him recognition through various academic platforms, and he continues to actively engage with the global research community.

Research Interests

Volodymyr Polishchukā€™s research primarily focuses on information technology, fuzzy systems, and decision-making models, with a particular emphasis on their practical applications across various industries. He is deeply engaged in developing hybrid models for evaluating complex processes, such as tourism sustainability, risk assessment, and healthcare outcomes. His work also explores the integration of artificial intelligence in decision-making, specifically in aviation education and urban development. Additionally, he is interested in the application of multicriteria decision analysis (MCDA) in solving real-world challenges. Polishchuk’s interdisciplinary approach allows him to connect cutting-edge technology with pressing global issues, contributing valuable insights to sectors like smart cities, start-up financing, and pandemic management. His research has significant implications for optimizing resource allocation, improving system efficiency, and mitigating risks in both public and private sectors.

Awards and Honors

Throughout his academic career, Volodymyr Polishchuk has earned several prestigious honors and recognition for his contributions to research and education. His interdisciplinary approach to problem-solving has led to numerous successful collaborations with leading academic and industry experts across Europe. He has been acknowledged by his peers for his innovative contributions to the fields of fuzzy logic, decision support systems, and sustainability models. Polishchukā€™s research papers are widely cited, indicating the significant impact his work has had on the academic community. His exceptional leadership in research has also helped foster international collaborations, particularly in the development of sustainable tourism models and risk assessment frameworks for emerging sectors. His continued excellence in academia and research is further demonstrated by his involvement in high-impact projects and his active participation in global conferences.

Publications Top Noted

  1. Artificial Intelligence Technology for Assessing the Practical Knowledge of Air Traffic Controller Students Based on Their Responses in Multitasking Situations
    • Authors: AntoÅ”ko, M., Polishchuk, V., Kelemen, M., Korniienko, A., Kelemen, M.
    • Year: 2025
    • Journal: Applied Sciences (Switzerland)
    • Volume: 15(1), 308
    • Citations: 0
  2. A large-scale decision-making model for the expediency of funding the development of tourism infrastructure in regions
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2025
    • Journal: Expert Systems
    • Volume: 42(1), e13443
    • Citations: 1
  3. On Convergence of the Uniform Norm and Approximation for Stochastic Processes from the Space FĻˆ(Ī©)
    • Authors: Rozora, I., Mlavets, Y., Vasylyk, O., Polishchuk, V.
    • Year: 2024
    • Journal: Journal of Theoretical Probability
    • Volume: 37(2), pp. 1627ā€“1653
    • Citations: 0
  4. THE IMPACT OF DIGITAL DISINFORMATION ON QUALITY OF LIFE: A FUZZY MODEL ASSESSMENT
    • Authors: Gavurova, B., Moravec, V., Hynek, N., Petruzelka, B., Stastna, L.
    • Year: 2024
    • Journal: Technological and Economic Development of Economy
    • Volume: 30(4), pp. 1120ā€“1145
    • Citations: 0
  5. An information-analytical system for assessing the level of automated news content according to the population structure ā€“ A platform for media literacy system development
    • Authors: Gavurova, B., Skare, M., Hynek, N., Moravec, V., Polishchuk, V.
    • Year: 2024
    • Journal: Technological Forecasting and Social Change
    • Volume: 200, 123161
    • Citations: 0
  6. Decision Support System Regarding the Possibility of Financing Cross-Border Cooperation Projects
    • Authors: Polishchuk, V., Kelemen, M., Polishchuk, I., Kelemen, M.
    • Year: 2024
    • Conference: CEUR Workshop Proceedings
    • Volume: 3702, pp. 58ā€“71
    • Citations: 0
  7. Hybrid Mathematical Model of Risk Assessment of UAV Flights Over Airports
    • Authors: Polishchuk, V., Kelemen, M., Kelemen, M., Scerba, M.
    • Year: 2024
    • Conference: New Trends in Civil Aviation
    • Citations: 0
  8. A Fuzzy Multicriteria Model of Sustainable Tourism: Examples From the V4 Countries
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2024
    • Journal: IEEE Transactions on Engineering Management
    • Volume: 71, pp. 12182ā€“12193
    • Citations: 6
  9. Fuzzy multicriteria evaluation model of cross-border cooperation projects under resource curse conditions
    • Authors: Skare, M., Gavurova, B., Polishchuk, V.
    • Year: 2023
    • Journal: Resources Policy
    • Volume: 85, 103871
    • Citations: 3
  10. A fuzzy model for evaluating the level of satisfaction of tourists regarding accommodation establishments according to social class on the example of V4 countries
  • Authors: Skare, M., Gavurova, B., Polishchuk, V., Nawazish, M.
  • Year: 2023
  • Journal: Technological Forecasting and Social Change
  • Volume: 193, 122609
  • Citations: 7

Meiyan Liang | Computer Science | Best Researcher Award

šŸŒŸAssoc Prof Dr. Meiyan Liang, Computer Science, Best Researcher Award šŸ†

  • Ā Associate Professor at Shanxi University, China

Meiyan Liang, PhD, is an accomplished researcher in the field of Instrument Science and Technology, with a focus on Deep Learning and Medical Image Processing. Currently affiliated with the School of Physics and Electronic Engineering at Shanxi University in China, Dr. Liang completed her PhD at the Opto-Electronic College, Beijing Institute of Technology. She has made significant contributions to the development of innovative technologies for the identification and classification of various medical conditions, particularly in cancer diagnosis. Her work spans both theoretical and experimental domains, with a particular emphasis on leveraging neural networks and terahertz imaging techniques. Dr. Liang’s expertise is recognized through numerous awards, patents, and a prolific publication record in prestigious journals.

Author Metrics

Scopus Profile

ORCID Profile

Dr. Liang’s research output is not only extensive but also impactful, as evidenced by her author metrics. She has consistently published in high-impact journals, demonstrating the significance of her work within the scientific community. Additionally, Dr. Liang’s patents highlight her innovative approach to problem-solving and technology development.

  • Citations: 138 citations across 136 documents
  • Documents: Authored 25 documents
  • h-index: 5

Education

Dr. Meiyan Liang obtained her PhD in Instrument Science and Technology from the Opto-Electronic College at Beijing Institute of Technology. Her doctoral research focused on the application of deep learning methodologies in medical image processing, particularly for cancer diagnosis.

Research Focus

Dr. Liang’s research primarily centers around two main areas: Deep Learning and Medical Image Processing. Within these domains, she specializes in utilizing neural networks for the interpretation and analysis of medical images, with a particular emphasis on cancer detection and classification. Her work also involves the integration of advanced imaging techniques, such as terahertz imaging, to develop novel diagnostic tools.

Professional Journey

Following her doctoral studies, Dr. Liang embarked on a professional journey that has seen her become an esteemed researcher in the field of medical imaging. She has held positions at various academic institutions, including her current role at Shanxi University. Throughout her career, Dr. Liang has secured research funding, published extensively, and obtained several patents for her innovative contributions to the field.

Honors & Awards

Dr. Liang’s outstanding contributions to her field have been recognized through numerous honors and awards. Notable accolades include being awarded the “Sanjin talent” by the government of Shanxi Province and receiving the “China Instrument & Control Society Scholarship” from the Chinese instrumentation society.

Research Timeline

Dr. Liang’s research timeline showcases her progression as a researcher and the evolution of her research interests. Starting from her doctoral studies, she has continued to expand her expertise and contribute to advancements in medical imaging technology. Her research timeline reflects a commitment to excellence and a dedication to addressing critical challenges in healthcare through innovative research.

Publications Noted & Contributions

Dr. Liang has made significant contributions to the academic community through her prolific publication record. Her research findings have been published in prestigious journals such as the IEEE Journal of Biomedical and Health Informatics, Computer Methods and Programs in Biomedicine, and The Visual Computer. These publications cover a wide range of topics, including interpretable inference, whole-slide image prediction, and pathology image restoration.

Title: Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

  • Authors: Liang, M., Wang, R., Liang, J., Zhang, T., Zhang, C.
  • Journal: IEEE Journal of Biomedical and Health Informatics, 2023, 27(12), pp. 6006ā€“6017
  • Abstract: This paper presents a method for interpretable inference and classification of tissue types in histological colorectal cancer slides using ensembles adaptive boosting prototype tree.

Title: Multi-scale self-attention generative adversarial network for pathology image restoration

  • Authors: Liang, M., Zhang, Q., Wang, G., Liu, H., Zhang, C.
  • Journal: Visual Computer, 2023, 39(9), pp. 4305ā€“4321
  • Abstract: This paper introduces a multi-scale self-attention generative adversarial network for pathology image restoration.
  • Citations: 1

Title: Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

  • Authors: Liang, M., Chen, Q., Li, B., Jiang, X., Zhang, C.
  • Journal: Computer Methods and Programs in Biomedicine, 2023, 229, 107268
  • Abstract: This paper proposes an interpretable classification method for pathology whole-slide images using an attention-based context-aware graph convolutional neural network.
  • Citations: 6

Title: A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy

  • Authors: Liu, H., Liu, X., Zhang, Z., Liang, M., Zhang, C.
  • Journal: Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 2022, 274, 121104
  • Abstract: This paper proposes a novel strategy for liquids discrimination based on THz reflection spectroscopy using the geometric product.
  • Citations: 1

Title: THz ISAR imaging using GPU-accelerated phase compensated back projection algorithm

  • Authors: Liang, M.-Y., Ren, Z.-Y., Li, G.-H., Zhang, C.-L., Fathy, A.E.
  • Journal: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 2022, 41(2), pp. 448ā€“456
  • Abstract: This paper presents THz ISAR imaging using a GPU-accelerated phase-compensated back projection algorithm.
  • Citations: 3