Takeshi Nikawa | Biochemistry | Research Excellence Award

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Prof. Dr. Takeshi Nikawa | Biochemistry | Research Excellence Award

Tokushima University Graduate School | Japan

Prof. Dr. Takeshi Nikawa is a distinguished researcher at Tokushima University, Japan, with expertise in skeletal muscle physiology, molecular biology, and nutritional interventions. His research explores the mechanisms underlying muscle atrophy, mitochondrial function, and gene regulation during myogenesis, aiming to understand how these processes impact aging, metabolism, and overall health. Nikawa’s work integrates experimental studies with translational approaches to develop strategies for maintaining muscle mass and function, particularly in aging populations or individuals at risk of muscle degeneration. He actively collaborates with international scientists across multiple disciplines, fostering knowledge exchange and advancing global research initiatives. Through his publications and applied studies, Nikawa contributes to both fundamental scientific understanding and practical interventions, supporting the development of therapeutic, nutritional, and lifestyle strategies that enhance quality of life and address key societal challenges related to health and aging.

Citation Metrics (Scopus)

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4,787

Documents

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View Scopus 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

Xiang Li | Computer Science | Best Researcher Award

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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

Fengyu Liu | Computer Science | Best Researcher Award

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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

Qiao Ke | Deep Learning | Best Researcher Award

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🌟Assist Prof Dr. Qiao Ke, Deep Learning, Best Researcher Award🏆

  Assistant professor at Northwestern Polytechnical University, China

Qiao Ke is an Assistant Professor at Northwestern Polytechnical University, specializing in Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and Internet of Things. Qiao holds a Ph.D. in Mathematics from Xi’an Jiao Tong University and has been actively engaged in research, contributing significantly to various areas of computational mathematics and artificial intelligence.

Author Metrics:

Ke, Qiao – Scopus Profile

Orcid Profile

Qiao Ke is affiliated with Northwestern Polytechnical University in Xi’an, China. The Scopus Author Identifier 56465532300 provides valuable metrics regarding their academic contributions.

  • Citations: Qiao Ke has received a total of 481 citations across 420 documents, indicating the impact of their research on the academic community.
  • Documents: The author has contributed to 16 documents, showcasing a consistent and substantive scholarly output.
  • h-index: With an h-index of 8, Qiao Ke has demonstrated a noteworthy level of influence in their field. The h-index is a metric that considers both the number of publications and the number of citations they receive.

These metrics reflect the academic impact and productivity of Qiao Ke, highlighting their contributions to the scholarly landscape. The provided information encourages further exploration into the specific content and context of their publications for a comprehensive understanding of their research achievements.

Education:

Qiao Ke pursued a B.S. in Mathematics from Shaanxi Normal University, an M.S. in Mathematics, and a Ph.D. in Mathematics from Xi’an Jiao Tong University. Additionally, they completed postdoctoral research in the Department of Computer Science at Northwestern Polytechnical University.

Research Focus:

Qiao Ke’s research interests span Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and the Internet of Things. Notably, their work includes innovative contributions to neural frameworks for software models, hierarchical search-based code generation, and adaptive disentangled representation learning.

Professional Journey:

Qiao Ke’s professional journey involves serving as an Assistant Professor at the School of Mathematics and Statistics, Northwestern Polytechnical University. They have also actively participated as a reviewer for several reputed journals and conferences, demonstrating their commitment to scholarly peer review.

Publications Top Noted & Contributions:

Qiao Ke has made significant contributions to the field, with publications in respected journals and conferences. Notable works include research on modular neural frameworks for software model connections, deep hierarchical search-based code generation, and adaptive disentangled representation learning.

A research paper titled “RRGcode: Deep hierarchical search-based code generation.” The paper addresses the challenges of retrieval-augmented code generation, where a retrieval model is used to select relevant code snippets from a code corpus to strengthen the generation model. The primary concern is that if the retrieval corpus contains errors or sub-optimal examples, the generation model might replicate these mistakes in the generated code.

To overcome these challenges, the authors propose RRGcode, a deep hierarchical search-based code generation framework. The key components of RRGcode are outlined as follows:

  1. Retrieval: The framework first retrieves relevant code candidates from a large code corpus. This initial retrieval step aims to gather a set of potential code snippets based on the given query.
  2. Re-ranking: A re-ranking model is introduced to fine-tune the initial retrieved code rankings. This involves a detailed semantic comparison between the code candidates and the query, ensuring that only the most relevant and accurate candidates are considered. The re-ranking process aims to mitigate the risk of replicating errors from the retrieval corpus.
  3. Generation: The re-ranked top-K codes, along with the query, serve as input for the code generation model. This final step focuses on generating high-quality and reliable code based on the refined set of code candidates.

The authors claim that RRGcode demonstrates state-of-the-art performance in code generation tasks through extensive experiments. The deep hierarchical search-based approach aims to improve the quality of generated code by addressing the limitations associated with erroneous or sub-optimal code examples present in the retrieval corpus.

1. Title: Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purposes

2. Title: Intelligent Internet of Things System for Smart Home Optimal Convection

  • Publication Date: June 2021
  • Journal: IEEE Transactions on Industrial Informatics
  • DOI: 10.1109/tii.2020.3009094
  • ISSN: 1551-3203, 1941-0050

3. Title: High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

  • Publication Date: November 28, 2020
  • Journal: Security and Communication Networks
  • DOI: 10.1155/2020/8889317
  • ISSN: 1939-0122, 1939-0114

4. Title: Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach

5. Title: Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Research Timeline:

Qiao Ke’s research journey spans from their Bachelor’s degree at Shaanxi Normal University in 2012 to their current role as an Assistant Professor at Northwestern Polytechnical University. Notable milestones include completing a Ph.D., engaging in postdoctoral research, and actively contributing to various research projects, including leadership roles in national and provincial-level foundations.

Dawei Zhang | Computer Vision and Deep Learning | Best Researcher Award

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🌟Dr. Dawei Zhang, Zhejiang Normal University, China:  Computer Vision and Deep Learning🏆
Professional Profiles:

Bio Summary:

Dawei Zhang is a Ph.D. and Assistant Professor in the Department of Computer Science and Technology at Zhejiang Normal University, located in Jinhua, China. He holds expertise in computer vision, deep learning, and multimedia computing, with a focus on areas such as visual object tracking, video object segmentation, lightweight neural networks, adversarial attacks, and multi-modal information fusion.

Research Focus:

  1. Visual Object Tracking and Video Object Segmentation
  2. Light-weight Neural Networks for Mobile or Edge Computing Devices
  3. Research on Adversarial Attacks and Interpretability in Deep Learning
  4. Applications of Multi-modal Information Fusion in Vision and Language

Professional Journey:

  • Ph.D. (2017.09-2022.06) – Zhejiang Normal University, supervised by Prof. Zhonglong Zheng & Xiaoqin Zhang
  • Visiting Intern (2021.05-2021.09) – ISTBI, Fudan University, supervised by Prof. Yanwei Fu
  • B.E. (2013.09-2017.06) – Huaiyin Institute of Technology, supervised by Prof. Sen Xia

Honors & Awards:

  • 2023: 2nd “Chengtai Gonghao” Qihang Teaching Scholarship of Zhejiang Normal University
  • 2022: Talent Ambassador of Wucheng District, Jinhua City, Zhejiang Province
  • 2022: Outstanding Doctoral Dissertation Award of Zhejiang Normal University
  • 2022: Outstanding Graduate Students of Zhejiang Province
  • 2022: “Top-10 Students” of GREENTOWN Group in Zhejiang Normal University
  • 2021: National Scholarship for Postgraduate Students
  • 2018-2021: First class Academic Scholarship of Zhejiang Normal University
  • 2021: “Top-10 Academic Stars” for Graduate Students of Zhejiang Normal University
  • 2020: Academic Innovation Scholarship of Zhejiang Normal University
  • 2020: Outstanding Paper Award of National Conference of Computer Application of CCF

Publications Top Noted & Contributions:

  • Journals: Several papers in prominent journals including International Journal of Machine Learning and Cybernetics, Neurocomputing, IEEE Access, and Sensors.
  • Conferences: Contributions to conferences such as ICML, AAAI, ACM MM, and more, with papers accepted in CCF-A, CCF-B, and CCF-C category conferences.

Title: “Cross Channel Aggregation Similarity Network for Salient Object Detection”

  • Journal: International Journal of Machine Learning and Cybernetics
  • Year: 2022
  • Citations: 8

Title: “UAST: Uncertainty-Aware Siamese Tracking”

  • Conference: International Conference on Machine Learning (ICML), 2022
  • Year: 2022
  • Citations: 11

Title: “Deep Regression Tracking with Graph Attention”

  • Conference: International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2022
  • Year: 2022
  • Citations: 0

Title: “CSART: Channel and Spatial Attention-Guided Residual Learning for Real-Time Object Tracking”

  • Journal: Neurocomputing
  • Year: 2021
  • Citations: 19

Title: “Global Perception Attention Network for Fine-Grained Visual Classification”

  • Conference: International Conference on Computer Communication and Artificial Intelligence (CCAI), 2021
  • Year: 2021
  • Citations: 0

Author Metrics:

  • Total Citations: 170
  • h-index: 8
  • i10-index: 6
  • Documents: 16

Research Timeline:

  • Ongoing: Conducting research on Lightweight Siamese Networks for Efficient UAV Target Tracking (2023-2025).
  • Ongoing: Leading research on Key Algorithms of Intelligent Video Surveillance System in Smart Campus (2023-2025).
  • Ongoing: Participating in Information Asynchronous Propagation Traceability for Temporal Networks (2023-2025).
  • Ongoing: Contributing to Research on Trusted Target Tracking Based on Deep Learning in Intelligent Video Analysis (2023-2026).
  • Ongoing: Involved in Research on Visual Object Tracking Algorithms in Complex Scenarios (2022-2024).