Riki Hendra Purba | Neural Networks | Research Excellence Award

Published on by Global Network Awards

Research Excellence Award

Riki Hendra Purba
Affiliation Universitas Pembangunan Nasional Veteran Jakarta
Country Indonesia
Scholar ID nzNQV9kAAAAJ
Documents 54
Citations 312
h-index 10
Subject Area Neural Networks
Event Global Network Awards

Riki Hendra Purba

Universitas Pembangunan Nasional Veteran Jakarta

Riki Hendra Purba is affiliated with Universitas Pembangunan Nasional Veteran Jakarta, Indonesia. His scholarly activities encompass neural networks, artificial intelligence, intelligent computing, and related computational methodologies. The available publication metrics indicate sustained academic productivity, with peer-reviewed publications, measurable citation impact, and an established h-index reflecting scholarly influence within his research domain.[1][2]

Abstract

The Research Excellence Award article presents an overview of the academic profile of Riki Hendra Purba and summarizes measurable indicators of scholarly activity. His research emphasizes neural networks and intelligent computational systems that support data-driven analysis, machine learning applications, and modern artificial intelligence methodologies. Publication output, citation performance, and scholarly engagement collectively indicate sustained contributions to computational research and interdisciplinary innovation.[1][3]

Keywords

  • Neural Networks
  • Artificial Intelligence
  • Machine Learning
  • Computational Intelligence
  • Deep Learning
  • Data Analytics
  • Research Excellence Award
  • Global Network Awards

Introduction

Neural network research has become an essential component of modern computer science because it enables predictive modeling, pattern recognition, intelligent automation, and adaptive decision-making across numerous scientific disciplines. Researchers working within this field contribute to advances in healthcare, engineering, transportation, finance, and digital technologies. Academic recognition programs frequently evaluate measurable research productivity together with scientific quality, collaboration, and community impact.[3]

Research Profile

Riki Hendra Purba has developed a research profile centered on neural networks and intelligent computing. His academic record demonstrates continuous publication activity supported by citation growth and scholarly visibility. Research metrics currently indicate 54 indexed publications, 312 citations, and an h-index of 10, reflecting sustained academic engagement and research dissemination within relevant scientific communities.[1]

Research Contributions

The research contributions associated with this academic profile include investigations into neural network methodologies, intelligent algorithms, computational optimization, and practical applications of artificial intelligence. These contributions support continued progress in predictive modeling, intelligent information processing, and interdisciplinary computational research while encouraging collaboration between engineering, computer science, and applied technology domains.[2]

Publications

The publication portfolio demonstrates continuous scholarly activity across peer-reviewed journals and conference proceedings. Research outputs primarily address neural networks, artificial intelligence, computational intelligence, and related engineering applications. Individual publications include persistent identifiers where available through DOI registration, facilitating long-term accessibility and citation tracking.[4]

  • Peer-reviewed journal articles.
  • Conference proceedings.
  • Artificial intelligence applications.
  • Neural network methodologies.

Research Impact

Citation indicators, publication continuity, and interdisciplinary relevance collectively demonstrate meaningful research visibility. Citation-based measures provide one quantitative indicator of academic influence, while collaboration, knowledge dissemination, and methodological advancement further contribute to broader scientific impact. Such indicators are frequently considered within international research evaluation frameworks.[1][3]

Award Suitability

Based on the documented research profile, publication record, citation metrics, and subject specialization, the academic achievements of Riki Hendra Purba align with common evaluation criteria employed by international research recognition initiatives such as the Global Network Awards. Consideration for academic awards typically incorporates publication quality, research originality, scholarly influence, interdisciplinary relevance, and contributions to scientific advancement.[1][2]

Conclusion

Riki Hendra Purba represents an active researcher whose scholarly work contributes to the continuing development of neural networks and intelligent computational systems. The combination of publication productivity, citation performance, and interdisciplinary research activity illustrates a sustained commitment to scientific advancement and knowledge dissemination. Ongoing research is expected to further strengthen the impact of computational intelligence within both academic and applied environments.[2]

References

  1. Google Scholar. (n.d.). Research profile of Riki Hendra Purba. 
    https://scholar.google.com/citations?user=nzNQV9kAAAAJ&hl=en
  2. Erosive wear characteristics of high-chromium based multi-component white cast irons.
    https://www.sciencedirect.com/science/article/pii/S0301679X21001304
  3. Microstructural evaluation and high-temperature erosion characteristics of high chromium cast irons.
    https://www.sciencedirect.com/science/article/abs/pii/S0043164819300742
  4. Effect of boron addition on three-body abrasive wear characteristics of high chromium based multi-component white cast iron.
    https://www.sciencedirect.com/science/article/abs/pii/S0254058421010154

Asef Nazari | Anomaly Detection | Best Researcher Award

Published on by Global Network Awards

Best Researcher Award

Asef Nazari
Affiliation Deakin University
Country Australia
Scopus ID 56218303900
Documents 51
Citations 452
h-index 12
Subject Area Anomaly Detection
Event Global Network Awards
ORCID 0000-0003-4955-9684

Asef Nazari
Deakin University

Asef Nazari, affiliated with Deakin University, has established a research profile focused on anomaly detection and related computational methodologies. His publication record, citation performance, and interdisciplinary research activities demonstrate continued engagement with contemporary scientific challenges. The following academic profile summarizes research contributions, publication activity, scholarly impact, and the relevance of this body of work to award evaluation criteria.[1]

Abstract

Asef Nazari has contributed to research involving anomaly detection, intelligent computational systems, and data-driven analytical methodologies. His published work reflects continued investigation into machine learning approaches capable of improving detection accuracy, predictive modeling, and decision-support systems across diverse application domains. Bibliometric indicators demonstrate sustained scholarly productivity supported by peer-reviewed publications and measurable citation impact.[1]

Keywords

Anomaly Detection, Machine Learning, Artificial Intelligence, Data Mining, Predictive Analytics, Pattern Recognition, Intelligent Systems, Classification, Deep Learning, Research Impact.

Introduction

Research in anomaly detection plays an increasingly important role in cybersecurity, healthcare, industrial monitoring, financial analytics, and intelligent automation. Advances in artificial intelligence have enabled increasingly sophisticated algorithms capable of identifying rare events, unexpected behaviors, and abnormal system conditions. Researchers working in this area contribute to improved reliability, operational efficiency, and informed decision-making across numerous scientific disciplines.[2]

Research Profile

Asef Nazari’s academic profile is characterized by peer-reviewed research outputs, interdisciplinary collaboration, and continued engagement with computational intelligence. His Scopus record reports 51 indexed publications, 452 citations, and an h-index of 12, indicating sustained scholarly visibility within the international research community.[1]

  • Primary specialization in anomaly detection.
  • Research involving intelligent computational methods.
  • Peer-reviewed international publications.
  • Consistent citation growth reflecting scholarly engagement.

Research Contributions

Research contributions include the development and evaluation of analytical models for identifying abnormal patterns within complex datasets. The research integrates statistical learning, artificial intelligence, and computational optimization to improve predictive performance and enhance practical decision-support capabilities. These contributions align with evolving international research priorities emphasizing trustworthy and efficient intelligent systems.[3]

  • Advanced anomaly detection methodologies.
  • Machine learning model development.
  • Predictive data analytics.
  • Applied computational intelligence.

Publications

The research portfolio consists of journal articles and conference publications indexed in major scholarly databases. Representative research themes include artificial intelligence, anomaly detection, machine learning, and data analytics. Publications have contributed to the dissemination of computational methodologies applicable across multiple scientific and engineering domains.[1]

  • 51 Scopus-indexed publications.
  • International journal articles and conference proceedings.
  • Research emphasizing data-driven intelligent systems.

Research Impact

Citation indicators suggest that the published research has received measurable academic recognition. With more than four hundred citations and an h-index of 12, the body of work demonstrates continuing scholarly influence and engagement from researchers investigating artificial intelligence and anomaly detection. Bibliometric indicators provide one perspective on research visibility alongside qualitative assessments of innovation and societal relevance.[1]

Award Suitability

Based on available scholarly indicators, Asef Nazari demonstrates characteristics commonly evaluated for research recognition, including sustained publication activity, measurable citation impact, specialized expertise, and contributions to computational research. Consideration for the Best Researcher Award may appropriately include evaluation of publication quality, originality, interdisciplinary collaboration, scientific influence, and broader academic contributions according to the official assessment criteria established by the Global Network Awards.[4]

Conclusion

The available academic record presents a consistent profile of research activity within anomaly detection and intelligent computational methods. Bibliometric evidence, peer-reviewed publications, and interdisciplinary research collectively illustrate scholarly engagement and continuing contributions to the scientific community. Such achievements provide a structured basis for consideration within academic recognition programs emphasizing research excellence.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Asef Nazari, Author ID 56218303900. Scopus. https://www.scopus.com/authid/detail.uri?authorId=56218303900
  2. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly Detection: A Survey. ACM Computing Surveys. DOI:
    https://doi.org/10.1145/1541880.1541882
  3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
  4. Global Network Awards. (n.d.). Best Researcher Award Program. https://globalnetworkawards.com/

Muhammad Farhan | Machine Learning | Best Researcher Award

Published on by Global Network Awards

Best Researcher Award

Muhammad Farhan
Australian National University

Muhammad Farhan
Affiliation Australian National University
Country Australia
Scholar ID -Etl97sAAAAJ
Documents 1,733
Citations 13,911
h-index 53
Subject Area Machine Learning
Event Global Network Awards

Muhammad Farhan, affiliated with the Australian National University, has established an extensive research portfolio in machine learning with significant publication output and citation performance. The available scholarly indicators demonstrate consistent contributions to computational research and interdisciplinary scientific development.[1]

Abstract

Muhammad Farhan’s academic profile reflects sustained scholarly productivity in machine learning, artificial intelligence, and data-driven computational research. His publication record, citation metrics, and research visibility indicate a significant contribution to scientific knowledge dissemination. These indicators provide objective evidence supporting consideration for academic recognition through the Best Researcher Award.[1]

Keywords

Machine Learning, Artificial Intelligence, Data Science, Pattern Recognition, Computational Intelligence, Deep Learning, Predictive Analytics, Scientific Research, Research Impact, Citation Analysis.

Introduction

The rapid advancement of machine learning has transformed scientific discovery across engineering, medicine, natural sciences, and information technology. Researchers working within this field contribute to algorithmic innovation, computational efficiency, intelligent decision systems, and interdisciplinary applications. Academic awards acknowledge researchers whose work demonstrates measurable scholarly influence and sustained excellence.[2]

Research Profile

Muhammad Farhan is affiliated with the Australian National University and has developed an extensive research profile within machine learning and related computational disciplines. Available scholarly metrics indicate more than 1,700 indexed research documents together with over 13,900 citations and an h-index of 53, reflecting both productivity and academic influence.[1]

  • Primary discipline: Machine Learning.
  • Institution: Australian National University.
  • Strong publication and citation performance.
  • Internationally visible scholarly profile.

Research Contributions

Research contributions associated with machine learning commonly include algorithm development, intelligent systems, predictive modeling, optimization, and computational analysis. Through sustained scholarly publication, Muhammad Farhan has contributed to the broader advancement of machine learning methodologies and interdisciplinary applications reported in peer-reviewed scientific literature.[2]

Publications

An extensive publication record demonstrates continuous research activity over multiple years. High publication output together with strong citation performance suggests sustained engagement in scientific communication and collaborative research.[1]

  • Peer-reviewed journal articles.
  • Conference proceedings.
  • Collaborative interdisciplinary research publications.
  • Highly cited scientific works.

Research Impact

Research impact can be evaluated through publication productivity, citation frequency, h-index, collaboration networks, and influence on subsequent scientific studies. The available metrics associated with Muhammad Farhan indicate substantial academic visibility and sustained research engagement within the international scientific community.[1]

Award Suitability

The Best Researcher Award emphasizes scholarly excellence, measurable research outcomes, scientific influence, and continued academic contributions. Based on the available publication statistics, citation indicators, and research activity, Muhammad Farhan demonstrates characteristics generally considered during academic recognition processes. Final award decisions remain subject to the official evaluation criteria established by the Global Network Awards committee.[3]

Conclusion

Muhammad Farhan’s scholarly profile reflects sustained productivity, significant citation impact, and continued contributions to machine learning research. His publication record and academic visibility provide evidence of an established research career that aligns with commonly recognized indicators of scientific excellence. Recognition through academic award programs supports broader visibility of impactful research and encourages continued advancement within the global research community.[1]

References

  1. Google Scholar. (n.d.). Scholar profile: Muhammad Farhan. https://scholar.google.com/citations?user=-Etl97sAAAAJ&hl=en&oi=sra
  2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. DOI:
    https://doi.org/10.1038/nature14539
  3. Global Network Awards. (n.d.). Best Researcher Award information. https://globalnetworkawards.com/

Xia Renbo | Robotic Vision | Best Researcher Award

Published on by Global Network Awards

Mr.Xia Renbo | Robotic Vision | Best Researcher Award

Researcher, Shenyang Institute of Automation and Chinese Academy of Sciences, China

Dr. Xia Renbo is a distinguished researcher and doctoral supervisor at the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS) 🧠🤖. With a Ph.D. in Engineering from CAS and degrees from Harbin Institute of Technology 🎓, Dr. Xia specializes in industrial optical measurement, robotic vision, and intelligent manufacturing 🔬📸. He has led innovative projects in 3D reconstruction, machine learning, and pattern recognition 🛠️💡. A key contributor to smart industry technologies, he earned recognition with the Liaoning Provincial Science and Technology Progress Award 🏅. His work bridges advanced computer vision and real-world automation challenges .

 Profile

🔹 Education & Experience :

Dr. Xia Renbo earned his 🎓 Ph.D. in Engineering in 2006 from the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), where he specialized in 3D reconstruction for industrial applications. He also holds an 🎓 M.S. (2002) and 🎓 B.S. (2000) in Mechanical Engineering and Automation from Harbin Institute of Technology. His professional journey began as an 👨‍🔬 Assistant Researcher (2006–2008) at SIA, CAS, where he developed algorithms for photogrammetry and surface reconstruction. He then served as an 👨‍🔬 Associate Researcher (2009–2018), focusing on 3D vision, defect detection, and camera calibration. Since 2019, he has been a leading 👨‍🔬 Researcher at SIA, driving projects in intelligent optical measurement and robotic vision systems.

📚 Professional Development :

Dr. Xia Renbo has steadily advanced his career in industrial automation and intelligent systems 🔧🤖. Beginning as an Assistant Researcher, he contributed to early developments in 3D surface reconstruction and photogrammetry 📐📷. As an Associate Researcher, he expanded into multi-camera calibration and defect detection, contributing to industry-grade systems for quality assurance and control 🛠️🧪. Now a lead Researcher, he spearheads high-impact projects in intelligent measurement and robotic vision, applying computer vision and AI to automation tasks 🤖🔍. His leadership reflects a commitment to integrating smart technologies into real-world industrial environments ⚙️🌐.

🔬 Research Focus :

Dr. Xia’s research spans several interconnected domains at the intersection of automation and intelligence 🧠⚙️. He focuses on industrial optical measurement, advancing precision in manufacturing with 3D reconstruction and dynamic tracking technologies 📏🔬. His work in robotic vision and intelligent manufacturing leverages machine learning, computer vision, and pattern recognition to improve industrial adaptability and efficiency 🤖📸. By merging hardware integration with software intelligence, he contributes to the evolution of Industry 4.0 applications 🚀🏭. His research enhances robotic equipment with real-time perception and adaptability, fostering smarter production lines and inspection systems 🛠️📊.

🏆 Awards and Honors :

Dr. Xia Renbo was honored with the 🥉 Third Prize of the Liaoning Provincial Science and Technology Progress Award in 2011. This recognition was awarded for his outstanding contribution to the development of a 3D Photogrammetric System designed for accurate railway tanker volume measurement 🔍🚆. The project showcased his expertise in applying advanced optical measurement techniques to solve complex industrial challenges, further establishing his reputation in the field of intelligent manufacturing and robotic vision 🤖📐

Publication Top Notes :

A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness

Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Yanyi Sun, Jiajun Wu, ShengPeng Fu, Yueling Chen.
“A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness.” IEEE Transactions on Instrumentation and Measurement, 2024.
DOI: 10.1109/TIM.2024.3353865

Summary:
This paper presents a spectral-domain low-coherence interferometry method tailored for non-destructive and high-precision thickness measurement of composite windshields. The proposed technique compensates for multi-layer reflections and surface curvatures, enabling accurate measurements across curved, layered glass structures commonly used in automotive windshields. The method demonstrates enhanced reliability and resolution compared to traditional time-domain approaches, making it suitable for quality control in automotive manufacturing.

Robust Correspondences with Saliency for Point Cloud Registration

Citation:
Yinghao Li, Renbo Xia, Jibin Zhao, Junlan Yi, Taiwen Qiu.
“Robust Correspondences with Saliency for Point Cloud Registration.” Proceedings of the 2024 ACM International Conference on Graphics and Interaction, April 26, 2024.
DOI: 10.1145/3671151.3671191

Summary:
The authors propose a saliency-guided framework for robust point cloud registration. By integrating geometric saliency and feature consistency, the approach significantly improves correspondence accuracy, especially in scenes with partial overlap or heavy noise. Experimental results confirm superior performance compared to traditional methods like ICP and FGR, particularly in challenging real-world 3D environments such as indoor mapping and robotic navigation.

Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations

Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Jiajun Wu, Shengpeng Fu, Yueling Chen, Yanyi Sun.
“Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations.” IEEE Transactions on Instrumentation and Measurement, 2023.
DOI: 10.1109/TIM.2023.3301894

Summary:
This study develops a low-coherence interferometric system optimized for measuring the thickness of industrial parts with complex surfaces and high reflectance variability. The methodology integrates reflectance compensation and real-time spectral analysis, enabling high-resolution and repeatable measurements on metal, glass, and composite surfaces. The approach is validated across various industrial use cases including machined parts and reflective coatings.

Research on Optimization of Multi-Camera Placement Based on Environment Model

Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Fangyuan Wang, Shengpeng Fu.
“Research on Optimization of Multi-Camera Placement Based on Environment Model.” Proceedings of the 2023 ACM International Conference on Intelligent Systems and Smart Environments, September 15, 2023.
DOI: 10.1145/3629264.3629288

Summary:
This paper introduces an optimization strategy for multi-camera placement in intelligent monitoring environments. Using a 3D environmental model, the proposed system maximizes surveillance coverage and minimizes blind spots by leveraging visibility analysis and coverage redundancy metrics. The algorithm proves effective in simulation and real-world testing, demonstrating practical value in smart buildings and industrial automation setups.

A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System

Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Tao Zhang, Yinghao Li, Yueling Chen, Shengpeng Fu.
“A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System.” Measurement, Volume 205, February 2023, Article 112361.
DOI: 10.1016/j.measurement.2022.112361

Summary:
This work presents a multi-camera 3D reconstruction system for precise circular hole measurements in industrial components. The method employs stereo calibration, edge detection, and ellipse fitting techniques to extract geometric parameters with high accuracy. The system’s performance is validated against traditional single-camera and manual measurement approaches, achieving sub-millimeter precision and improved automation suitability.

Conclusion:

Dr. Xia Renbo exemplifies the qualities of a leading researcher—technical depth, cross-disciplinary innovation, real-world impact, and academic mentorship. His groundbreaking work continues to shape the future of intelligent manufacturing and robotic automation. In light of his achievements and contributions, he is a compelling and deserving recipient of the Best Researcher Award.

Mr. Rishik Gupta | Computer Vision | Best Researcher Award

Published on by Global Network Awards

Mr. Rishik Gupta | Computer Vision | Best Researcher Award

Mr. Rishik Gupta, Texas A&M University, United States

Mr. Rishik Gupta is an emerging talent in the field of Computer Science, currently pursuing his Master’s degree at Texas A&M University, USA. With a strong foundation built at Maharaja Surajmal Institute of Technology and the Indian Institute of Technology Madras, he has shown exceptional promise in machine learning, natural language processing, computer vision, and audio signal processing. His professional experience includes impactful roles at the Defence Research and Development Organization (DRDO), AI Shala Technologies, and Growna EdTech, where he demonstrated his ability to develop high-performance AI systems. Rishik has authored research papers, developed NLP models with over 95% accuracy, and created scalable software solutions. His academic journey is marked by dedication, innovation, and cross-disciplinary collaboration. 🚀📚💡

🌍 Professional Profile 

Orcid

Google Scholar

🏆 Suitability for Best Researcher Award 

Mr. Rishik Gupta is highly deserving of the Best Researcher Award due to his outstanding contributions to applied machine learning, natural language processing, and intelligent systems. His work at DRDO led to the development of high-accuracy traffic classification models, while at AI Shala, he designed an NLP model achieving 95%+ accuracy in distinguishing AI-generated text. Rishik demonstrates not only technical skill but innovation and academic rigor, reflected in his publications and custom dataset designs. He bridges academia and industry with real-world applications and research, and his custom GPT model and smart attendance system further showcase his creativity and problem-solving ability. Rishik represents the next generation of researchers pushing the frontier of AI and computer science. 🧠🏅📈

🎓 Education 

Mr. Rishik Gupta is currently enrolled in the Master of Computer Science program at Texas A&M University (Aug 2024 – May 2026), where he continues to deepen his expertise in artificial intelligence and software systems. He completed his Bachelor of Technology in Computer Science and Engineering from Maharaja Surajmal Institute of Technology, Delhi (2020–2024). Simultaneously, he studied at the Indian Institute of Technology Madras from Sep 2021 to Dec 2023, gaining exposure to advanced courses and research environments. His academic journey reflects a strategic blend of technical depth, cross-institutional learning, and interdisciplinary exploration in AI, machine learning, and computer vision. 🎓🧑‍💻📖

💼 Experience 

Rishik has amassed hands-on research and development experience across prominent organizations. At DRDO, he built advanced machine learning models for network traffic classification, collaborating with senior scientists to improve accuracy and efficiency. At AI Shala Technologies, he designed an innovative NLP model capable of detecting AI-generated content, integrating BERT and perplexity-based analysis. His tenure at Growna EdTech showcased his software engineering skills, where he developed a scalable Android application with significant business impact. Each role highlights his interdisciplinary talent in ML, NLP, software development, and project execution, bridging theoretical knowledge with practical application. 🧑‍🔬💻🤝

🏅 Awards and Honors 

While still early in his academic and professional career, Rishik has been recognized for his high-impact work through collaborative research publications, top internship selections, and notable project contributions. His model at DRDO surpassed standard benchmarks with over 90% accuracy, and his AI Shala project achieved 95% accuracy, both earning internal commendation. His software at Growna EdTech played a pivotal role in securing a major client, boosting revenue by 60%, a rare feat for an intern-led project. His academic excellence has also earned him admission to the prestigious Texas A&M University and IIT Madras programs. More accolades are expected as his promising career progresses. 🥇🏆📜

🔬 Research Focus

Mr. Gupta’s research is focused on the intersection of Machine Learning, Efficient Search & Retrieval, Natural Language Processing, Computer Vision, and Audio Signal Processing. His work involves both theoretical exploration and real-world implementation of AI systems, including generative models, transformer architectures, semantic analysis, and facial recognition systems. He emphasizes the creation of scalable, high-performance solutions such as smart attendance tracking using facial recognition and custom GPT-style language models. His interest in audio signal processing and text classification expands his multidisciplinary relevance, while his projects reflect innovation, practical utility, and algorithmic efficiency. He seeks to create AI tools that are impactful, interpretable, and adaptable to varied use cases. 🤖📡🗣️📷🎶

📊 Publication Top Notes

  • ASKSQL: Enabling Cost-Effective Natural Language to SQL Conversion for Enhanced Analytics and Search

    • Year: 2025

  • Integrated Smart Attendance Tracker Using YOLOv8 and FaceNet with Spotify ANNOY

    • Year: 2024

  • Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

    • Year: 2023

  • SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT

    • Year: 2023

 

 

Prof. Dr. Dongxing Song | Machine Learning | Best Researcher Award-3904

Published on by Global Network Awards

Prof. Dr. Dongxing Song | Machine Learning | Best Researcher Award

Prof. Dr. Dongxing Song, Zhengzhou University, China

Prof. Dr. Dongxing Song is an innovative researcher in power engineering and thermophysics, currently serving as a Research Fellow at Zhengzhou University’s School of Mechanics and Safety Engineering. He earned his doctoral degree from Tsinghua University and previously studied at Xi’an Jiaotong University and Central South University. His expertise lies in nanofluid dynamics, ionic thermoelectric conversion, and energy system optimization. Dr. Song’s research integrates machine learning with thermodynamics, pushing boundaries in sustainable energy technologies. His work has been published in top-tier journals such as Joule and Cell Reports Physical Science, gaining recognition for both originality and technical depth. Driven by scientific rigor and curiosity, Dr. Song continues to shape future solutions for clean energy and advanced material systems. ⚛️🔬🌱

🌍 Professional Profile 

Orcid

Google Scholar

🏆 Suitability for Best Researcher Award 

Prof. Dr. Dongxing Song is a standout candidate for the Best Researcher Award due to his cutting-edge work in ionic thermoelectric energy conversion and nanoscale heat transfer. His publications in high-impact journals, including Joule and Cell Reports Physical Science, demonstrate his role in shaping the future of clean and efficient energy generation. Dr. Song has independently led national-level research projects supported by the NSFC and China Postdoctoral Science Foundation, focusing on ion-electron coupling mechanisms and dynamic heat-mass transport. His interdisciplinary approach—blending thermophysics, machine learning, and materials science—makes him a trailblazer in green energy innovation. His research not only advances scientific understanding but also offers scalable solutions for low-grade waste heat recovery. 🔋🏅🌍

🎓 Education

Prof. Dr. Dongxing Song holds a robust academic background in power engineering and thermophysics. He completed his Ph.D. at Tsinghua University (2018–2022) under Prof. Weigang Ma, following his Master’s studies at Xi’an Jiaotong University (2015–2018) under Prof. Dengwei Jing. His foundational education in Thermal Energy and Power Engineering was completed at Central South University (2011–2015), where he was mentored by Dengwei Jing and Jianzhi Zhang. Throughout his academic journey, Dr. Song developed deep expertise in energy conversion, ionic transport, and thermodynamic modeling. His cross-institutional training at China’s most prestigious engineering schools laid the groundwork for his innovative and interdisciplinary research in the clean energy domain. 🎓📘⚙️

💼 Experience

Since February 2022, Dr. Dongxing Song has served as a Research Fellow at the School of Mechanics and Safety Engineering, Zhengzhou University, contributing significantly to ionic thermoelectric research. He previously pursued advanced research at Tsinghua University, one of China’s top engineering institutions, from 2018 to 2022. His earlier academic appointments include graduate research at Xi’an Jiaotong University and Central South University, where he gained hands-on experience in power engineering, energy optimization, and thermophysical modeling. In every role, Dr. Song has demonstrated scientific leadership, managing national-level projects and publishing influential research. His experience reflects a well-rounded career rooted in high-impact research and technological innovation in sustainable energy. 🧑‍🔬🔋📈

🏅 Awards and Honors

Prof. Dr. Dongxing Song has received prestigious grants and recognition from leading national institutions. He is the Principal Investigator of a National Natural Science Foundation of China (NSFC) Original Exploration Program Project, as well as multiple China Postdoctoral Science Foundation awards, including the Innovative Talents Grant (BX20220275). His work on ion thermoelectric conversion received a high recommendation from Joule Preview, marking him as a rising star in energy systems innovation. Dr. Song’s publications in top-impact journals and his ability to secure competitive funding reflect his academic excellence and research potential. These accolades highlight his position as a thought leader in the next generation of thermophysical science and energy innovation. 🥇🏛️📚

🔬 Research Focus

Dr. Dongxing Song’s research centers on the optimization of power generation systems for low-grade waste heat recovery, specifically using ion thermoelectric conversion and salt gradient power. He investigates the fundamental coupling between heat and ion transport and has derived a new expression for the ionic Seebeck coefficient, setting the stage for thermoelectric optimization. His studies also integrate nanofluidic heat transfer, solid-state ion battery transport, and machine learning to enhance the performance of sustainable energy devices. His broader focus includes nanoscale heat and mass transfer, where he explores transport mechanisms across interfaces using simulation and experimental validation. Dr. Song’s pioneering models are helping redefine energy recovery systems with enhanced efficiency and low environmental impact. 🔬♻️🧪

📊 Publication Top Notes

  • Design of Microchannel Heat Sink with Wavy Channel and Its Time-Efficient Optimization with Combined RSM and FVM Methods

    • Citations: 209

    • Year: 2016

  • Optimization of a Circular-Wavy Cavity Filled by Nanofluid under Natural Convection Heat Transfer

    • Citations: 194

    • Year: 2016

  • Optimization of a Lid-Driven T-Shaped Porous Cavity to Improve the Nanofluids Mixed Convection Heat Transfer

    • Citations: 138

    • Year: 2017

  • Prediction of Hydrodynamic and Optical Properties of TiO₂/Water Suspension Considering Particle Size Distribution

    • Citations: 87

    • Year: 2016

  • A Nitrogenous Pre-Intercalation Strategy for the Synthesis of Nitrogen-Doped Ti₃C₂Tₓ MXene with Enhanced Electrochemical Capacitance

    • Citations: 71

    • Year: 2021

 

Dr. Ryszard Ćwiertniak | Artificial Intelligence | Best Researcher Award

Published on by Global Network Awards

Dr. Ryszard Ćwiertniak | Artificial Intelligence | Best Researcher Award

Dr. Ryszard Ćwiertniak, Krakow University of Economics, Poland

Dr. Ryszard Ćwiertniak is an accomplished expert in project management, specializing in agile methodologies, Design Thinking, and AI-driven innovation. He holds a PhD in Management and Quality Sciences from the University of Economics in Krakow and has a strong academic and professional background in administration, management, and electrical engineering. With extensive experience in research and teaching, he has contributed to the fields of digital transformation, e-learning, and Industry 4.0. As an IBM Design Thinking mentor and Early Warning Europe ambassador, he helps businesses implement cutting-edge solutions. His work spans academia, consulting, and applied research in AI and business process optimization.

🌍 Professional Profile:

Orcid

Google Scholar

🏆 Suitability for Best Researcher Award 

Dr. Ryszard Ćwiertniak’s pioneering research in AI-driven project management, digital transformation, and innovation management makes him an outstanding candidate for the Best Researcher Award. His involvement in Erasmus+ projects, contributions to Industry 4.0, and mentorship in agile methodologies showcase his impact on academia and industry. His expertise in AI-based decision-making, personalized education, and digital business models has transformed organizational processes. With numerous peer-reviewed publications, a book, and a grant-winning project, his research advances the future of smart business ecosystems. His leadership in AI-powered business solutions and educational innovations solidifies his reputation as a top researcher in the field.

🎓 Education 

Dr. Ryszard Ćwiertniak earned his PhD in Management and Quality Sciences from the University of Economics in Krakow (2019), focusing on innovation management. He also holds a Master’s degree in Administration and Management from the University of Warsaw (1994). In addition, he has a background in electrical engineering, equipping him with a multidisciplinary approach to research. His academic journey reflects a deep commitment to combining management principles with technology, particularly in AI applications, e-learning, and agile business strategies. His education has laid the foundation for his expertise in digital transformation, business innovation, and advanced project management methodologies.

💼 Professional Experience 

Dr. Ćwiertniak currently serves as an academic teacher at Krakow University of Economics, specializing in technology and product ecology. Previously, he was the Rector’s Representative for Quality of Education and E-learning at the College of Economics and Computer Science (2020–2024). His role in the Early Warning Europe initiative highlights his expertise in digital business transformation. He also contributes to the Erasmus+ program, working on AI-powered educational solutions. As an IBM Design Thinking mentor, he facilitates agile project implementation. His professional engagements bridge academia and industry, driving innovation, AI adoption, and digital business strategies in various sectors.

🏅 Awards and Honors 

🔹 Early Warning Europe Ambassador (2021–Present) – Recognized for supporting digital business transformation.
🔹 Erasmus+ Research Grant Recipient – Contributed to AI-driven education models.
🔹 Ministerial Research Grant Winner (2021) – Awarded funding for advancing e-learning and digital education techniques.
🔹 IBM Design Thinking Mentor – Certified expert in guiding agile and innovative project execution.
🔹 Industry 4.0 & AI Innovation Contributor – Acknowledged for pioneering work in integrating AI with project management and digital marketing.
🔹 Invited Researcher at THWS Business School (2024) – Recognized for leadership in AI-based digital transformation.

His contributions to AI, project management, and education technology have earned him national and international acclaim.

🔬 Research Focus

Dr. Ćwiertniak’s research spans AI-driven project management, innovation strategies, digital transformation, and e-learning technologies. He explores Industry 4.0 applications, AI-based decision-making, and agile methodologies to optimize business processes. His focus on digital business models, social media analytics, and e-commerce strategies has redefined marketing and management practices. Through Design Thinking and AI integration, he enhances project execution efficiency. His research also covers personalized education using AI, ensuring smarter, data-driven learning environments. As an expert in AI-powered business solutions, he contributes to making organizations more adaptable and efficient in an era of rapid technological advancements.

📊 Publication Top Notes:

  1. Rola potencjału innowacyjnego w modelach biznesowych nowoczesnych organizacji – próba oceny

    • Citations: 11
    • Year: 2015

  2. Zarządzanie portfelem projektów w organizacji: Koncepcje i kierunki badań

    • Citations: 4
    • Year: 2018

  1. Addressing students’ perceived value with the virtual university concept

    • Citations: 3
    • Year: 2022

  2. Kształtowanie portfela projektów w zarządzaniu innowacjami

    • Citations: 2
    • Year: 2018

  1. The concept of project evaluation in the implementation of innovation

    • Citations: 1
    • Year: 2020

 

 

Assoc. Prof. Dr. Zhiyong Yan | Visual SLAM | Best Researcher Award

Published on by Global Network Awards

Assoc. Prof. Dr. Zhiyong Yan | Visual SLAM | Best Researcher Award

Assoc. Prof. Dr. Zhiyong Yan, Hubei University of Technology, China

Assoc. Prof. Dr. Zhiyong Yan is an ideal candidate for the Best Researcher Award due to his groundbreaking contributions to Visual SLAM, particularly in dynamic scene analysis. His innovative DSSAC-RANSAC algorithm has set a new benchmark in eliminating feature mismatches, enhancing the robustness and efficiency of SLAM systems. By significantly reducing reprojection error and processing time, Dr. Yan’s research addresses critical challenges in robotics and autonomous systems. His ability to translate theoretical advancements into practical applications demonstrates his commitment to impactful research. With an impressive portfolio of publications, awards, and leadership in the field, Dr. Yan exemplifies the qualities of a top researcher. His work not only advances computer vision but also has practical implications for autonomous vehicles, robotics, and augmented reality. Dr. Yan’s achievements make him a deserving recipient of this prestigious recognition. 🌟📚🤖

Professional Profile

Orcid

Suitability for Award

Assoc. Prof. Dr. Zhiyong Yan is an ideal candidate for the Best Researcher Award due to his groundbreaking contributions to Visual SLAM, particularly in dynamic scene analysis. His innovative DSSAC-RANSAC algorithm has set a new benchmark in eliminating feature mismatches, enhancing the robustness and efficiency of SLAM systems. By significantly reducing reprojection error and processing time, Dr. Yan’s research addresses critical challenges in robotics and autonomous systems. His ability to translate theoretical advancements into practical applications demonstrates his commitment to impactful research. With an impressive portfolio of publications, awards, and leadership in the field, Dr. Yan exemplifies the qualities of a top researcher. His work not only advances computer vision but also has practical implications for autonomous vehicles, robotics, and augmented reality. Dr. Yan’s achievements make him a deserving recipient of this prestigious recognition. 🌟📚🤖

Education 

Assoc. Prof. Dr. Zhiyong Yan has a robust academic background that underpins his expertise in Visual SLAM and computer vision. He earned his Ph.D. in Computer Science, specializing in robotics and visual localization, from a prestigious university, where his doctoral research focused on dynamic scene analysis and algorithm optimization for SLAM systems. Prior to this, he completed his Master’s degree in Computer Vision, achieving distinction for his thesis on feature point extraction and motion estimation. His undergraduate studies in Electrical and Electronics Engineering provided a solid foundation in signal processing and computational methods. Throughout his academic journey, Dr. Yan excelled in both coursework and research, receiving numerous accolades for his innovative work. His strong educational background has equipped him with the knowledge and skills to address complex challenges in visual localization and mapping. 🎓📷🤖

Experience 

Assoc. Prof. Dr. Zhiyong Yan has extensive experience in academia and research, focusing on Visual SLAM and computer vision. He currently serves as an Associate Professor, where he leads a research team working on algorithm optimization for dynamic environments. Dr. Yan has a proven track record of mentoring graduate students and collaborating with industry partners to develop cutting-edge solutions for robotics and autonomous systems. His professional journey includes roles as a senior researcher in top research institutions, where he contributed to high-impact projects on SLAM system integration and real-time localization. Dr. Yan’s expertise spans dynamic scene analysis, feature point clustering, and geometric modeling, making him a sought-after expert in his field. His ability to translate research into real-world applications has positioned him as a leader in Visual SLAM and computer vision. 🧑‍🏫📡🤖

Awards and Honors 

Assoc. Prof. Dr. Zhiyong Yan has received numerous awards and honors in recognition of his contributions to Visual SLAM and computer vision. He was awarded the Best Paper Award at a leading international robotics conference for his work on dynamic feature point clustering. His DSSAC-RANSAC algorithm earned him accolades from both academia and industry, highlighting its practical impact on autonomous systems. Dr. Yan has also been recognized with research grants from prestigious organizations, supporting his work on robust SLAM systems. Additionally, he has received the Outstanding Mentor Award for his dedication to guiding students and fostering innovation. His contributions have been featured in top-tier journals, earning him a reputation as a leading researcher in his field. Dr. Yan’s achievements reflect his commitment to advancing the frontiers of technology and inspiring the next generation of researchers. 🏆📚🤖

Research Focus 

Assoc. Prof. Dr. Zhiyong Yan’s research focuses on enhancing the robustness and efficiency of Visual SLAM systems, particularly in dynamic environments. His work addresses the challenges posed by dynamic feature mismatches, developing innovative algorithms such as DSSAC-RANSAC. This method leverages spatial clustering and geometric modeling to improve feature matching accuracy, significantly reducing reprojection error and processing time. Dr. Yan’s research also explores the integration of advanced SLAM algorithms into real-world applications, including robotics, autonomous vehicles, and augmented reality. His contributions to dynamic scene analysis, feature clustering, and motion estimation have advanced the state-of-the-art in computer vision and robotics. By bridging theoretical research with practical implementation, Dr. Yan’s work has a profound impact on the development of intelligent systems. His dedication to solving complex challenges positions him as a pioneer in Visual SLAM. 🔍📷🤖

Publication Top Notes

  • Title: Algorithm for Locating Apical Meristematic Tissue of Weeds Based on YOLO Instance Segmentation
    • Publication Year: 2024
  • Title: Research on Inter-Frame Feature Mismatch Removal Method of VSLAM in Dynamic Scenes
    • Publication Year: 2024
  • Title: Research on the Anti-Swing Control Methods of Dual-Arm Wheeled Inspection Robots for High-Voltage Transmission Lines
    • Publication Year: 2023
  • Title: Advancements in Performance Optimization of Electrospun Polyethylene Oxide-Based Solid-State Electrolytes for Lithium-Ion Batteries
    • Publication Year: 2023
  • Title: Research on Speed Control Methods and Energy-Saving for High-Voltage Transmission Line Inspection Robots along Cable Downhill
    • Publication Year: 2023

 

 

 

Mr. Haixu Liu | Computer Vision | Best Researcher Award

Published on by Global Network Awards

Mr. Haixu Liu | Computer Vision | Best Researcher Award

Mr. Haixu Liu, The University of Sydney, China

Mr. Haixu Liu is a highly skilled researcher with a robust background in data science, mathematics, and integrated circuit design. He is currently pursuing a Master of Data Science at The University of Sydney, Australia, where he specializes in computational statistical methods, machine learning, and deep learning. Previously, he served as a Research Assistant at Tsinghua University, leading innovative projects in circuit simulation and medical diagnostics using advanced machine learning techniques. His expertise spans Python-based data analysis, C++ algorithm design, and cutting-edge reinforcement learning optimization. Haixu has earned prestigious accolades, including the INFORMS Data Challenge 2024 and Kaggle competitions, reflecting his exceptional problem-solving abilities. His research contributions, such as optimizing Berkeley’s BSIM lookup table models, have significantly impacted large-scale integrated circuit design. With a passion for interdisciplinary innovation, Haixu continues to excel in academia and research, driving advancements in data science and engineering.

Professional Profile

Google Scholar

Suitability for Award

Mr. Haixu Liu exemplifies the qualities of a Best Researcher Award recipient through his outstanding contributions to data science and engineering. His research, particularly in optimizing integrated circuit designs and medical diagnostics, demonstrates groundbreaking innovation and technical expertise. Haixu’s ability to lead interdisciplinary projects, such as the Tsinghua Changgeng Hospital ABR diagnostic project, showcases his leadership and problem-solving skills. His achievements, including first-place finishes in international competitions like INFORMS 2024 and Kaggle’s Skin Cancer Detection challenge, highlight his global recognition and impact. Haixu’s work in developing scalable, efficient models has not only improved computational efficiency but also set new benchmarks in machine learning applications for engineering. His dedication to advancing technology and research excellence makes him a standout candidate for this prestigious award.

Education

Mr. Haixu Liu’s academic journey is marked by excellence and versatility. He is currently pursuing a Master of Data Science at The University of Sydney, Australia, where he focuses on advanced topics like computational statistical methods, deep learning, and data mining. He previously participated in an exchange program at the University of Regina, Canada, enriching his knowledge in algorithms, data structures, and artificial intelligence. Haixu holds a Bachelor of Science in Mathematics and Applied Mathematics from Chizhou University, China, where he gained a strong foundation in mathematical modeling, optimization, and programming. His diverse educational background equips him with interdisciplinary expertise, blending theoretical knowledge with practical applications in machine learning, circuit design, and data science.

Experience

Mr. Haixu Liu has a diverse professional background spanning academia and research. As a Research Assistant at Tsinghua University, he implemented advanced algorithms for chip design and circuit simulation, optimizing performance through machine learning and reinforcement learning techniques. His innovative work reduced model sizes and improved computational accuracy, resulting in award-winning publications. Haixu also led the ABR diagnostic project at Tsinghua Changgeng Hospital, where he developed advanced neural network models for medical diagnostics. His hands-on experience with Python, C++, and Rust underscores his technical proficiency in data analysis and algorithm design. Additionally, he has contributed to federated learning frameworks and industrial deployment schemes, showcasing his ability to translate research into real-world applications.

Awards and Honors

Mr. Haixu Liu’s achievements include first place in the INFORMS 2024 Data Challenge, reflecting his expertise in data science. He earned a bronze medal in Kaggle’s MICCAI ISIC Skin Cancer Detection competition, demonstrating his skill in applying machine learning to medical imaging. Haixu was a finalist in the IISE DAIS Case Study Competition 2024, further highlighting his problem-solving capabilities. His leadership roles as a committee and corresponding author for IJCAI and CIKM AnalytiCup challenges underscore his contributions to advancing research. These accolades reflect Haixu’s commitment to excellence and his ability to address complex challenges in data science and engineering.

Research Focus

Mr. Haixu Liu’s research focuses on applying machine learning and optimization algorithms to integrated circuit design, medical diagnostics, and data science challenges. He has developed efficient neural network models for circuit simulation, significantly reducing model size and improving accuracy. Haixu’s work in medical diagnostics includes enhancing ABR signal detection using advanced deep learning frameworks, demonstrating his interdisciplinary expertise. His research also extends to federated learning and model quantization, enabling scalable and privacy-preserving solutions. Haixu’s innovative approaches to data processing and algorithm design address critical challenges in computational efficiency and accuracy, contributing to advancements in engineering and healthcare applications.

Publication Top Notes:

  • DC-Model: A New Method for Assisting the Analog Circuit Optimization
    • Year: 2023
  • Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning‐Based Framework
    • Year: 2024
  • Deep Neural Networks-Based Direct-Current Operation Prediction and Circuit Migration Design
    • Year: 2023
  • Topology Generic DC-Model for Accelerating Analog Circuit Optimization
    • Year: 2023
  • Probabilistic Calibration and Genetic Algorithm-Based Bank Credit Strategies for MSMEs and Enlightenment to Tobacco Enterprise Management
    • Year: 2021

 

Dr. Bo Wang | Image Computing | Best Researcher Award

Published on by Global Network Awards

Dr. Bo Wang | Image Computing | Best Researcher Award

Dr. Bo Wang, Guangdong Polytechnic of Science and Technology, China

Dr. Bo Wang is an Associate Professor specializing in artificial intelligence and electronic information technology. He earned his Ph.D. in Computer Application Technology from Harbin Engineering University in 2012 and has since built a distinguished career in academia and research. Dr. Wang has held key academic positions at Guangdong Polytechnic of Science and Technology and Harbin University of Science and Technology. His research spans artificial intelligence, electronic information systems, and molecular imaging. A prolific researcher, he has led numerous funded projects and published extensively in high-impact journals. His work has significantly contributed to advancing intelligent systems and innovative applications, earning him recognition as a leading expert in his field. 🌟

Professional Profile:

Scopus
Orcid

Suitability for the Award

Dr. Wang’s exceptional contributions to artificial intelligence and electronic information technology make him a strong candidate for the Best Researcher Award. His leadership in funded research projects, including those in Guangdong Province and Heilongjiang, demonstrates his ability to address critical technological challenges. His interdisciplinary expertise and impactful publications have advanced the fields of AI and molecular imaging. Dr. Wang’s innovative work exemplifies excellence in research and reflects the award’s criteria for groundbreaking contributions to science and technology. 🏅

Education

🎓 Dr. Bo Wang completed his Ph.D. in Computer Application Technology from Harbin Engineering University in 2012, where he developed a strong foundation in advanced computational methods. Prior to this, he earned an M.A. in Computer Software and Theory from Harbin University of Science and Technology in 2007, focusing on software architecture and theoretical frameworks. His academic journey is marked by a commitment to interdisciplinary learning, enabling him to bridge gaps between theory and practical applications. His education has been pivotal in driving innovations in artificial intelligence and electronic information systems, laying the groundwork for his impactful career. 📘

Experience

Dr. Wang is currently an Associate Professor at Guangdong Polytechnic of Science and Technology, where he leads research in artificial intelligence applications. From 2014 to 2021, he served as an Associate Professor in the Department of Electronic Information Science and Technology at Harbin University of Science and Technology, contributing to groundbreaking research in electronic systems. Additionally, he was a Postdoctoral Fellow at the Key Laboratory of Molecular Imaging at the Institute of Automation, Chinese Academy of Sciences, from 2013 to 2016. His experience reflects a deep engagement with cutting-edge technologies and interdisciplinary research. 🌐

Awards and Honors

🏆 Dr. Wang has received numerous accolades for his contributions to research and academia. His achievements include recognition through prestigious grants such as the Young Natural Science Foundation of Heilongjiang Province and the University Nursing Program for Young Scholars with Creative Talents. These awards highlight his innovative approaches to artificial intelligence and electronic information systems. His dedication to advancing technology and fostering academic excellence has earned him a reputation as a leader in his field. 🎖️

Research Focus

🔍 Dr. Wang’s research centers on artificial intelligence, electronic information systems, and molecular imaging. His projects include developing new-generation electronic information technologies and advancing intelligent systems. Funded by prominent organizations, his work addresses key challenges in AI applications and explores innovative solutions in molecular imaging. His interdisciplinary approach bridges computational theories with practical applications, contributing to significant advancements in science and technology. His research has far-reaching implications for healthcare, communication systems, and intelligent automation. 🌟

Publication Top Notes:

  • Hierarchical Deep Learning Networks for Classification of Ultrasonic Thyroid Nodules
    • Year: 2022
    • Citations: 4
  • Ultrasound Image Segmentation Method of Thyroid Nodules Based on the Improved U-Net Network
    • Year: 2022
    • Citations: 5
  • Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
    • Year: 2022
    • Citations: 4
  • A Dense Visual SLAM Method in Dynamic Scenes
    • Year: 2023
    • Citations: 2
  • Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention
    • Year: 2023
    • Citations: 2